Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

Embedding defaults into terminological knowledge representation formalisms

Abstract: We consider the problem of integrating Reiter's default logic into terminological representation systems. It turns out that such an integration is less straightforward than we expected, considering the fact that the terminological language is a decidable sublanguage of first-order logic. Semantically, one has the unpleasant effect that the consequences of a terminological default theory may be rather unintuitive, and may even vary with the syntactic structure of equivalent concept expressions. This is due to the unsatisfactory treatment of open defaults via Skolemization in Reiter's semantics. On the algorithmic side, we show that this treatment may lead to an undecidable default consequence relation, even though our base language is decidable, and we have only finitely many (open) defaults. Because of these problems, we then consider a restricted semantics for open defaults in our terminological default theories: default rules are applied only to individuals that are explicitly present in the knowledge base. In this semantics it is possible to compute all extensions of a finite terminological default theory, which means that this type of default reasoning is decidable. We describe an algorithm for computing extensions and show how the inference procedures of terminological systems can be modified to give optimal support to this algorithm.

The engineering of knowledge-based systems: theory and practice

Abstract: 1. Introduction to Knowledge-Based Systems. 2. Structure. 3. Logic and Automated Reasoning. 4. Forward Reasoning Rule-Based Systems. 5. Backward-Reasoning Systems. 6. Associative Networks, Frames, and Objects. 7. Blackboard Architectures. 8. Uncertainty Management. 9. Advanced Reasoning Techniques. 10. The Software Lifecycle in Knowledge-based Systems. 11. Feasibility Analysis. 12. Requirements Specification and Design. 13. Knowledge Acquisition and System Implementation. 14. Practical Considerations in Knowledge Acquisition. 15. Alternative Knowledge Acquisition Means. 16. Verification and Validation. 17. Legal Issues in Knowledge-Based Systems. Appendix A: The CLIPS System. Appendix B: The Personal Consultant Shell System.

P-CLASSIC: a tractable probablistic description logic

Abstract: Knowledge representation languages invariably reflect a trade-off between expressivity and tractability. Evidence suggests that the compromise chosen by description logics is a particularly successful one. However, description logiC (as for all vanants of first-order logic) is severely limited in its ability to express uncertainty. In this paper, we present P-CLASSIC, a probabilistic version of the description logiC CLASSIC. In addition to teoninological knowledge, the language utilizes Bayesian networks to express uncertainty about the basic properties of an individual, the number of fillers for its roles, and the properties of these fillers. We provide a semantics for P-CLASSIC and an effective inference procedure for probabilistic subsumption: computing the probability that a random individual in class C is also in class D. The effectiveness of the algorithm relies on independence assumptions and on our ability to execute lifted inference: reasoning about similar individuals as a group rather than as separate ground teons. We show that the complexity of the inference algorithm is the best that can be hoped for in a language that combines description logic with Bayesian networks. In particular, if we restrict to Bayesian networks that support polynomial time inference, the complexity of our inference procedure is also polynomial time.

Conceptual Graph Matching for Semantic Search

Abstract: Semantic search becomes a research hotspot. The combined use of linguistic ontologies and structured semantic matching is one of the promising ways to improve both recall and precision. In this paper, we propose an approach for semantic search by matching conceptual graphs. The detailed definitions of semantic similarities between concepts, relations and conceptual graphs are given. According to these definitions of semantic similarity, we propose our conceptual graph matching algorithm that calculates the semantic similarity. The computation complexity of this algorithm is constrained to be polynomial. A prototype of our approach is currently under development with IBM China Research Lab.

Part-whole relations in object-centered systems: an overview

Abstract: Knowledge bases, data bases and object-oriented systems (referred to in the paper as Object-Centered systems) all rely on attributes as the main construct used to associate properties to objects; among these, a fundamental role is played by the so-called part-whole relation. The representation of such structural information usually requires particular semantics together with specialized inference and update mechanisms, but rarely do current modelling formalisms and methodologies give it a specific, ‘first-class’ dignity. The main thesis of this paper is that the part-whole relation cannot simply be considered as an ordinary attribute: its specific ontological nature requires to be understood and integrated within data-modelling formalisms and methodologies. On the basis of such an ontological perspective, we survey the conceptual modelling issues involving part-whole relations, and the various modelling frameworks provided by knowledge representation and object-oriented formalisms.