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United States Patent Application
20030069908
Kind Code
A1
Anthony, Jon S ; et al.
April 10, 2003
Software composition using graph types,graph, and agents
Abstract
An environment for composing software permits the separation of control functions from information about the context in which the control functions operate. The software composition environment is used to make a system which will translate XML documents into models and vice-versa. The translation system is used to translate an XML document having one DTD into an XML document having another DTD by translating the first XML document into a model representing the semantics of the XML document and translating the model into the second XML document (2005). The system for translating XML documents into models employs a general technique for translating any XML documents into a mirror model (2107) that reflects the structure of the XML document and a general technique of using tag pattern models (2109) to obtain information from one model and using it to make or modify another model. In the system for translating XML document, the tag pattern models are used to translate mirror models into semantic models and vice-versa.
Inventors:
Anthony; Jon S
(Arlington, MA)
, Allemang; Dean T
(Boston, MA
)
Correspondence Name and Address:
PATENT ATTORNEY, PC 57 CENTRAL ST PO BOX 782
GORDON E NELSON
ROWLEY
MA
01969
US
Series Code:
182295
Claims
What is claimed is:
1. Apparatus for controlling a processor that has access to a storage device, the apparatus comprising: a model type in the storage device that defines a class of models, a model being a graph that has one or more entities as vertices; an agent in the storage device that is associated with the model type and specifies an operation to be performed in a context provided by a model of the class defined by the model type; and a particular model of the model type in the storage device to which the agent has been made available, the processor responding to the agent by performing the operation specified thereby in the context provided by the particular model.
2. The apparatus for controlling a processor set forth in claim 1 further comprising: a set of one or more definitions of events in the storage device, the agent being further associated with a definition of an event and the processor responding to the event defined in the definition by performing the operation specified by the agent in the context of the particular model.
3. The apparatus for controlling a processor set forth in claim 2 wherein: the set of definitions of events is a model.
4. The apparatus for controlling a processor set forth in claim 1 wherein: there is a plurality of the particular models; and the agent is available to more than one of the particular models.
5. The apparatus for controlling a processor set forth in claim 1 wherein: there is a plurality of the particular models; and a given vertex belongs to more than one of the particular models.
6. The apparatus for controlling a processor set forth in claim 1 wherein: there is a plurality of the model types; and the agent is associated with more than one of the model types.
7. The apparatus for controlling a processor set forth in claim 1 wherein: the agent is made available to the particular model when the agent is an entity in the particular model.
8. The apparatus for controlling a processor set forth in any one of claims 1 through 7 wherein: the model type defines a facet type that specifies a type of relationship to a vertex of a model of the type; and the particular model has a facet of the facet type; and the processor manipulates the particular model as specified by the facet type definition for the facet.
9. The apparatus for controlling a processor set forth in claim 8 wherein: there is a plurality of the facet types defined in the model type.
10. The apparatus for controlling a processor set forth in claim 9
wherein: there is a plurality of vertices in the particular model, ones of the plurality of vertices being related to others thereof by facets having types belonging to the plurality of types
11. The apparatus for controlling a processor set forth in claim 10
wherein: a given vertex has a plurality of facets of different facet types.
12. The apparatus for controlling a processor set forth in claim 8
wherein: the facet type definition specifies a set-theoretic character of the relationship.
13. The apparatus for controlling a processor set forth in claim 8
wherein: the facet type definition specifies a constraint on the facet.
14. The apparatus for controlling a processor set forth in claim 8
wherein: the facet type definition specifies a propagator on the facet.
15. The apparatus for controlling a processor set forth in any one of claims 1 through 7 wherein: there is a plurality of the vertices; the vertices of a first subset of the vertices are instance vertices representing things; and the vertices of a second subset of the vertices are vertices representing abstractions concerning the things represented by the first subset of the vertices.
16. The apparatus for controlling a processor set forth in claim 15
wherein: an instance vertex may represent an instance of an abstraction represented by a vertex of the second subset.
17. The apparatus for controlling a processor set forth in claim 15
wherein: an instance vertex may exhibit a property represented by a vertex of the second subset.
18. The apparatus for controlling a processor set forth in claim 15
wherein: there is a plurality of the particular models; and a vertex in the first subset represents another particular model.
19. The apparatus for controlling a processor set forth in claim 15
wherein the apparatus further comprises: things represented by the instance vertices in the storage device; and a thing represented by an instance vertex is locatable from the instance vertex.
20. Apparatus employed in a system having a processor and storage accessible thereto for using information contained in a first graph stored in the storage to make or modify a second graph stored in the storage, both graphs having vertices containing information that are joined by edges specifying relationships between the vertices, the apparatus comprising: a third graph stored in the storage, the third graph including a pattern part that specifies a pattern of vertices and/or edges in the first graph and a synthesis part that specifies a modification of the second graph; and code stored in the storage and executable by the processor that matches the pattern part to vertices and/or edges having the pattern in the first graph and uses information from the matched vertices and/or edges in the first graph as specified in the synthesis part to make or modify the second graph.
21. The apparatus set forth in claim 20 wherein: the first graph and the second graph have types; and the third graph is associated with the types of the first and second graphs.
22. The apparatus set forth in claim 20 or claim 21 wherein: there is a plurality of the third graphs; and the code matches the third graphs to the first graph in a predetermined order.
23. The apparatus set forth in claim 22 wherein: the predetermined order is associated with the types of the first and second graphs.
24. The apparatus set forth in claim 20 or claim 21 wherein: there is a plurality of predefined ones of the third graphs; and a user of the apparatus selects a set of the third graphs from the predefined ones and determines an order in which the third graphs in the set are matched to the first graph.
25. The apparatus set forth in claim 24 wherein: the user further associates the selected and ordered set of the third graphs with the types of the first and second graphs
26. The apparatus set forth in claim 24 further comprising: a graphical user interface for selecting the set of third graphs and determining the order.
27. The apparatus set forth in claim 20 further comprising: a graphical user interface for specifying a pattern part in the third graph by selecting one or more vertices in the first graph.
28. The apparatus set forth in claim 27 wherein: the graphical user interface further specifies the pattern part by specifying a class of edge in the first graph..
29. The apparatus set forth in claim 28 wherein: the first graph is hierarchical; and when the graphical user interface selects a plurality of the vertices, the pattern part in the third graph includes vertices corresponding to the vertices in the smallest subtree with regard to the selected class of edge in the first graph that contains the selected vertices.
30. The apparatus set forth in claim 20 wherein: a vertex of a pattern part matches a class of vertex in the first graph and an edge of a pattern part matches a class of edge in the first graph.
31. The apparatus set forth in claim 30 wherein: an annotation in the vertex of the pattern part specifies the class of vertex or the class of edge.
32. The apparatus set forth in claim 20 wherein: the synthesis part includes a vertex that that specifies a class of vertex or a class of edge that is to be made or modified in the second graph.
33. The apparatus set forth in claim 32 wherein: an annotation in the vertex specifies the class of vertex or class of edge.
34. The apparatus set forth in claim 32 wherein: the synthesis part further includes a vertex that combines information from a plurality of vertices in the third graph for use in a further vertex of the synthesis part.
35. The apparatus set forth in claim 32 wherein: an edge in the synthesis part together with an annotation in a vertex of the synthesis part specifies a portion of a flow of the information via the third graph from the matched vertices of the first graph to the edges and/or vertices being made or modified in the second graph.
36. Apparatus employed in a system having a processor and storage accessible thereto for producing a graph having a plurality of vertices and edges in the storage, the apparatus comprising: a linear representation of a plurality of nested entities in the storage; and code stored in the storage and executable by the processor that makes the graph in response to the linear representation, the graph having vertices representing the entities and edges representing the nesting.
37. The apparatus set forth in claim 36 wherein: the entities in the linear representation have content; and the code associates content from the entity with the vertex corresponding to the entity.
38. The apparatus set forth in either of claims 36 or 37 wherein: the linear representation is an XML document and the entities are XML constructs.
39. Apparatus employed in a system having a processor and storage accessible thereto for producing a linear representation in the storage, the linear representation having a plurality of nested entities and the apparatus comprising: a graph in the storage, the graph having vertices connected by edges indicating an enclosure relationship; and code stored in the storage and executable by the processor that makes the linear representation in response to the graph, entities in the linear representation corresponding to vertices in the graph and the entities being nested in the linear representation as indicated by the edges of the graph.
40. The apparatus set forth in claim 39 wherein: the vertices in the graph are associated with content; and an entity in the linear representation corresponding to a vertex contains the content associated with the vertex.
41. The apparatus set forth in either of claims 39 or 40 wherein: the linear representation is an XML document and the entities are XML constructs.
42. Apparatus employed in a system having a processor and storage accessible thereto for translating a XML document having a first DTD into another XML document having a second DTD, the apparatus comprising: a first translator that executes on the processor and translates any XML document having the first DTD into a semantic representation in the storage of information from the translated XML document; and a second translator that executes on the processor and translates the semantic representation into the other XML document.
43. The apparatus set forth in claim 42 wherein: the semantic representation is a first graph of a type having vertices and edges that are capable of representing information from any XML document having the first or second DTD.
44. The apparatus set forth in claim 43 further comprising: a transform component that makes or modifies a target graph using information in a source graph as specified in a tag pattern graph that matches patterns of vertices and edges in the source graph and uses information contained in the matched vertices and edges to make and/or modify vertices and/or edges in the target graph, the first translator using the transform component in translating from XML to the first graph and the second translator using the transform component in translating from the first graph to XML.
45. The apparatus set forth in claim 44 wherein: the first translator comprises an import component that translates any XML document into a graph in the storage of a mirror type whose edges describe nesting in the XML document and whose vertices contain the information from the XML document; the second translator comprises an export component that translates any graph in the storage of the mirror type into an XML document; the first translator makes and/or modifies the first graph using the transform component with a first tag pattern graph and a first mirror graph made by the import component from the XML document as a source graph and the first graph as a destination graph; and the second translator makes and/or modifies a second graph of the mirror type using the transform component with a second tag pattern graph and the first graph as a source graph and the second graph of the mirror type as a destination graph and makes the other XML document using the export component.
