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1 METHOD AND STRUCTURE FOR DOMAIN-INDEPENDENT MODULAR REASONING AND RELATION REPRESENTATION FOR ENTITY-RELATION BASED INFORMATION STRUCTURES

CROSS-REFERENCE TO RELATED APPLICATIONS

The present Application is related to U.S. patent application Ser. No. 10/326,380, filed on Dec. 23, 2002, to Rosario Uceda-Sosa, entitled “METHOD AND STRUCTURE FOR UNSTRUCTURED DOMAIN-INDEPENDENT OBJECTORIENTED INFORMATION MIDDLEWARE”, and to U.S. patent application Ser. No. 10/326,375, filed on Dec. 23, 2002, to Rosario Uceda-Sosa, entitled “METHOD AND STRUCTURE FOR TEMPLATE-BASED DATA RETRIEVAL FOR HYPERGRAPH ENTITY-RELATION INFORMATION STRUCTURE”, both assigned to the present assignee, and both incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to a method of packaging reasoning heuristics so that they can be easily attached to data, thereby effectively creating individual and self-describing knowledge capsules which can be stored or transmitted through a network using standard and widely available software techniques. The technique is particularly advantageous in distributed knowledge architectures, such as multi-agent enviromnents in which knowledge is communicated or transferred between independent parts of the architecture.

2. Description of the Related Art

Even though there has been much study on knowledge representation and reasoning based on the entity-relation paradigm (semantic networks, conceptual graphs, frames), the focus has been on how functionally powerful these systems are, such as what type of infonnation can be stored, and what type of inferencing can be made.

Conventional systems implementing this paradigm have centralized architectures and reasoning heuristics which are available to the system pennanently, thereby causing each system to be isolated from a system having different architecture and reasoning heuristics. Furthermore, reasoning is rule-based, as opposed to navigation-based reasoning.

With the advent of multiagent enviromnents, the focus of knowledge systems has been centered on partial knowledge management, and system-wide heuristics (i.e., what assertions can be made of the system as a whole).

The conventional knowledge systems using this wholesystem approach have not worked well in multiagent enviromnents. Thus, hither to the present invention, there has been no knowledge system providing the capability to construct inferences in a multiagent enviromnent, using data and heuristics independent of any specific domain.

SUMMARY OF THE INVENTION

In view of the foregoing problems, drawbacks, and disadvantages of the conventional systems, a purpose of the present invention is to provide a structure (and method) for representing, storing, and using reasoning heuristics and their underlying binary relations in infonnation systems based on the entity-relation paradigm. The present invention also provides

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a method that can be applied to any infonnation structure based on the entity-relation model, regardless of the knowledge domain.

According to the present invention, a method is provided in which information about relations may be represented in a declarative fashion that allows performing meta-inferencing on the data.

This meta-inferencing technique is preferably independent of the specific relations of the data, so they can be dynamically added/removed from the space.

The present invention also presents a navigation-based reasoning teclmique, in contrast to the conventional rule-based reasoning techniques.

To accomplish the above pmposes and goals, in a first aspect of the present invention, herein is described a method (and structure) for using a reasoning heuristic in a networkbased infonnation system based on an entity/relation paradigm and characterized as being a self-similar hypergraph, including packaging a domain-independent reasoning heuristic so that it is attachable to data in a self-similar-hypergraph information system, thereby allowing self-describing knowledge capsules to be created from the data.

In a second aspect of the present invention, herein is described a method (and apparatus) of navigating through an information system, including establishing a relational algebra defining a set of relations over the infonnation system and retrieving information from the infonnation system by using the relational algebra to provide an automatic inferencing over the infonnation system.

In a third aspect of the present invention, herein is described a network configured to navigate through a network-based infonnation system, including at least one computer having at least one of: a middleware module containing a relational algebra, said relational algebra defining a set of relations over a netvvork-based information system and a middleware module permitting infonnation to be retrieved from the network-based infonnation system by using the relational algebra to provide an automatic ir1ferencing over the network-based infonnation system.

In a fourth aspect of the present invention, herein is described a middleware module for navigating through an information system, including a module that packages domain-independent reasoning heuristics so that they can be attached to data in a self-similar-hypergraph infonnation system, thereby allowing self-describing knowledge capsules to be created from the data and a meta-inferencing to be performed on the data.

In a fifth aspect of the present invention, herein is described a signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perfonn a method of navigating through an information system, including establishing a relational algebra defining a set of relations over the infonnation system and retrieving infonnation from the infonnation system by using the relational algebra to provide an automatic ir1ferencing over the information system.

