Approximate reasoning using terminological models

Term Subsumption Systems (TSS) form a knowledge-representation scheme in AI that can express the defining characteristics of concepts through a formal language that has a well-defined semantics and incorporates a reasoning mechanism that can deduce whether one concept subsumes another. However, TSS's have very limited ability to deal with the issue of uncertainty in knowledge bases. The objective of this research is to address issues in combining approximate reasoning with term subsumption systems. To do this, we have extended an existing AI architecture (CLASP) that is built on the top of a term subsumption system (LOOM). First, the assertional component of LOOM has been extended for asserting and representing uncertain propositions. Second, we have extended the pattern matcher of CLASP for plausible rule-based inferences. Third, an approximate reasoning model has been added to facilitate various kinds of approximate reasoning. And finally, the issue of inconsistency in truth values due to inheritance is addressed using justification of those values. This architecture enhances the reasoning capabilities of expert systems by providing support for reasoning under uncertainty using knowledge captured in TSS. Also, as definitional knowledge is explicit and separate from heuristic knowledge for plausible inferences, the maintainability of expert systems could be improved

Using fuzzy logic to integrate neural networks and knowledge-based systems

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems

Enhanced Search Method for Ontology Classification

The web ontology language (OWL) has become a W3C recommendation to publish and share ontologies on the semantic web. In order to infer implicit information (classification, satisfiability and realization) of OWL ontology, a number of OWL reasoners have been introduced. Ontology classification is to compute a partial ordering or hierarchy of named concepts in the ontology using the subsumption testing. Most of the reasoners use both top-down and bottom-up searches using subsumption testing for ontology classification. As subsumption testing is costly, it is important to ensure that the classification process uses the smallest number of tests. In this paper, we propose an enhanced method of optimizing the ontology classification process of ontology reasoning. Our work focuses on two key aspects: The first and foremost, we describe classical methods for ontology classification. Next, we present description of the enhanced method of optimizing the ontology classification and the detailed algorithm. We evaluate the effect of the enhanced method on four different types of test ontology. The enhanced search method shows 30% performance improvement as compared with the classical method according to the result of the experiment

Combining terminological and rule-based reasoning for abstraction processes

Terminological reasoning systems directly support the abstraction mechanisms generalization and classification. But they do not bother about aggregation and have some problems with reasoning demands such as concrete domains, sequences of finite but unbounded size and derived attributes. The paper demonstrates the relevance of these issues in an analysis of a mechanical engineering application and suggests an integration of a forward-chaining rule system with a terminological logic as a solution to these problems

Queries, rules and definitions as epistemic statements in concept languages

Concept languages have been studied in order to give a formal account of the basic features of frame-based languages. The focus of research in concept languages was initially on the semantical reconstruction of frame-based systems and the computational complexity of reasoning. More recently, attention has been paid to the formalization of other aspects of frame-based languages, such as non-monotonic reasoning and procedural rules, which are necessary in order to bring concept languages closer to implemented systems. In this paper we discuss the above issues in the framework of concept languages enriched with an epistemic operator. In particular, we show that the epistemic operator both introduces novel features in the language, such as sophisticated query formulation and closed world reasoning, and makes it possible to provide a formal account for some aspects of the existing systems, such as rules and definitions, that cannot be characterized in a standard first-order framework

Explainable expert systems: A research program in information processing

Our work in Explainable Expert Systems (EES) had two goals: to extend and enhance the range of explanations that expert systems can offer, and to ease their maintenance and evolution. As suggested in our proposal, these goals are complementary because they place similar demands on the underlying architecture of the expert system: they both require the knowledge contained in a system to be explicitly represented, in a high-level declarative language and in a modular fashion. With these two goals in mind, the Explainable Expert Systems (EES) framework was designed to remedy limitations to explainability and evolvability that stem from related fundamental flaws in the underlying architecture of current expert systems

An empirical analysis of optimization techniques for terminological representation systems : or: \u27Making KRIS get a move on\u27

We consider different methods of optimizing the classification process of terminological representation systems, and evaluate their effect on three different types of test data. Though these techniques can probably be found in many existing systems, until now there has been no coherent description of these techniques and their impact on the performance of a system. One goal of this paper is to make such a description available for future implementors of terminological systems. Building the optimizations that came off best into the KRIS system greatly enhanced its efficiency

Combining terminological and rule-based reasoning for abstraction processes

Terminological reasoning systems directly support the abstraction mechanisms generalization and classification. But they do not bother about aggregation and have some problems with reasoning demands such as concrete domains, sequences of finite but unbounded size and derived attributes. The paper demonstrates the relevance of these issues in an analysis of a mechanical engineering application and suggests an integration of a forward-chaining rule system with a terminological logic as a solution to these problems

Extensible Knowledge Representation: the Case of Description Reasoners

This paper offers an approach to extensible knowledge representation and reasoning for a family of formalisms known as Description Logics. The approach is based on the notion of adding new concept constructors, and includes a heuristic methodology for specifying the desired extensions, as well as a modularized software architecture that supports implementing extensions. The architecture detailed here falls in the normalize-compared paradigm, and supports both intentional reasoning (subsumption) involving concepts, and extensional reasoning involving individuals after incremental updates to the knowledge base. The resulting approach can be used to extend the reasoner with specialized notions that are motivated by specific problems or application areas, such as reasoning about dates, plans, etc. In addition, it provides an opportunity to implement constructors that are not currently yet sufficiently well understood theoretically, but are needed in practice. Also, for constructors that are provably hard to reason with (e.g., ones whose presence would lead to undecidability), it allows the implementation of incomplete reasoners where the incompleteness is tailored to be acceptable for the application at hand