46. Apparatus employed in a system having a processor and storage accessible thereto that translates an XML document in the storage that has a particular DTD into a first graph in the storage whose vertices and/or edges represent information from the XML document, the apparatus comprising: an import component that translates any XML document into a graph in the storage of a mirror type whose edges describe nesting in the XML document and whose vertices contain information from the XML document; and a transform component that makes or modifies a target graph using information in a source graph as specified in a tag pattern graph that matches patterns of vertices and edges in the source graph and uses information contained in the matched vertices and edges to make and/or modify vertices and/or edges in the target graph, the import component translating the XML document into a second graph and the transform graph using a tag pattern graph for the particular DTD with the second graph as a source graph to make and/or modify the first graph.
47. The apparatus set forth in claim 46 wherein: the tag pattern graph includes a pattern part that specifies a pattern to be matched and a synthesis part that specifies how the information contained in the matched vertices and/or edges is to be used to make and or modify vertices and/or edges in the target graph, the transform component making and/or modifying the target graph as specified by the synthesis part.
48. Apparatus employed in a system having a processor and storage accessible thereto that translates a first graph in the storage whose vertices and edges represent information into an XML document that contains the information and has a particular DTD, the apparatus comprising: a transform component that makes or modifies a target graph using information in a source graph as specified in a tag pattern graph that matches patterns of vertices and edges in the source graph and uses information contained in the matched vertices and edges to make and/or modify vertices and/or edges in the target graph; and an export component that translates a graph in the storage of a mirror type whose edges describe nesting in an XML document and whose vertices contain information to be contained in the XML document into the XML document, the transform component using a tag pattern graph for the particular DTD with the first graph as a source graph and a second graph of the mirror type as a destination graph and the export component translating the second graph into the XML document.
49. The apparatus set forth in claim 48 wherein: the tag pattern graph includes a pattern part that specifies a pattern to be matched and a synthesis part that specifies how the information contained in the matched vertices and/or edges is to be used to make and or modify vertices and/or edges in the target graph, the transform component making and/or modifying the target graph as specified by the synthesis part.
Description
CROSS REFERENCES TO RELATED APPLICATIONS
[0001] The present application claims priority from U.S. provisional application No. 60/178,363, D. T. Allemang, System for translating XML documents into interactive multimodal semantic models and translating such models into AML documents, filed Jan. 27, 2000 and is a continuation-in-part of the international patent application PCT/US00/01042, J. S. Anthony, A system for composing applications based on explicit semantic models, event driven autonomous agents, and resource proxies, filed Jan. 14, 2000 and claiming priority from U.S. provisional application No. 60/116,257, J. Anthony, et al., A system for composing applications based on interacting multimodal models and the explicit separation of models and their subjects, filed Jan. 16, 1999. The present patent application contains the complete Detailed Description and figures of PCT/US00/01042. The new material begins with the section Innovations and benefits of the Ariadne system and includes new FIGS. 18-26.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The invention pertains generally to techniques for composing software and more particularly to composition techniques which permit separate specification of context information and control information. The software composition techniques are illustrated with an example that shows how they can be employed to implement a new technique for converting XML having one DTD into XML that has another DTD. The new technique in turn employs new generally-applicable techniques for making or modifying a graph from information contained in another graph.
[0004] 2. Background of the Invention
[0005] a. Software Composition Architecture
[0006] One of the main goals behind the design of the Ariadne system disclosed in the parent of the present patent application was to provide a capability for a new paradigm for software composition. In other composition frameworks (e.g., Object Oriented Programming, Modular Programming), information about the context of use of a program are intertwined with the control structure of the program in the program code; if one wanted to reuse the control information in a new semantic context, one would have to reverse engineer the code to separate these aspects, and reengineer a solution in the new context.
[0007] What is needed is a program composition architecture that separates control information from contextual information. Such an architecture would allow for a more robust kind of reuse than is possible in any program component based reuse paradigm. It is an object of the present patent application to provide such an architecture
[0008] b. Using the Software Composition Architecture to Solve an XML Translation Problem
[0009] XML (extensible Markup Language), described in George Lawton, "Unifying Knowledge with XML", Knowledge Management, August 1999, is an emerging standard for information interchange between applications. XML advocates promise that XML will create a true "open system" environment in which it is easy to create integrated systems that exchange information using XML. In order for XML to be able to deliver on these promises, it must be possible to read and write information in XML easily and smoothly. As part of the standardization process, XML parsers and the Document Object Model (DOM) have been made available in the public domain to perform the basics of processing XML. Using these tools is a skilled programming task. With these tools, system integration involves settling on an XML template (called a DTD) that defines how the XML documents will be formatted. Then all the information in each system is exported/imported into/from XML using this DTD. If both systems use the same DTD, then this happens quite smoothly, and the overall system integration proceeds as promised. Certain aspects of this integration process can be simplified by current tools; for example, Bluestone Software provides tools that allow non-programmers to create XML documents and DTDs using graphical interfaces, making certain parts of this process more easily edited by non-programmers.
[0010] The problem facing the industry today is that there is no standards body for the DTDs. Each individual who uses an XML application can create his or her own DTD. In the case of multiple DTDs, interchange will require more sophisticated tools than just parsers and servers; there needs to be a way to match patterns in an XML document and do something with them. The W3 consortium has recognized this problem, and is addressing it with the XML Query Language, explained in The W3
Consortium, XML Query Language Workshop CFP, http://www.w3.org/TandS/QL/Q- L98/cfp. However, the XML query language only deals with specifying searches through an XML document, not with the problem of transforming the XML document into another form. Thus proposed solutions to the W3 XQL call can only solve part of the problem; the problem of translating information from one DTD to another remains. It is thus another object of the present patent application to provide a technique for translating information from one DTD to another.
SUMMARY OF THE INVENTION
[0011] One of the design aims of the Ariadne system described in the parent of the present patent application is to provide a software composition architecture that separates control information from contextual information; in the Ariadne system, context is specified by defining graph types and making graphs of the types and control information is specified by defining agents that operate in the contexts provided by graphs of particular types. The program composition architecture provided by Ariadne is used to implement new techniques for extracting information from one graph and using the information to make or modify another graph and these techniques are in turn used to implement new techniques for translating an XML document into an Ariadne graph that represents the semantics of the XML document and vice-versa. The semantic representation and the new techniques are then used to implement a new system for translating an XML document having one DTD into another XML document that has similar semantics but a different DTD.
[0012] Other objects and advantages will be apparent to those skilled in the arts to which the invention pertains upon perusal of the following Detailed Description and drawing, wherein:
BRIEF DESCRIPTION OF THE DRAWING
[0013] FIG. 1 illustrates how graphs may be used to show relationships among entities;
[0014] FIG. 2 shows a complex model;
[0015] FIG. 3 shows how the concepts of a model are related to instances and agents;
[0016] FIG. 4 shows the structures that represent model types, models, concepts, and instances in a preferred embodiment;
[0017] FIG. 5 is an overview of a system in which models and model types are implemented;
[0018] FIG. 6 is an overview of views and viewers in the system of FIG. 5;
[0019] FIG. 7 shows a user interface for defining a new model;
[0020] FIG. 8 shows a user interface for defining a root concept;
[0021] FIG. 9 shows a user interface for adding a subclass concept to a model of the taxonomy type;
[0022] FIG. 10 shows a user interface for adding an instance to a concept of a model;
[0023] FIG. 11 shows a user interface for adding a referent to an instance;
[0024] FIG. 12 shows a user interface for displaying a model;
[0025] FIG. 13 shows the events model in Ariadne;
[0026] FIG. 14 shows the actions model in Ariadne;
[0027] FIG. 15 shows the operations model in Ariadne;
[0028] FIG. 16 shows two agents attached to the root of a taxonomy model;
[0029] FIG. 17 shows a user interface for attaching an agent to a model;
[0030] FIG. 18 shows an agent and its relationship to other components of the Ariadne system;
[0031] FIG. 19 shows an XML document according to the OCF standard;
[0032] FIG. 20 shows how XML documents may be translated using semantic representations;
[0033] FIG. 21 shows how models may be used to translate other models;
[0034] FIG. 22 shows an XML mirror model for the document of FIG. 19;
[0035] FIG. 23 shows a number of tag pattern models that are used to translate the XML mirror model of FIG. 22 into a catalog model;
[0036] FIG. 24 shows another such tag pattern model;
[0037] FIG. 25 shows a tag pattern model used to translate a catalog model into an XML mirror model; and
[0038] FIG. 26 shows the GUI used to define pattern vertices in a tag pattern model by specifying vertices in a source model.
[0039] Reference numbers in the drawing have three or more digits: the two right-hand digits are reference numbers in the drawing indicated by the remaining digits. Thus, an item with the reference number 203 first appears as item 203 in FIG. 2.
DETAILED DESCRIPTION
[0040] The following Detailed Description will begin with a simple example of how the invention may be used and a description of an implementation of the example and will conclude with a generalized description of the invention.
[0041] Using Graphs to Specify Multiple Aspects of a Collection of Data: FIG. 1
[0042] For purposes of the following informal discussion, the term graph is used in the sense of a set of points where at least one of the points is connected to itself or another point by an arc. The points are termed the vertices of the graph and the arcs are termed its edges. In the graphs used in the invention, the vertices represent entities such as concepts and the edges represent relationships between the concepts. In FIG. 1, graphs are used to represent a taxonomy 101 of concepts relating to clothing. The concepts belonging to a given taxonomy are related to each other in both a top-down fashion, i.e., from the most general concept to the least general concept, and a bottom-up fashion, i.e., from the least general concept to the most general. In the top-down relationship, the concepts are related as class and subclass; for example, in taxonomy 101, footwear is a subclass of clothing and insulated boots is a subclass of footwear. The bottom-up relationship is termed an is a relationship, i.e., insulated boots is one of the concepts of footwear and footwear is one of the concepts of clothing.
[0043] Thus, in taxonomy 101, each vertex 103 represents a concept relating to clothing, and edges 105 connect the vertices 103. The arrowhead on the edge indicates the direction of the relationship. There are two graphs in FIG. 1; one graph, indicated by dashed straight lines 107, indicates the subclass relationships between the concepts represented by the vertices; the other graph, indicated by solid arcs 109, indicates the is a relationships. Thus, graph 107 shows that outerwear 113 and footwear 115 are subclasses of clothing 111 and parkas 117 and raingear 119 are in turn subclasses of outerwear 113. Further, as shown by solid arcs 109, sandals 121 has an is a relationship to footwear 115, footwear 115 has an is a relationship to clothing 111, and so forth for the other concepts. Each concept has a solid arc 119 pointing to itself because each concept is itself, and therefore has an is a relationship with itself.