Thus, the present invention does not address the aforementioned ir1formation system issues by using system-wide heuristics. Rather, given an already existing set of reasoning heuristics, the present invention shows how this knowledge can be packed to be stored or transmitted, using available software techniques, so that meta-inferencing can be performed on the data itself.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects and advantages will be better understood from the following detailed descrip

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tion of a preferred embodiment of the invention with reference to the drawings, in which:

FIG. 1 shows exemplary node configuration 100 for use with a real estate database using the middleware program according to the present invention;

FIG. 2 shows a configuration 200 illustrating how information from a remote real estate database might be packaged to allow a query by a real estate agent;

FIG. 3 provides an upper-level architecture 300 of the present invention;

FIG. 4 shows an example 400 of the partial order of the Reasoning Algebra for Example 2, involving the BuildingBlocks domain;

FIG. 5 is a flowchart of a method 500 to construct a Reasoning Module according to the present invention;

FIG. 6 shows a partial order 600, as discussed in Example 3;

FIG. 7 is an exemplary IRIS architecture 700 according to the present invention;

FIG. 8 illustrates an exemplary IRIS GUI 800 according to the present invention;

FIG. 9 illustrates an exemplary hardware/ir1formation handling system 900 for incorporating the present invention therein; and

FIG. 10 illustrates a signal bearing medium 1000 (e.g., storage medium) for storing steps of a program of a method according to the present invention.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT OF THE INVENTION

Referring now to the drawings, and more particularly to FIG. 1, a preferred embodiment of the present invention will now be described. A method according to the present invention is described for representing, storing, and using reasoning heuristics and their underlying binary relations in information systems based on the entity-relation paradigm. In the entity-relation paradigm, infonnation is represented as entities with relations among them. Examples of infonnation systems using the entity-relation paradigm are semantic networks, frames, or conceptual graphs. A binary relation is a relation that is defined between two nodes of the infonnation system.

This invention packages reasoning heuristics so that they can be easily attached to data, effectively creating an individual and self-describing knowledge capsule, which can be stored or transmitted through a network using standard and widely available software techniques. This is particularly advantageous in distributed knowledge architectures, like multiagent enviromnents, where knowledge is communicated or transferred between independent parts of the architecture.

The present invention has been exemplarily implemented as one of various novel aspects of a middleware program called IRIS (Infonnation Representation Inferencing Sharing). By the incorporation of the present invention, IRIS attains the capability to represent, store, and use reasoning heuristics, thereby providing an inference capability that is domain-independent. It is noted, however, that this exemplary embodiment in the IRIS middleware is not intended as a limitation on the scope of the present invention.

The IRIS middleware module is further described in the two above-listed copending applications and in an article entitled “IRIS: An Intelligent Infonnation Infrastructure For Distributed Knowledge Enviromnents”, published in the 2002 Proceedings of the Intemational Conference on Intelli

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gent Information Technology, pages 1 13-1 19, ISBN 7-1 1575100-5/0267. The contents of this article are also incorporated herein by reference.

A heuristic is a rule that allows a procedure to be carried out. A heuristic, however, differs from an algoritlnn (i.e., a precise recipe to perform a task). In contrast, a heuristic is a rule or set of rules that facilitate a task but may not entirely solve or achieve that task. For example, an algoritlnn would be a complete recipe to bake a cake, including a precise listing of ingredients, mixing conditions, exact oven temperature and predetermined baking time. A possible heuristic for baking a cake would be a rule that the oven temperature should be no higher than X degrees and baking time should be no longer thanY minutes.

FIG. 1 shows a sample node configuration 100 as might be used for a real estate database using the middleware program IRIS described in greater detail in the above-listed copending applications. Every rectangle 101-109 shown in FIG. 1 is an IRIS node, including Description1 101 and Description2 104, which describe subgraphs.

Nodes in IRIS can contain a whole IRIS graph. This aspect, called “self-similarity”, distinguishes the nodes of IRIS from nodes of other conventional systems. This aspect means that any IRIS graph (made up of nodes and nexus) can be described and addressed as a node.

FIG. 2 shows an exemplary ontology 200 that might be used in a repository of real estate data. Here, MLS stands for Multiple Listing Service, a unique identifier of a real estate property. A key advantage of IRIS is that it hides the complexity of data organization from agents, whether human agents such as real estate agents or software-implemented agents. For example, if an agent wants to find ir1formation on houses available in a specific city, without knowing the details of the underlying ontology 200, IRIS can retum a collection of house objects with data from House 201, Parcel 202, Money 203, and Address 204. To do this, IRIS must know what the entity boundaries of House 201 are and what attributes or entities in its neighborhood must be selected.

Perhaps even more demonstrative to the present invention is a scenario in which a real estate agent wants to add new views (e.g., of the properties) which are to be integrated automatically with the data. IRIS must be able to reason about these new views as if they were native to the repository. For example, if an agent’s main concem is the financial data on houses, he or she would be able to use IRIS to add a HouseProperty concept to capture the MLS, Price, and Taxes data by using the same interface of the query just mentioned above. The agent could then simply request instances of HouseProperty located in a specific city such as Scarsdale. To process the query, IRIS must be automatically able to relate HouseProperty to the City concept.

Before continuing with describing the technique of the present invention, it should be apparent that the infonnation capsules in FIGS. 1 and 2 include two fundamental components: data and relations. By incorporating relations into information capsules, IRIS additionally is able to achieve an automatic inferencing capability. How this inferencing capability is accomplished by the present invention is described below.

The present invention has at least two main advantages. First, as previously mentioned, the present invention involves information capsules that package reasoning heuristics, such that the heuristics can be easily attached to data. Therefore, it effectively creates individual and self-describing knowledge capsules, which can be stored or transmitted through a network using standard and widely available software techniques. This is particularly advantageous in distributed