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.

Reconciling description logics and rules

Abstract: Description logics (DLs) and rules are formalisms that emphasize different aspects of knowledge representation: whereas DLs are focused on specifying and reasoning about conceptual knowledge, rules are focused on nonmonotonic inference. Many applications, however, require features of both DLs and rules. Developing a formalism that integrates DLs and rules would be a natural outcome of a large body of research in knowledge representation and reasoning of the last two decades; however, achieving this goal is very challenging and the approaches proposed thus far have not fully reached it. In this paper, we present a hybrid formalism of MKNFpknowledge bases, which integrates DLs and rules in a coherent semantic framework. Achieving seamless integration is nontrivial, since DLs use an open-world assumption, while the rules are based on a closed-world assumption. We overcome this discrepancy by basing the semantics of our formalism on the logic of minimal knowledge and negation as failure (MKNF) by Lifschitz. We present several algorithms for reasoning with MKNFp knowledge bases, each suitable to different kinds of rules, and establish tight complexity bounds.

The Logic of Frames

Abstract: Publisher Summary Minsky introduced the terminology of frames to unify and denote a loose collection of related ideas on knowledge representation. Frames are sometimes understood at the metaphysical level. One aspect of frame reasoning which is often considered to lie outside of logic is the idea of a default value: a value that is taken to be the slot filler in the absence of explicit information to the contrary. A close analysis of what defaults mean shows that they are intimately connected with the idea of observations: additions of fresh knowledge into a data-base. Their role in inference — the drawing of consequences of assumptions — is readily expressible in logic, but their interaction with observation requires that the role of the state of the system's own knowledge is made explicit. This requires not a new logic but an unusual ontology and some new primitive relations. One needs to be able to talk about the system itself, in its own language, and to involve assumptions about itself in its own processes of reasoning. An emphasis on the analysis of such processes of reflexive reasoning is one of the few positive suggestions which the frames movement has produced.

Map Generalization: Making Rules for Knowledge Representation

Abstract: Part 1 Rule base organization: design considerations for a rule based system conceptual frameworks for geographical knowledge knowledge engineering for generalization. Part 2 Data modelling issues: suitable representation schema for geographic information knowledge classification and organization object modelling and phenomenon-based generalization. Part 3 Formulation of rules: constraints on rule formation rule section for small scale map generalizations a rule for describing feature geometry amplified intelligence and rule based systems. Part 4 Computational and representational issues: role of interpolation in feature displacement parallel software and computation integration and evaluation of map generalization.

Description logic framework for information integration

Abstract: Information Integration is one of the core problems in distributed databases, cooperative information systems, and data warehousing, which are key areas in the software development industry. Two critical factors for the design and maintenance of applications requiring Information Integration are conceptual modeling of the domain, and reasoning support over the conceptual representation. We demonstrate that Knowledge Representation and Reasoning techniques can play an important role for both of these factors, by proposing a Description Logic based framework for Information Integration. We show that the development of successful Information Integration solutions requires not only to resort to very expressive Description Logics, but also to significantly extend them. We present a novel approach to conceptual modeling for Information Integration, which allows for suitably modeling the global concepts of the application, the individual information sources, and the constraints among different sources. Moreover, we devise inference procedures for the fundamental reasoning services, namely relation and concept subsumption, and query containment. Finally, we present a methodological framework for Information Integration, which can be applied in several contexts, and highlights the role of reasoning services within the design process.