[0044] Subclass graph 107 and is a graph 109 thus organize the set of clothing concepts in FIG. 1 according to two aspects: a subclass aspect and an is a aspect. Subclass graph 107 tells us that outerwear 113 has two subclasses: parkas 117 and raingear 119; is a graph 109 tells us that outerwear 113 is clothing 111. Graphs 107 and 109 make it possible to consider any concept in taxonomy 101 from the point of view of its subclass relationships to other concepts and from the point of view of its is a relationships to other concepts. The operation of considering an entity in taxonomy 101 first as it belongs to one of the graphs and then as it belongs to another of the graphs is termed pivoting. The concepts of FIG. 1 can of course have relationships other than those of taxonomy 101, and those relationships, too, can be represented by graphs made up of concepts belonging to the set shown in FIG. 1 and edges connected to them. Each such graph organizes the set of clothing concepts according to another aspect, and pivoting permits a given concept to be seen according to any of the aspects represented by any of the graphs that the concept belongs to.
[0045] Models and Facets: FIG. 2
[0046] Taxonomy 101 is of course only one of many possible ways of organizing the set of concepts shown in FIG. 1. In the following discussion, a particular way of organizing a set of concepts or other entities is termed a model. Thus, in FIG. 1, the concepts are organized according to a taxonomy model. As we have seen, when concepts are organized in this fashion, the relationships between them are shown by two graphs: subclass graph 107 and is a graph 109; each of these graphs is termed a facet of the model; thus the taxonomy model of FIG. 1 has a subclass facet 107 and an is a facet 109. The pivoting operation permits a concept in the set to be considered according to each of the facets that the concept belongs to.
[0047] The model of FIG. 1 is simple, i.e., it is a single taxonomy. A model may, however, also be complex, i.e., composed of two or more models. FIG. 2 shows such a complex model 201. In FIG. 2, the set of concepts of FIG. 1 has been expanded so that the items of clothing can be organized according to the season they are appropriate for. The new concepts represent the five seasons of the New England climate: winter 205, mud season 206, spring 213, summer 207, and fall 215. The set of concepts shown in FIG. 2 is organized according to complex model 201, which in turn is made up of two simple models. Clothing taxonomy model 209 is the taxonomy model shown in FIG. 1; seasonal clothing model 211 is a model of type simple graph which relates concepts representing clothing to concepts representing the five New England seasons. The facets of model 211 relate a season concept to clothing concepts for the kinds of clothing worn in the season and a clothing concept to the seasons in which the clothing is worn. The concepts parkas 117, raingear 119, sandals 121, and insulated boots 123 belong to both models. Considered as part of clothing model 209, sandals 121 is a subclass of footwear 115; considered as part of the seasonal clothing model, sandals 121 is related to the seasons in which sandals are worn, namely spring, summer, and fall. Outerwear 113, on the other hand, belongs only to clothing model 209, while winter 205 belongs only to seasonal clothing model 211.
[0048] Complex models permit additional operations. For instance, pivoting may be used with complex model 201 to consider a given concept according to each facet of each of the models the concept belongs to. For example, the concept sandals may be considered on the one hand as it is related to the concepts of clothing model 209 and on the other as it is related to the concepts of seasonal clothing model 211. Moreover, since each model organizes the concepts in different ways, the models define different sets of concepts and set operations such as union, intersection, difference, and set xor may be applied.
[0049] Model Types
[0050] Any set of entities which belongs to a taxonomy can be organized by means of a taxonomy model like model 209. Just as all taxonomies are alike in how they organize the entities that belong to them, any taxonomy model will have an is a facet and a subclass facet and similar relationships will exist between the entities belonging to a given facet. Moreover, any user of a taxonomy model will want to perform similar operations using the taxonomy. For example, a user will want to display all of the concepts that are subclasses of a given concept or all of the concepts that a given concept has an is a relationship with. One can thus speak of the taxonomy model type, and all other models will similarly belong to model types. As with models, a model type may be either simple or complex. Because all models belonging to a given model type have similar operations, it is possible to define those operations for the model type and make them automatically available for any model of the type.
[0051] In the present invention, users of the invention may define their own model types or use model types defined by others. A model type is defined as follows:
[0052] a facet specifier specifies each of the facets belonging to models of the type;
[0053] within each facet specifier, a relation specifier that specifies how entities joined by an edge of the facet are related;
[0054] propagation specifiers for the facets and/or the entire model; a propagation specifier specifies how operations belonging to models having the model type are performed.
[0055] The model type for the taxonomy model thus has a subclass facet specifier for the subclass facet and an is a facet specifier for the is a facet. The relation specifier for the subclass facet specifies that the subclass relationship is transitive, non-reflexive, and non-symmetric. The fact that the relationship is transitive means that if entity A is a subclass of entity B and entity C is a subclass of entity B, then entity C is a subclass of entity A, or in terms of FIG. 1, that parkas 117 is a subclass of clothing 111. The fact that the subclass relationship is non-reflexive means that an entity cannot be a subclass of itself (which is why there are no edges of subclass graph 107 connecting an entity to itself). The fact that the relationship is non-symmetric means that if entity B is a subclass of entity A, entity A cannot be a subclass of entity B or in terms of FIG. 1, if parkas 117 is a subclass of outerwear 113, outerwear 113 cannot be a subclass of parkas 117. The relation specifier for the is a facet specifies that the is a relationship is transitive, reflexive, and non-symmetric. Thus, as shown in FIG. 1, parkas 117 is itself as well as outerwear and clothing, but if parkas are outerwear, then outerwear cannot be (just) parkas.
[0056] The relation specifiers are used to define procedures for adding concepts to models belonging to the class. For instance, if new concepts, say swimwear, bathing suits, and wetsuits are added to the model of FIG. 1, with swimwear being a subclass of clothing and bathing suits and wetsuits being subclasses of swimwear, the relation specifiers will ensure that there are edges in the subclass facet connecting clothing to swimwear and swimwear to bathing suits and wetsuits, but no edges in the subclass facet connecting clothing to wetsuits or bathing suits to wetsuits, and will similarly ensure that there are edges in the is a facet connecting each of the new concepts to itself and wetsuits and bathing suits to swimwear and swimwear to clothing, but no edges connecting wetsuits and bathing suits to clothing and none connecting wetsuits and bathing suits to each other.
[0057] One example of a propagator for a taxonomy is a subclass display propagator that displays all of the subclasses belonging to a class. The subclass display propagator works by simply following the subclass facet beginning at the specified class. Thus, if the class is clothing, the display propagator will display outerwear 113, parkas 117, raingear 119, footwear 115, sandals 121, and insulated boots 123. Another example is an is a display propagator that displays the concepts that the specified concept belongs to. This propagator simply follows the is a facet beginning at the specified concept. Thus, for sandals 121, it will display sandals 121, footwear 115, and clothing 111.
[0058] Relating Concepts to the World: FIG. 3
[0059] In order to be useful, the cards in a library card catalog relate the concepts used in the catalog to books in the library. The same is true with concepts organized by models. In order for the concepts to be useful, they must be related to entities that are examples of the concepts. In the invention, an entity that is or may be an example of a concept is termed an instance, and an instance that is an example of a concept is termed an instance of the concept. It should be pointed out here that one of the things which may be an example of a concept is a model, and thus, an instance may be a model. Using models as instances in other models is one way of making complex models.
[0060] All of the instances available to a system in which the invention is implemented is termed the world of the system. In general, one makes a model to deal with a given area from several aspects, and this area is termed the model's subject. For example, the subject of model 209 is clothing and all of the instances of its concepts represent items of clothing. One thus makes a model for a subject and then relates the model to instances in the world that are relevant to the model's subject. The instances in the world that are relevant to a given subject are termed the subject's collection.
[0061] FIG. 3 shows how concepts are related to instances in a preferred embodiment. FIG. 3 shows a set 301 of instances representing objects accessible to the system upon which model 209 is being used. This set 301
is termed herein the world of the model. The subject of model 209 is clothing; in FIG. 3, instances belonging to clothing's collection are surrounded by a curve, as shown at 306. Thus, in FIG. 3, model 209 is being applied to world 301, but the instances with which it is actually concerned belong to clothing collection 306. Item instances in clothing collection 306 are consequently termed clothing instances 307. The instances in clothing collection 306 with which model 209 is concerned all represent items of clothing or agents, as will be explained below; however, other instances in clothing collection 306 may represent models. Of course, more than one set of concepts may apply to a subject or a world and a given set of concepts may be applied to different subjects or worlds.
[0062] There are two kinds of instances in world 301: item instances 303, which represent items, including other models, that may be related to concepts, and agent instances 304, which represent programs that are executed by models in response to the occurrence of events such as the addition of a concept to the model or a request by a user to view items belonging to a given concept. While the program represented by an agent may be any program at all, the program executes in the context of the model and can thus take advantage of the model's facets and propagators. In effect, the operations defined for the model are available to agents in the same fashion that programs belonging to run-time libraries are available to application programs.
[0063] The mechanism by which an item instance 303 or an agent instance 304 is related to a concept is an instance facet 309. There is an instance facet 309 for each instance that is related to a given concept. Thus, instance facets relate clothing instances 307(b and c) to concept 121. Of course, an instance may have instance facets connecting it to more than one concept and even to concepts belonging to different models. Generally, the item represented by an instance has another representation, termed an object, in the computer system. What kind of object an instance represents will depend on the application for which the invention is being used. For example, the clothing instances might represent database identifiers of rows describing products in a database table describing a clothing company's products or they might be URLs of WEB pages describing the products.
[0064] Propagators may work on instances as well as concepts. For example, a propagator may be defined for the taxonomy model type which retrieves all of the instances associated with a concept and its subclasses. It does so by first following the instance facets for the concept and retrieving all of the concept's instances. Then it follows subclass facet 107 from the concept to its subclasses, their subclasses, and so on down to concepts which have no subclasses. At each concept, the propagator retrieves the instances associated with the concept. Thus, in FIG. 3, when the propagator is applied to concept 115, it will retrieve the clothing instances 307 labeled a,b,c,d in collection 306.
