Some Remarks on Ontology and Information Fusion Dr. James Llinas - - PowerPoint PPT Presentation

Some Remarks on Ontology and Information Fusion

• Dr. James Llinas

Research Professor, Director (Emeritus) Center for Multisource Information Fusion University at Buffalo and CUBRC llinas@buffalo.edu

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Roles for Ontologies in IF Processes/Systems

• Reasonably reliable a priori Declarative Knowledge

about some domain

– In the face of domains for which reliable a priori Procedural (dynamic) Knowledge is hard to specify

• “Weak Knowledge” problems
• As such, they provide a framework that connects

Entities and Relationships

– Of fundamental concern for COIN, Ctr-Terrorism, Irregular Warfare re social structures and militarily- significant entity relations

• The basic construct of a “Situation” or a “Threat”

and thus Level 2, 3 Fusion estimation

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Complexities in Distributed and Networked Systems

• In modern Distributed/Networked Systems there are No single

points of authority: These systems are collages of Legacy systems—Joint/Multiservice systems—Coalition systems

• Nodal Ontologies for Fusion/Situational Estimation, and

Communication-support Ontologies for Inter-Nodal Communications/Data-sharing (eg JC3IEDM)

• Harmonizing NLP Operations and Ontologies within and across

such systems

• The issue of Uncertainty in Ontological specification:

– Probabilistic and Non-Probabilistic Ontologies

• Is there an Inescapable need for Semantic Mediation?

– Mediator systems well-studied and developed*

• Eg Gio Wiederhold (June 1, 1993). "Intelligent integration of information". ACM SIGMOD Record 22 (2)

(This was a major DARPA program)

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Semantic Complexity

• Controlling Semantic Proliferation/Complexity:

– Ontologies – Controlled Languages

• Eg Battle Management Language

– Eg Shade, U., et al, From Battle Management Language (BML) to Automatic Information Fusion, Chapter in Information Fusion and Geographic Information Systems, Lecture Notes in Geoinformation and Cartography,Popovich, V.V.; Claramunt, C.; Devogele, Th.; Schrenk, M.; Korolenko, K. (Eds.), 2011, Springer

• Understanding complexity drivers in text
• Eg McDonald, D.D., Partially Saturated Referents as a Source of Complexity

in Semantic Interpretation, Proceedings of NLP Complexity Workshop: Syntactic and Semantic Complexity in Natural Language Processing Systems, 2000

• Measuring Semantic Complexity
• Eg, Pollard, S and Biermann, A.W., A Measure of Semantic Complexity for

Natural Language Systems (2000) Proceedings of NLP Complexity Workshop: Syntactic and Semantic Complexity in Natural Language Processing Systems, 2000

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The Association Problem

• The Ontologically-specified World is controllable—the Real Data

World is not

• While Ontologies can help in Fusion-based estimation and

inferencing problems, the mechanics of exploitation will involve the associability of Real (uncontrolled) data to (controlled) Entities and Relations in the Ontologies

– Semantic similarity, metrics, degree (“hops”), etc – Efficient algorithms—eg Cloud implementations – PhD-level research

• There is also the issue of “Coverage”—in poorly-

understood/known problems, how does one specify an Ontology that has “adequate” coverage?

– Issue of negative information

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Summary

• Ontologies have a useful role in the design and

development of Information Fusion systems

• Questions regarding issues of:

– Authoritative control of semantics in distributed systems

• Acceptable, optimal methods for mediation

– Complexity of semantics

• Understanding, measuring, controlling

– Association of semantic terms and complex, high- dimensional semantic structures

• Seem to require further, continuing study to better

define best ways to employ ontological information in complex, distributed, large-scale Information Fusion systems and applications

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