Knowledge compilation is a technique for dealing with computational in-tractability of propositional reasoning. Transforming a given knowledge base into a special normal form ([MR03,DH05]) is aiming at efficient rea-soning. We propose to apply this technique to knowledge bases defined

We show how to interoperate, semantically and inferentially, between the leading Semantic Web approaches to rules (RuleML Logic Programs) and ontologies (OWL/DAML+OIL Description Logic) via analyzing their expressive intersection. To do so, we define a new intermediate knowledge representation (KR

We propose a new family of Description Logics (DLs), called DL-Lite, specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. Reasoning here means not only computing subsumption between concepts, and checking satisfiability of the whole knowledge base

This paper describes the language Lustre, which is a dataflow synchronous language, designed for programming reactive systems --- such as automatic control and monitoring systems --- as well as for describing hardware. The dataflow aspect of Lustre makes it very close to usual description tools

are dynamically routed among the many parallel pro-cessing units with the help of compiler-generated masks. Memory accesses may occur speculatively without knowledge of preceding loads or stores. Addresses are disambiguated dynamically, many in parallel, and processing waits only for true data dependence

, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summa-rized, and their actual realization is discussed

. The study of decisions addresses both normative and descriptive questions. The normative analysis is concerned with the nature of rationality and the logic of decision making. The descriptive analysis, in contrast, is concerned with people's beliefs and preferences as they are, not as they should be

. Description Logics (DLs) are a family of knowledge representation formalisms mainly characterised by constructors to build complex concepts and roles from atomic ones. Expressive role constructors are important in many applications, but can be computationally problematical. We present

too restrictive for practical applications, whereas the latter leads to uncertainty about exactly what can and cannot be inferred from the knowledge base. We present a third alternative, in which knowledge given in a general representation language is translated (compiled) into a tractable form

Description logics are a family of knowledge representation formalisms that are descended from semantic networks and frames via the system Kl-one. During the last decade, it has been shown that the important reasoning problems (like subsumption and satisfiability) in a great variety