[0065] One agent instance is shown in collection 306: the instance for refinement agent 308. Refinement agent 308 is executed when a concept representing a new subclass is added to model 209. For example, in model 209 as shown in FIG. 1, the concept footwear 115 has two subclasses: sandals 121 and insulated boots 123. Instances which belong to neither of those subclasses belong to footwear. One such instance, 307(a), is shown in FIG. 3. The instance represents gardening clogs. Now, the user of the model is planning to sell more kinds of clogs and consequently decides to add the concept clogs as a subclass of footwear. When that is done, instance 307(a) should become an instance of clogs rather than an instance of footwear. This process of moving an instance into the proper subclass concept is termed refinement, and refinement agent instance 308
automatically does refinement whenever a subclass concept is added to model 209.
[0066] In FIG. 3, refinement agent instance 308 is shown attached to clothing concept 111 and to footwear concept 115. Clothing concept 111 is the broadest concept in the model and is termed the root concept of the model. Of course, every model of type taxonomy has a root concept. In models of the taxonomy type, an agent attached to a concept propagates along subclass facet 107; thus, any concept which is a subclass inherits the agent. Consequently, each concept in model 209 has its own copy of refinement agent instance 308. In FIG. 3, only the copies for clothing 111 and footwear 115 are shown. Since each concept has its own copy of refinement agent instance 308, execution of the agents can be done in parallel.
[0067] When the user adds the new subclass clogs to footwear 115, that event causes refinement agent instance 308(k) to execute. The program follows the subclass facet to the new subclass concept clogs and examines it to determine whether any of the item instances that are related to it are also related to footwear 115. One such item instance, garden clogs, is, and the program rearranges the instance facets 309 so that there is now an instance facet relating clogs to garden clogs, but no longer an instance facet relating footwear to garden clogs. As can be seen from the foregoing, an agent, while user-defined, operates within the context of the environment provided by the model and takes advantages of the operations defined for the model's type.
[0068] Representing Models, Concepts and Instances: FIG. 4
[0069] FIG. 4 shows at 401 how the representations of model types, models, concepts, and instances are structured in a preferred embodiment. In overview, as shown by the arrows in FIG. 4, each model definition 413
refers to a model type definition for its model type and to a set of node structures. Some of the node structures represent concepts belonging to the model and others represent instances of the concepts. Each concept node 425 refers to its model and each instance node 437 refers to the concepts the node is instances of. There may be many models of a given model type, a given model may have many concepts, a given concept may have many instances and a given instance may be an instance of many concepts. A model type definition may thus be located from any model definition of its type, a model definition may be located from any of its concepts, and a concept may be located from any of its instances.
[0070] Continuing in more detail, model type definition 403 includes the model type's name 405, a description 407 of the model type, a facet specifier list 409 that specifies the kinds of facets that models of the type have, and a propagator list 411 that specifies the propagators for models of the type.
[0071] Model definition 413 includes the model's name and description at 415 and 417, a list 419 of the concept and instance nodes in the model, a facet list 421 showing how the model's nodes are related by each facet of the model, and a model type name 423, which refers back to the model type definition 403 for the model.
[0072] Concept node 425 includes the concept's name and description at 427
and 429, a property list 431, which is a list of user-defined properties of the concept, and attribute list 433, which is a list of attributes for the concept. Each attribute specifies the name of a facet to which the concept node belongs and the name of the node which is the next neighbor of the concept node in the facet. The facets, and correspondingly, the attributes may be subdivided into model facets, which specify facets whose vertices are made up only of concepts of the model, and instance facets, which specify facets connecting concepts and instances. What kinds of model facets a model has is determined by its model type; in a preferred embodiment, there are three kinds of instance facets that run from the concept to an instance:
[0073] item facets, which connect a concept to an item instance representing an item that belongs to the concept;
[0074] exhibitor facets, which connect a concept to an item instance representing an item that possesses a property specified by the concept; and
[0075] action facets, which connect a concept to an agent instance.
[0076] Exhibitor facets are used to deal with concepts like color. A blue clog, for example, exhibits the property of being blue and would therefore be connected to a concept representing the color blue by an exhibitor facet. Owning model 435, finally, refers to model definition 413 for the model the concept belongs to.
[0077] Instance node 439, finally, has an instance name 439, an instance description 441, and a property list 443 for the instance. Included in property list 443 is referent 445, which specifies how to locate the object represented by instance node 439. What the referent is depends on what kind of object the instance node represents. For example, if the instance node represents a Web page, the referent will be the page's URL; if it represents an agent, it may be a pathname for the agent's code; if it represents another model, the referent will be the model's name. Attribute list 447, finally, specifies the instance facets that run from the instance to the concepts it belongs to. There is one such facet corresponding to each of the instance facets running from the concept to the instance. Each of these facets is termed the dual of the corresponding facet. Thus, the item of facet is the dual of the item facet; exhibitor of is the dual of the exhibitor facet; and action of is the dual of the action facet.
[0078] Applying all of the foregoing to concept 115 of model 209, we see that concept node 425 for that concept has model attributes for the subclass facet for concepts 121 and 123 and for the is a facet for itself and for concept 111, an item instance attribute for clothing instance 307(a), and an action instance attribute for refinement agent instance 308(k). Instance node 437 for clothing instance 307(a) has an item of instance attribute for concept 115 and the instance node for refinement agent instance 308(k) has an action of attribute for concept 115.
[0079] In a preferred embodiment, the structures that make up the components of a model are all linked by name, and hash functions and hash tables are used to relate names in the structures to the locations of the structures in memory. For example, to find a concept instance, the preferred embodiment takes the name and presents it to a hash function, which hashes the name to obtain an index of an entry in a hash table and uses the index to find the entry for the name in the hash table; that entry contains a pointer to the location of the concept instance. In other embodiments, other techniques such as pointers might be used to link the components of the structures 401 that represent a model.
[0080] A System that Uses Models to Organize Information: FIG. 5
[0081] FIG. 5 is an overview of a system 501 that uses models to organize information. The system, called Ariadne, has three major components:
[0082] server 509 maintains the data structures 401 that implement model types, models, and instances, together with views 513, which provide logical descriptions of models and their parts, but do not specify how the model will appear in a specific GUI.
[0083] a number of viewers 507, which present the contents of the views as required for particular graphical user interfaces (GUIs); and
[0084] ERIS (external resource interface system) 505, which provides access to the systems 503 that contain the objects represented by instances 407.
[0085] Server 509 may be implemented on any kind of computer system, and viewers 507 may be monitors, Web browsers, PC's or other systems that have either local or remote access to the computer system upon which server 509 is implemented. As shown in FIG. 5, the outside systems accessed via ERIS 505 may include relational database systems, with the objects being records or queries, Web servers, with the objects being Web pages, email systems, with the objects being email messages, and systems that use XML as their interface to other systems. The viewers 507 and the components of ERIS 505 interact with the model types, models, agents, views, and instances by way of interfaces 511 defined using Interface Definition Language (IDL).
[0086] An example of how system 501 functions is the following: A user of a viewer 507(i) is interacting with clothing model 209 via a graphical user interface and wishes to see all of the instances of footwear that are currently available in collection 306 of clothing model 209. The user specifies footwear concept 115 and a "display instances" operation. This operation specification arrives via IDL 511 in server 509, and the propagator for the taxonomy model type which retrieves instances retrieves the instances that are related to concepts footwear 115, sandals 121, and insulated boots 103. Ariadne server 509 then typically makes a list of the instances represented by the objects for display in viewer 507(i). If the user of the viewer selects one or more of the instances from the list, Ariadne server 509 provides the referents 445
for the objects represented by the selected instances to ERIS 505, which retrieves the objects referred to by the referents and returns them to Ariadne, which then makes a display using the retrieved objects and sends the display to viewer 507(i). For example, if the clothing instances represent Web pages containing catalog descriptions of the items, when the user of viewer 501 selects an item from the list, Ariadne server 509
will provide the URL for the item's web page to ERIS 505, ERIS 505 will fetch the Web pages, and Ariadne 509 will provide them to viewer 507(i). Ariadne server 509 also provides views 513 which permit a user at viewer 507(i) to define, examine, and modify models. The user interfaces for doing so will be explained in detail later on.
[0087] Details of Views 513: FIG. 6
[0088] FIG. 6 shows details of the implementation of views 513 in a preferred embodiment. Models may have multiple views and views may have multiple presentations. The implementation supports different presentations of the same model concurrently, collaborative modeling and real time knowledge sharing, and independent yet sharable knowledge explorations.
[0089] In Ariadne, views are implemented in a subsystem known as Calyx. Calyx 601 is a CORBA server which exports via IDL specifications an abstract interface for views. Calyx 601 could also be any other distributed middleware server (for example, proprietary RPCs or DCE or possibly DCOM). A view 603 is a collection of bins 605 of information about the target source: A model or a world. Bins hold information such as the current objects being shown, whether the attributes of an object along any given facet are expanded, what facet a bin is looking at, etc. The typical representation 601 of a view is a structure containing (among other things) a container of bins 605.
[0090] All views and bins (as well as any other externally accessible resource) are referenced by opaque IDs which are presented to any viewer 607 logging into Ariadne. A viewer 607 is a active object through which the abstract information is displayed. Each viewer takes the abstract information maintained by Calyx in a view 601 and presents it in a manner which is consistent with the interface requirements and look and feel of a given GUI. For example, a taxonomy might be represented by a graph, an outline, or simply as an indented list of text and the viewer will use whatever resources are provided by its GUI to make the representation. For example, an outline might be presented by a Java Swing tree widget or an MFC tree widget.
[0091] As may be seen from the dashed lines in FIG. 6, a view 601 may be shared by a number of viewers 607. Calyx ensures that all viewers 607
that use a given view 6021(i) are synchronized to the most recent changes in view 602(i). When a viewer 607(j) requests Calyx to update or otherwise change part of the view (say, expand a node in a bin), Calyx performs this operation for viewer 607(i) and then asynchronously sends the update information to all other viewers actively using the view in question. These requests by Calyx to such viewers are client requests to server portions in those viewers. Hence, Calyx is a client and the viewers must implement a server interface for these asynchronous updates.
[0092] Calyx also supports (via the model and world infrastructure) various operations on the contents of bins. Specifically, various set operations (union, set difference, intersection, etc.) may be applied to arbitrary sets of bins. Additional operations may be defined by the user. The effect of the set operations is to apply the operation on the sets of information represented in the bin to produce a new bin (called a composition bin) with the computed resulting information. This is then propagated to all connected viewers. Further, bins may be combined in this way to create constraint networks of composition bins. If any bin in the network is changed (manually or via automated updates) the effect is propagated throughout the entire affected subnetwork in which the bin is connected. These propagated results are sent to all viewiers via the asynchronous operations described above.
[0093] Separation of Levels of Information in the Implementation: FIGS. 3-6
[0094] An important characteristic of Ariadne is the manner in which complexity is reduced and flexibility increased by separating various levels of information from each other. One of these is the separation of model types from models, as seen in the separation of model type definition 403 from model definition 413 in FIG. 4. Another is the separation of models from instances, as seen in FIGS. 3 and 4; this permits multiple models to be built independently of each other and yet work over the same world. It also permits models to be reused in different worlds. Yet another is the separation of an instance from the object that it represents, so that the instance serves as a proxy for the object, as seen in with regard to referent property 445 in FIG. 4 and the use of ERIS interface 505 to retrieve objects represented by referents from a number of different information sources 503. Then there is the agent/model separation: agents run in the context of models, but they are defined in terms of model types, not the individual models. For example, the refine agent will work with any model that has the taxonomy type. Finally, as seen in FIGS. 5 and 6, views 601 are separated from models and worlds and viewers 607 are separated from views 601.
[0095] The User Interface for Building, Modifying, and Displaying Models: FIGS. 7-12
[0096] A particular advantage of model types is that they greatly simplify the construction and modification of models. They do so because the part of Ariadne which constructs models can use the information in the model type to automatically place concepts in the proper facets and in the proper locations in those facets and to propagate information provided by the user to the concepts that require it. One example of such propagation is the propagation of the refinement agent from the root of a model of the taxonomy type via the subclass facet to all of the concepts in the model.
[0097] FIG. 7 shows the dialog box 701 used in a preferred embodiment to create a new model. At 703 there appears a list of the presently-available model types; the user has selected simple taxonomy, indicating that the new model is to have the simple taxonomy model type; in the name box, the user has input "usr:Clothing", indicating that that is to be the name of the new model; at 709, the user may input the description. The result of these inputs is of course the construction of a model definition 413 for the new model, with model name 415 being "usr:Clothing" and model type name 423 being "Simple Taxonomy". List 705
gives an example of what can be done with models. In Ariadne, models themselves are instances in a model whose concepts are model types; one can thus simply select an already-made model from that model. In instance node 437 for an instance representing a model, referent 445 simply specifies the location of the model's model definition 413. The action model similarly treats agents as instances of a model whose concepts are the model types the agents are written for.
[0098] FIG. 8 shows the dialog box 801 used to add a root concept to the subclasses facet of the new model "Clothing". At 803 would normally appear the concepts that are presently in the model; the field is empty, as the model as yet has no concepts. At 805, the user writes the name of the root concept, and as before, the user may also add a description. The result of these inputs is the creation of a concept node 425 with the name "Clothing" in field 427 and the model name "usr:Clothing" in field 435. Since "Clothing" is a root concept and there are no other nodes, the taxonomy type requires that there be as yet no subclass attributes in attribute list 433, but a single is a attribute for "Clothing" itself, and Ariadne automatically adds these to "Clothing"'s concept node 425.
[0099] FIG. 9 shows the dialog box 901 used to add subclasses to an existing taxonomy model. Here, the model already has as subclasses of the root concept clothing the concepts accessories, apparel, swimmwear, and footwear, and further subclasses are being added to to the apparel subclass. At 903, the name apparel of the concept to which subclasses is being added appears; at 904, names of aready existing concepts appear; since only the first level of concepts have as yet been defined, the names are those of concepts at the same level as apparel; at 905, finally, is a field for adding a newly-made concept.
[0100] A user may add a subclass either by selecting from among concepts listed in 904 or by using field 905 to add a newly-made subclass. For each newly-made subclass concept that is added, Ariadne creates a concept node 425 with the name of the concept at 427 and the name of the model at 435; for each concept being added as a subclass, Ariadne adds attributes in attribute list 433 for the is a facet specifying the new concept node itself and the concept node for the apparel concept. Ariadne further creates an attribute in attribute list 433 in the concept node for the apparel concept for the subclass facet which specifies the new concept node. Thus, when all of the subclasses have been added, they all belong to the subclass and is a facets in the manner required for the taxonomy model type. It should be pointed out here that if the user attempts to select one of the concepts listed in 904 to be added to apparel, Ariadne will determine from the model type that this is not possible in the taxonomy model type (in a taxonomy, a concept at one level of the taxonomy may not be a subclass of another concept at the same level) and will not add the concept but will indicate an error. In other embodiments, Ariadne may simply not display concepts that cannot be added to the concept selected at 903.
[0101] FIG. 10 shows dialog box 1001 used to relate instances to a concept. Dialog box 1001 has the same form as dialog box 901, with area 903 containing the name of the concept to which the instances are being related, area 905 containing the names of instances that are available to be added to the concept, and field 1007, which can be used to add a newly-made instance. When a newly-made instance is added, an instance node 437 is created for the instance, with the instance's name at 439 and any description provided by the user at 441. For a newly-made or prevously-existing instance, an attribute for the item of facet that indicates the concept sweaters is added to the instance node's attribute list 447, and one for the item facet that indicates the instance is added to the concept node's attribute list 433. Similar dialog boxes are used to add agents and items that are exhibitors, with corresponding modifications in the attribute lists of the concept and instance nodes. Ariadne also has a copying interface that can be used to select instances belonging to a concept in one one model to become instances of a concept in another. The attribute lists 433 off the instance nodes for the copied instances are modified to add attributes for the instance of facet specifying the concept, and the other concept's attribute list 433 is modified to include attributes for the instance facet for the newly added instances.
[0102] FIG. 11 shows how referent fields 445 are set in instance nodes 437. Window 111 has three subwindows: two show models that apply to the clothing world: "clothing categories" and "fabrics". Both models belong to the taxonomy type, and thus both can be displayed as outlines, as shown at 1103. The user wishes to add referents, in this case the URLs of Web pages that show the items represented by the instances, to the instances that belong to the concept "apparel". In terms of facets, that is all of the instances which have an is a relationship to "apparel", that is, the instances that are related to "apparel" and all of its subclasses. To perform this operation the user selects "apparel" in outline 1103; Ariadne then uses a propagator for the taxonomy model type to generate the list seen at 1107, which is the list of all of the instances that belong to "apparel" and its subclasses. To assign an URL to an instance, the user writes the URL opposite the instance in field 1109. The URL for a given instance goes into referent 445 in node 437 for the instance.
[0103] FIG. 12 shows how Ariadne displays a model. Model 1201 is a taxonomy of the events handled by Ariadne. The boxes are the model's concepts and the arcs 1203 are the arcs of one of the facets, in this case, the is a facet. Selection of facets to be viewed is controlled by check box 1205; as seen there, model 1201 is to be displayed showing its concepts and its is a facets. More than one facet may be selected, in which case, the arcs for each selected facet are displayed simultaneously.
[0104] Architecture of Model Types
[0105] Facets and Facet Specifiers
[0106] As could be seen from the taxonomy models explored in the foregoing, all models of a given type have the same kinds of facets. To define a model type, therefore, one defines its facets. Each facet of a model is defined by its corresponding facet specifier. All the facets available to a model are determined by the set of facet specifiers given in the model's corresponding model type definition (see below).
[0107] Each facet specifier defines the set theoretic relational properties of the base relation captured by the facet and provides an interpretation of what the relation is intended to convey. This interpretation provides the meaning of the facet through semantic constraints on what concepts may be related by the facet and how the facet is mapped to facet descriptions in other model types. Hence, the set of facet specifiers defines the complete semantics of the model type at any given concept in an instance of that model type.
[0108] Def: Facet-specifier. A facet specifier F is defined by a tuple:
F=<N, I>
[0109] where
[0110] N=the name of the facet and
[0111] I=the interpretation of the facet
[0112] We will often refer to a facet specifier as simply a facet and let context ensure the sense of use. A facet name is a simple string (actually an interned symbol).
[0113] Def: Facet Interpretation. A facet interpretation I is defined by a tuple:
I=<R, P>
[0114] where
[0115] R=The specification of the relation semantics governing the facet
[0116] P=Designates a propagator for the facet. P may be null.
[0117] While a propagator may be null an interpretation can never be null, since a relation specifier can never be null as it must at least provide the basic set theoretic properties of the relation.
[0118] Def: Relation Specifier. A relation specifier R is a tuple which describes the relation of the facet in terms of its set theoretic character and the local semantic constraints imposed on concepts connected to each other through the facet.
R=<C, SC>
[0119] where
[0120] C=the set theoretic character of R. This is a list selected from the following set of properties (note that other properties can be deduced from this, such as equivalence relation, partial order, etc.):
[0121] reflexive, xRx
[0122] nonreflexive
[0123] symmetric, xRyyRx
[0124] nonsymmetric
[0125] transitive, xRy and yRzxRz
[0126] nontransitive
[0127] trichotomy, for any z and y, exactly one of x=y, xRy, yRx holds
[0128] nontrichotomy
[0129] SC=the semantic constraints of R governing the structure of the graph represented by the facet. These are given by a semantic constraint specifier.
[0130] Def: A semantic constraint specifier: A semantic constraint specifier for a relation R of a facet F in a model type MT is a set of sentences .GAMMA. which determines when two concepts in a model M of type MT can be connected along F and how that relationship is mapped relative to possibly connected models of other model types and to instances in the world. That is, .GAMMA. supply necessary conditions on R (and thus F): c.sub.1Rc.sub.2.GAMMA., .GAMMA. can be null and we adopt the convention that anything implies the null set: c.sub.1Rc.sub.2.O slashed. for all possible c.sub.1, c.sub.2, and R.
[0131] Each sentence .phi..epsilon..GAMMA. is a statement with free variables over the concepts in the model M, possibly free variables over the concepts in a related model FM of some model type FMT, and possibly free variables over the instances in the world. These variables are implicitly bound to the specific values of their corresponding sets provided by the context of each specific constraint action. In addition any global predicates and operators defined for all model types can be used as can R and any R.sub.FM associated with the related FMT. There may be several such related model types involved in a semantic constraint. Such related models and their model types are often referred to as "feature models" and "feature model types" and the concepts in them as "features", though this terminology is a bit misleading (they do not have to be related via a "feature" facet--any facet may have such relationships, but for historical reasons we often use this terminology).
[0132] Both universal and existential quantification are available for binding variables ranging over explicitly specified sets. Quantifiers can be mixed and nested to any level. Deeper sentences may refer to the quantifier variables of outer sentences with the expectation that any binding is properly maintained.
[0133] Additionally a constraint may assert a condition to hold provided another condition holds. This supports actions which must be atomic with respect to the overall constraint. For example, if a concept C.sub.1 is added to the concept C.sub.2 in the subclasses facet of a model with a taxonomic model type having facets subclasses and superclasses, then the constraint for the facet can assert the dual relationship: C.sub.2 added to C.sub.1 in the superclasses facet.
[0134] Language for Semantic Constraints
[0135] Letting M be a model of model type MT and FM be a model of model type FMT and F be a facet defined in MT, then the following lexical elements are available for use in semantic constraint specifiers:
[0136] c.sub.i, i=1, . . . , k, . . . =the set of natural numbers. The free variables available for concepts in M
[0137] f.sub.j, j=1, . . . , k, . . . =the set of natural numbers. The free variables available for concepts in FM
[0138] x.sub.i y.sub.j, i and j=1, . . . , k, . . . =the set of natural numbers. The free variables available for instances.
[0139] R=the relation of facet F to which the semantic constraint belongs
[0140] simple-name, the name of a facet of the model type MT
[0141] (simple-name simple-name), the designator for a facet in model type FMT, where the first name is that of FMT and the second is the name of the facet.
[0142] The following set of quantifiers (listed with their semantic interpretation):
[0143] for-every, universal quantification: for-every var set forms, where var is a free variable in the sentences of forms and set is the universe set for this quantifier instance. Forms is a set of sentences which may contain var, and if so it must be a free variable. Yields true if all sentences informs are true for every binding of var from set.
[0144] there-exists, existential quantification: there-exists var set forms, where var is a free variable in the sentences of forms and set is the universe set for this quantifier instance. Forms is a set of sentences which may contain var, and if so it must be a free variable. Yields true if there is at least one binding of var from set which makes all sentences in forms true.
[0145] The following set of connectors (listed with their semantic interpretation):
[0146] =>, logical implication
[0147] not, logical negation
[0148] and, logical conjunction
[0149] or, logical disjunction
[0150] <, numerical less than
[0151] >, numerical greater than
[0152] =, equality (across all types)
[0153] (, start enclosing s-expr
[0154] ), close enclosing s-expr
[0155] The names of all predefined operators on models of all types. This set is subject to continual change, but has at least the following (listed with their semantic interpretation):
[0156] card, cardinality
[0157] inst, instances of
[0158] attr, binary operator, takes a concept and attribute name and returns attribute value
[0159] prop, binary operator, takes a node (concept or instance) and property name and returns the value of the property. Note this includes the standard property of referent. A referent is the connection information for a resource (file, url, model, spreadsheet, accounting system, etc.)
[0160] in, set membership
[0161] intersect, set membership intersection
[0162] union, set membership union
[0163] set-diff, set membership difference
[0164] subset, set inclusion
[0165] deg, takes a concept and facet name and returns the degree of a concept (vertex) in a facet graph
[0166] The syntax for sentences is standard s-expression forms, where any quantifier, operator, relation, and connector may define a clause. Additionally, since all of .GAMMA. are implied by a constraint, .GAMMA. can be represented as a single conjunctive expression (there is no need for an explicit set of sentences).
[0167] Ex-1 Constraint: Suppose MT is a model type with a facet specifier F containing the following constraint:
1
(=> (R c1 c2) (or ((> (card (attr c2
features)) (card (attr c2 features)) (there-exists f1
(attr n1 features) (there-exists f2 (attr n2 features) (FM Rfm) f1 f2)))))
[0168] Then for any two concepts c.sub.1 and c.sub.2 of a model M of type MT if c.sub.1Rc.sub.2 then either there is a related model of type FM with facet (and relation) R.sub.fm for which there are features f.sub.1
and f.sub.2. in the features of c.sub.1 and c.sub.2 respectively, for which f.sub.1R.sub.fmf.sub.2 in the related model or the cardinality of the feature set of c.sub.2 is larger than the cardinality of the feature set of c.sub.1
[0169] The second disjunct of the or-clause in this example illustrates a particularly interesting constraint between models of two model types. It induces a homomorphism between two such models with respect to the graphs of the two facets involved. Hence, this sort of constraint ensures that sets of models are constructed to ensure such homomorphisms and this can be relied upon by agents or other processing of the models involved. One obvious use of this is the standard technique of exploring and investigating questions concerning one structure by looking at one or more of its homomorphic images. In such a technique, the issues would typically already have been resolved for the images or the images would be significantly simpler to explore. This can be particularly useful in agent autoclassifying and configuration scenarios.
[0170] Ex-2, Facet Specifier: If MT is a model type for a simple graph without edge constraints (a so called "weak" semantic model type) then the following simple facet specifier could capture the edge set of models of type MT:
<adjacent-vertices, <<(symmetric), nil>, nil>>
[0171] The facet's name is adjacent-vertices, its interpretation specifies no propagators and inside the relation specifier of the interpretation, no semantic constraints are given and the relation's properties are the singleton symmetric (standard character for simple graphs).
[0172] Ex-3, Facet Specifier: If MT is a simple taxonomic model type then the following facet specifiers could capture simple notions of subclasses and features (given in s-expr clause form):
2
(subclasses (((transitive nonreflexive nonsymmetric) (> (card (attr ?c2 features)) (card (attr ?c2
features)))) nil)) (features (((nontransitive nonreflexive nonsymmetric) nil) inherit-features))
[0173] The facets are subclasses and features. The subclasses facet has a simple constraint requiring the addition of some new feature(s) for a subclass to be legal and a null propagator. The features facet has a null constraint but designates a propagator.
[0174] The purpose of the propagator on features is to ensure that features of concepts of models of type MT obey the expected standard class based inheritance behavior for concept features (or characteristics).
[0175] Propagators and Propagation Specifiers
[0176] As noted earlier, a propagator provides a degree of expected behavior for all models of the model type containing the propagator's specification. Propagation specifiers define what and how values of attributes of models are moved, i.e., propagated, between concepts--both within the model and between concepts in related models.
[0177] Def: Propagation Specifier. A propagation specifier PS is a tuple which describes an expected intrinsic piece of behavior for information movement between selected attributes along a path in a given facet graph for any model whose model type contains the specifier.
PS=<N, A.sub.i, A.sub.j, D, O, W, F>
[0178] where
[0179] N=the name of the propagator (a simple-name)
[0180] A.sub.i=the attribute in the model type whose value is to be propagated, the from attribute
[0181] A.sub.j=the attribute in the model type to which the value is to be propagated, the to attribute
[0182] D=the direction of propagation as given by:
[0183] >, A.sub.i.fwdarw.A.sub.j
[0184] <, A.sub.j.fwdarw.A.sub.i
[0185] O=the form :on <condition>, where condition is one of
[0186] access, propagate when the from attribute of a concept is accessed
[0187] update, propagate when the from attribute of a concept is updated
[0188] change, propagate when the from attribute of a concept is changed (not just updated, but changed)
[0189] W=the form :when s-expression. propagate only when expression is true
[0190] F=the form :along <facet>, where facet denotes a facet that exists in the model type.
[0191] While A.sub.i and A.sub.j may be different attributes, the most typical case is where they are the same attribute. The along facet F controls whether propagation is one step or continues until all concepts along the facet from the starting concept have been visited. If the relation of F is transitive, then propagation continues for all concepts in the potential path, otherwise propagation stops after the first step.
[0192] In many cases, the global propagation semantics provided by propagation specifiers may need to be supplemented with context specific aspects. This is accommodated by providing two predefined properties for concepts in models of any type. These are,
[0193] pre-propagation-actions: A set of ordered pairs of names and functions:
[0194] {<N, f>.vertline.N names PS and f an operator for the space of A.sub.i of PS}
[0195] post-propagation-actions: A set of ordered pairs of names and functions:
[0196] {<N, f>.vertline.N names PS and f an operator for the space of A.sub.j of PS}
[0197] On a propagation event, if the propagator involved has a prepropagation action, then the corresponding function is called on the value of the from attribute before propagation; if the propagator has a postpropagation action, then the corresponding function is called on the updated value of the to attribute.
[0198] Ex-4: Suppose MT is a typical taxonomic model type including the facets superclasses and features, with the facet interpretation for features designating the following propagation specifier (also included in MT's definition): We also presume the typical case that the relation of superclasses is transitive.
[0199] (inherit-features features features > :on access :along superclasses)
[0200] Then any access designating features on a concept c in a model of type MT will obtain all the features directly attributed to c and any features in any superclass of c, i.e., the result is standard class based inheritance.
[0201] Ex-5, Propagation Specifier: Suppose MT is as above in example 1. All model types have the predefined attribute instances (and a simple predefined facet specifier for this) and so MT has this. Assume the typical further condition that MT includes a facet specifier subclasses whose relation is also transitive and that instances' interpretation designates the following propagation specifier (also included in MT):
[0202] (instances-of instances instances > :on access :along subclasses)
[0203] Then any access designating instances on a concept c in a model of type MT will obtain all the instances directly connected to c and any instances of any subclass of c, i.e., the result is standard class based instance set covering.
[0204] Ex-6, Propagation Specifier: Suppose MT is some example of a causal network model type. Let MT have facets causes, effects, and happened and assume that the facet interpretation of happened designates the following propagation specifier which is also defined in MT:
[0205] (happened happened happened > :on change :when (>0) :along effects)
[0206] If effects are transitive (each effect is a cause for something else), then any change to happened at a concept c in a model of type MT, where the value is greater than zero, will "fire" all the causes along the causal chains connected to c whose values are not the same as the value supplied.
[0207] Model Type Definition
[0208] We are now in a position to give the definition of a model type. A model type definition requires the following basic set of information:
[0209] A set of attributes for its models. This includes both predefined attributes for all model types and those specific to the requirements of the style of modeling being captured in the model type.
[0210] A set of facets defined over the attributes for specifying the semantics of how concepts in its models may be connected in order to capture the intended semantics of the facet graphs in the style modeling being captured
[0211] A set of propagators that provide the expected base behavior in its models for the style of modeling captured by the model type.
[0212] In order to satisfy any intermodel connection constraints in the semantics of the facets, there need to exist the set of model types referenced in the constraints along with any of their facets that are designated.
[0213] The last point can be made implicit. Any currently extent model types are available for use in semantic constraint specifications of facet specifiers of the model type. We assume this scenario.
[0214] Def: Model Type: A model type MT is defined by a tuple of sets which collectively describe the complete semantics of the model type:
MT=<{A.sub.i}, {P.sub.j}, {F.sub.k}>
[0215] where
[0216] A.sub.i=The attributes that are specific to the requirements of the modeling style being captured. These are simply a list of simple names. The following may be (this is an implementation's choice) predefined attributes which are always included (whether explicitly specified or not):
[0217] Instances, ties concepts to the world as examples of the concepts
[0218] Exhibitors, ties concepts to the world as characters of the concepts
[0219] P.sub.j=The propagation specifiers that capture the intrinsic behavior expected of the style of modeling being captured by the model type.
[0220] F.sub.k=The facet specifiers that tie together the attributes and propagators along with the relational character and semantic constraints for the set of graphs required to support the modeling style.
[0221] The P.sub.j and F.sub.k are specified per the definitions and descriptions covered in the relevant sections given earlier. This completes the definition of model type and this definition allows the description of the various styles of models mentioned earlier. We give some examples here to illustrate the technique for capturing a style with the machinery. All the examples are given in s-expression clausal form.
[0222] Ex-7, Model Type:One of the simplest model types possible is that of the basic simple graph from graph theory. The following model type definition provides this most basic structure:
3
(simple-graph nil ;; Specific attributes nil ;; Propagator is null => no expected behavior ;; Now the facet specifier set ;; ((adjacent-vertices ;; "concepts" here are simply vertices (((symmetric) nil) ;; Simple graph def has symmetric ;; relation & no constraints nil))) ;; And no propagators . . .
[0223] While not explicitly specified, there are also attributes and facets for instances and exhibitors (as required by the definition of model type). Note that there is also no need to specify attributes which have explicit facet specifiers, as these imply the corresponding attributes.
[0224] Ex-8, Model Type: A somewhat more interesting example is that of a very simple taxonomy. This style of model is the one where there are no "features" and subclassing proceeds essentially by fiat.
4
(simple-taxonomy nil ;; No attributes without explicit facet specifiers ;; Propagators ;; ((instances-of instances instances > :on access :along subclasses)) ;; Now the facets ;; ((is-a (((transitive reflexive nonsymmetric) nil) nil) (subclasses ((transitive nonreflexive nonsymmetric) nil) nil) (instances ((nontransitive nonreflexive nonsymmetric) nil) instances-of)))
[0225] Note that if we had decided to use a "superclasses" facet instead (or perhaps in addition to) the is-a facet the relational character would be slightly different: it would specify nonreflexive: a rose is a rose is a rose, but a rose is not a superclass of itself.
[0226] As pointed out in the example introduction, there are no semantic constraints on any of the facets so what is or isn't a legal subclass or instance is left completely up to the modeler when building a specific instance of simple-taxonomy.
[0227] Lastly note the propagator. It is the same as that given in Ex-5
and it is designated by facet instances. This means that whenever the facet instances is accessed at a concept c, the propagator instances-of will run in order to obtain the correct value for instances at c. When the propagator runs it will first get the instances directly attached to c, then it will move along the subclasses facet to all the immediate subclasses of c and get the instances of each of these. Since subclasses's relation is defined to be transitive, the propagator will then recurse.
[0228] We could augment simple-taxonomy a little to get automatic "what am I" kind of semantics (the standard "is-a" game). We would do this by adding a propagator:
[0229] (i-am-a is-a is-a > :on access :along is-a)
[0230] and designating it as the propagator for the is-a facet:
5
((is-a (((transitive reflexive nonsymmetric) nil) i-am-a)
[0231] Since is-a is transitive, on access to the is-a facet of a concept c, the result will be c and all concepts along the entire chains of is-a rooted at c.
[0232] Global Structural Semantics
[0233] It is worth noting that our definition of model type is silent concerning certain global semantic properties of possible models of the types that may be constructed. It is, however, not always silent as there are cases where it may be deduced that a given model type provides sufficient conditions for that property and thus all models of the type will have the property. However, this is the unusual case. The canonical example is that of all model to world (and vice-versa) facets which are bipartite. A more specific example comes from our definition of simple-taxonomy, which states that subclasses is transitive and nonreflexive and thus we know that (the graph of) subclasses is acyclic.
[0234] Typically, however, the above machinery is silent for individual models with regard to the following important global properties of facets:
[0235] Whether a facet is acyclic
[0236] Whether a facet is connected
[0237] Whether a facet is eulerian
[0238] Whether a facet is hamiltonian
[0239] Whether a facet is bipartite
[0240] Whether a facet is planar
[0241] Whether the graph resulting from the combination of two or more facet graphs has any of the above properties.
[0242] In large measure, these issues must be answered by looking at the particular graphs in question and running an analysis on them. In many cases such analyses will be unable to answer the question. This can be due to the fact that the graph does not exhibit known necessary or sufficient conditions for the property in question or that the required computational complexity to determine such conditions exceeds "allowable limits". In any event, various intrinsic predicates and path finders are provided for such analyses for all model types Notice that this is possible since the scope of the styles of models captured by model types is constrained by the requirement that they are all a graph in their most fundamental character.
[0243] Intrinsics for Model Walks
[0244] The set of supplied intrinsic model walkers and predicates should behave in a functional manner. This is not an issue with respect to predicates as they simply take a model and proceed to (attempt to) determine whether the graph in question is of the sort specified by the predicate. There are two possible sorts of output from such predicates:
[0245] Often, the determination of a property will involve finding a particular kind of path through the graph. In this case, it makes the most sense to return as the value the actual path found: the ordered list of nodes defining the path. Note, we allow for the case where both instances and concepts may be mixed in such a path.
[0246] Certain other properties should simply return nil or t.
[0247] The Available Intrinsics
[0248] The set of model intrinsics is subject to continual update, but includes at least the following set of capabilities:
[0249] Transitive closure walks: generalized facet "applier"
[0250] Model traversals and search forms. These may be invoked with various predicate or predicate sets to determin when to termininate and whether and what to collect and return:
[0251] Along a facet
[0252] Along a sequence of facets
[0253] Randomly (nondeterminancy)
[0254] If tree
[0255] depth first with pre-, in-, and post-order application
[0256] breadth first with pre-, in-, and post-order application
[0257] Search
[0258] depth first
[0259] breadth first
[0260] best first
[0261] beam search
[0262] hill climbing
[0263] A* search
[0264] Special graph predicates
[0265] Eurlerian
[0266] Hamiltonian
[0267] Bipartite
[0268] Connected
[0269] Details of Agents and Events: FIGS. 13-17
[0270] Agents and Events
[0271] Ariadne agents reflect the typical set of required properties for agents such as autonomy, mobility, reactiveness (sometimes called "responsive"), proactiveness, and social ability. These all have explicit constructs in the agent language to allow for direct and simple descriptions involving such characters. However, constructs for such more controversial agent aspects as the "Belief, Desire, and Intent" (BDI) model have been deemed too vague and problematic. In addition these latter are more concerned with large scale agents with internalized "symbolic models" of their world. Ariadne explicitly parts company with such traditional AI techniques.
[0272] Agents are specified by means of constructs arising out of a family of interrelated languages that all "play together". Model types in Ariadne provide various specialized "formats" to organize and structure information. As such, agent descriptions for moving within and among the models built upon these should have access to the more specific and higher level semantics that the model types present. Additionally models in an Ariadne application present semantic interpretations (perspectives) of the various subjects on which the application is focused. Collections of such interrelated models provide the contextual, or "domain level" semantics of the application. Again, descriptions of agents for processing these structures should have constructs which more directly reflect this level of semantic for the subject.
[0273] Hence users should have access to corresponding families of agent (mini) languages. In order to satisfy these highly desirable qualities and keep the results consistent and manageable, these "mini" languages are in turn part of a family of extensible languages layered on top of a more general base language. The base language has a fuller but "lower level semantic" capacity for agent descriptions and at any point a (power) user can dip down into it from a higher level child language to access this capacity.
[0274] Such layered languages creating families of inter-related languages can be built by various means, but the most straightforward method would be to define a very simple consistent syntax with a macro style compile time constructor. All new constructions are defined by means of this constructor and each construction itself becomes a new construct in a language layer. Hence, the constructor always has access to all previous constructions when defining a new construct. At compile time of a set of constructions for an agent's definition, each construct is first expanded according to its definition into the lower level constructs upon which it is based. The process recurses. The recursion stops when the base level constructs are all that is left. The resulting base level version of the original code is then compiled into machine code.
[0275] The design and implementation of such an extensible base language for agent descriptions needs to take into account these notable points:
[0276] Ariadne agents are largely reactive in nature, in the sense that they do not have internalized semantic structures reflecting a model or their external environment.
[0277] Nevertheless, agents will have access to and will directly utilize the conceptual information of many models to synthesize their results.
[0278] Despite their largely reactive nature, Ariadne agents have various degrees of proactive behavior. This allows users to create agents with a goal(s) which can run periodically in the background over a set of models.
[0279] Ariadne agents are independent--they each have their own thread of execution. Hence, the base language will account for such parallelization.
[0280] Agents can be highly communicative (requesting services and replying to such requests). The base language provides an event based messaging service for the expression of all such communication.
[0281] Agent construction and definition takes place within the context of the overall Ariadne system and makes use of three basic models:
[0282] An event model for Ariadne. Event model 1301, shown in FIG. 13, includes events received from the core level infrastructure, agent actions, Star interface, Calyx interface, any conforming GUI, and ERIS brokered external resources.
[0283] An actions model, shown at 1401 in FIG. 14. This model includes the various sorts of active processes that can be invoked via events. One of the subclasses of this model is agent 1405; among the instances of this concept is the refine agent discussed earlier; the instance is at 1407.
[0284] An operations model 1501 (FIG. 15) for describing the various parameter lists of actions and in particular the signatures of agents.
[0285] In addition, as we have seen, all models that have agents have the standard actions facet that relates agents to concepts. All of these standard models are user extensible and manipulatable like any other model. The ability of users to access and change the models provides a very high degree of flexibility to users in changing and contexualizing the processing model of Ariadne to their specific needs.
[0286] Details of Event Model 1301
[0287] Continuing in more detail with event model 1301, that model is used to indicate the event classes with which an agent may be registered. If an agent has been registered with an event class, the agent will be invoked and run whenever an event of the class occurs in a context where the agent is available. Each agent invocation creates an activation copy of the agent which runs as a separate thread. There can be any number of agents (modulo system resources) running at any given time. The event class for an agent indicates what sorts of events that the agent should respond to when it is configured for use. An agent is available in a given context if either:
[0288] 1. It has been connected along the actions facet of a given concept (FIG. 16) or
[0289] 2. It is in the extent of some concept "A" connected along the subclasses facet to the "Agent" concept in the actions model and further that there is an instance I connected to "A" in the caller's facet (FIG. 14) or
[0290] 3. It is manually selected and dropped onto a set of objects. This last option manually invokes an agent on a model. (FIG. 17)
[0291] Continuing in more detail with the above three options, FIG. 16
shows a fabric model 1601 which belongs to the taxonomy class. The actions facet of model 1601 has two agents attached to the root concept fabric, as shown at 1605. The two agents respond to the events of adding a concept to the model and adding an instance to the model. Since they are attached to the root of model 1601 and by the rules of the taxonomy class are inherited by all of the concepts of the model, one or the other of the agents runs whenever a concept is added to the model and whenever an instance is added to the model.
[0292] The predefined actions facet has several constraints on it which prevent various possible misconfigurations. Again all of this setup, configuration and enforcement is done via standard Ariadne model and model type definitions and manipulations. The actions facet (like any other) may also have a variety of propagation behaviors for any given model type. For example, in a typical taxonomy it may well be the case that the Actions facet will be inherited down the subclasses facet, as described above for the agents 1605. This basically gives all the capabilities of standard object oriented method inheritance, but is far more flexible and is also end user configurable. Many other scenarios are possible.
[0293] FIG. 17 shows how the user interface permits a user to manually invoke an agent. In interface 1701, the instances representing the agents are listed at 1705. The user has selected one of them, named FIND-BLANKS. When invoked with two taxonomy models, this agent finds concepts of the one model that have no instances which belong to a given concept of the other model. The instances representing the models are listed at 1703; the user has selected two models, gender and clothing. Ariadne will respond to this input by invoking the selected agent on the two models. The effect of invoking the agent is the following: for any combination of a clothing and a gender concept for which there are no instances, Ariadne will display both the clothing concept and the gender concept.
[0294] In addition to the models, there is a subsystem for event handling which fully supports asynchronous event processing, including posting, dispatching and handler threads for each top level event class (concept in the events model). Part of this subsystem is an event activation layer for the core level capabilities. This layer supports various core level actions (adding and deleting objects, adding and deleting neighbors along facets, agent invocation, etc.) with transparent posting of associated events. Each event consists of the event's event class in event model 1301, a universal identifier for the particular event, and an argument list. The latter, together with the event class, serve as a "signature" to determine what code is executed for the event. A component which generates the event posts it to a main event queue. Each of the GUI, Agent, Core, and IR classes of events has its own event queue and a main event dispatcher reads the events in the main queue and places each event in the proper queue for its class. The queues are read by an event dispatcher for each class. Each of the class event dispatchers runs in its own thread and dispatches each event in turn as it reads it from the class queue. The event dispatcher for the core class further runs in its own separate task. This split between the core event dispatcher and the other class event dispatchers supports clustering of event actions and increases flexibility and performance of core actions.
[0295] An extensive palette of out-of-the-box agent "prototypes" is provided for intermediate level users--those not expected to write agent level code. These prototypes can be completed by configuring various properties and registering them with a set of events. Both of these actions are performed by the standard model manipulation capabilities of Ariadne and the GUI: most typically selection, bin element addition, and copy a set of objects and paste along a facet of other objects. This will result in an agent definition instance (in the world) which can then be attached (along the actions facet) to any concept in any model of a model type, where the type is one that is included in the agent's model type list.
[0296] Typically the actions facet for a model type will have a propagator (though none of these are implicitly provided--the model type definer must decide to make one for the specific case). For example FIG. 16 shows a fabric model 1601 used in an Ecatalog application dealing with clothing. The model 1601 belongs to the model type Simple Taxonomy which is a kind of taxonomy. It has facets is-a (not displayed), subclasses 1611, actions 1607, and a propagator for facet actions:
6
(inherit-actions actions actions > :on access :along is-a) (actions (((nontransitive nonreflexive nonsymmetric) nil) inherit-actions)) ; Actions are inherited from parents
[0297] This propagator causes actions to be inherited by subclass concepts from their parents (in the direct analogue to OO class based inheritance of "methods"). For example, agent refine-content-on-clas-add 1605 is connected in facet actions 1607 to root concept Fabric 1613 and thus it will be available to all concepts throughout the underlying tree. This would be equally true if it were attached to any concept C in the subclasses facet: 1605 would be available to the subclasses under C.
[0298] Continuing further, let agent 1605 be registered with the event class "Neighbor addition" 1207 of FIG. 12. This indicates that the events that 1605 should watch for in any model where it is attached along the actions facet, are those where some concept (or instance) is being added to one of the existing concepts in the model. For example, if the new concept Chamois is added to the Cotton concept 1609 along subclasses facet 1611, this will generate a "Neighbor addition" event. Agent 1605
would then become active (in its own thread) and perform its actions based on the context of the event: the model where the event happened (model 1601), the concept being added to (Cotton concept 1609), the neighbors being added (new concept Chamois), and the facet involved (subclasses facet 1611).
[0299] Agent 1605 would then reclassify any chamois fabric instances attached to the originating concept (Cotton concept 1609) down into the more specific new Chamois concept. Note how this uses the context specific information of working with only the instances that are known to be only cotton (not some other existing specialization of cotton or all instances in the world).
[0300] As an example of an agents code we present here the definition for agent 1605:
7
(defagent refine-content-on-class-add ((owner node facet neighbors) "Neighbor addition" "Simple Taxonomy") (when (is= facet (the-facet "Subclasses")) (with ((new-concept (element 0 :of neighbors)) (insts (all x :in (the-direct-instances-of node) :suchthat (matches new-concept (the-description- of x))))) (the-name-of x) :case-fold t) (move insts :from node :to new-concept)))
[0301] Innovations and Benefits of the Ariadne System
[0302] Further experience with the Ariadne system disclosed in the parent of the present patent application has led to an improved understanding of the notions that underlie the system and of the benefits of basing a software composition system on those notions.
[0303] Underlying Notions
[0304] The notions underlying the Ariadne system can be summarized as follows:
[0305] 1. The use of multiple models of various styles (part-whole, grouping, taxonomy, associative, causal, etc.) to capture various aspects of a set of items or body of information. Each of these aspects represents an abstraction one might use when describing or analyzing the set of items of body of information. The result is an open ended, dynamically and independently extensible, interacting collection of varied representations that capture various sets of "knowledge" about the subject.
[0306] 2. The realization that these various ways of abstracting an aspect (categorizing, associating, cause-effect, etc.) of something can in turn be represented as sets of semantically enhanced overlaid graphs which all use the same vertex set. Each such graph captures a facet of the abstraction involved and the total collection is able to capture all the elements of the style of abstraction involved.
[0307] 3. The determination that a formal collection of facet definitions (or specifications) can be used to define a style of abstraction and thereby provide a model type for many models expressing this style. So, a model type can (without being unduly misleading) be thought of as a "generator" of multiple models (of the same style, which style is captured by the model type definition).
[0308] 4. The use of context sensitive declarative constraint annotations to provide and enforce the intended semantics of any given edge set for any given facet. The constraint annotations implicitly fire on associated events. This implies that the relation provided by the facet (by its edge set) actually means something. It is not just a set of connections between vertices made by fiat. Further, these constraints can (and typically should) make use of the semantics embodied in the facets of related models.
[0309] 5. The recognition that most styles of modeling presume an expected set of implicit behavior for any given example of the modeling. In a system of causes and effects, there is an expectation that when a cause is asserted the effects will be asserted. In a taxonomic sort of categorization, we expect the features of a more general category G to be "inherited by" or propagated to specializations of G; that the objects categorized as examples of some set of specializations of G are also subsumed under G.
[0310] 6. The determination that such behavior is actually the propagation of various subsets of the vertex set of one or more facets along subsets of the edge/arc set of other facets, and that such propagation can be made explicit by users and implicitly invoked.
[0311] 7. The observation, taken from model theory (the subbranch of mathematical logic explicitly dealing with the semantics of formal systems), that a "body of information" is a collection of things and any abstraction of them is a wholly separate and independent thing. Further, that what the collection of things is considered to be, how they will be used, etc. is in turn wholly determined by some set of these abstractions.
[0312] 8. The determination that this explicit separation of "abstractions and the world of things" can be formally captured by the same facet mechanism used to represent models. Further, these facets produce bipartite graphs with concepts in abstractions and objects in the world of things forming the partition of vertices.
[0313] 9. The recognition that much of the semantics of a program can be captured in explicit models instead of being woven throughout various pieces of code (as is the case in all traditional programming). In particular the contextual and domain semantics of an application can be captured by such explicit models and then utilized as the basis of various applications.
[0314] 10. The further recognition that autonomous agents guided by and roving over and among these structures can supply the behavioral aspects of programs. Hence a user can program a system (or modify the semantics of one and thus its characteristics and behavior) by changing the domain information captured in its set of domain and context models and/or placing different agents on these models.
[0315] Benefits of a System that Implements the Notions
[0316] The benefits of a system that implements the above notions include the following:
[0317] 1. Explicit and direct representation of the relevant information for the semantics of the various aspects of an application. This comes from the explicit support of multiple styles of modeling and their direct representation in model types and the ability to compose together various models via intermodel connections. For example, in a Web Ecommerce application for a hardware store, a hierarchical classification of its inventory (in which pipe fittings are further categorized in terms of water pipes or gaspipes, or where bathroom cabinets are a particular type of cabinet) will need to interoperate with a compatibility model (in which English vs. metric measurements or decorating styles for external fixtures, etc. are captured). In this setting there are two kinds of abstraction: hierarchical classification, and consistency constrain