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107
Description Logics in Data Management
, 1995
"... Description logics and reasoners, which are descendants of the klone language, have been studied in depth in Artificial Intelligence. After a brief introduction, we survey in this paper their application to the problems of information management, using the framework of an abstract information serve ..."
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Cited by 189 (13 self)
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Description logics and reasoners, which are descendants of the klone language, have been studied in depth in Artificial Intelligence. After a brief introduction, we survey in this paper their application to the problems of information management, using the framework of an abstract information server equipped with several operations  each involving one or more languages. Specifically, we indicate how one can achieve enhanced access to data and knowledge by using descriptions in languages for schema design and integration, queries, answers, updates, rules, and constraints.
Computing Least Common Subsumers in Description Logics with Existential Restrictions
, 1999
"... Computing the least common subsumer (lcs) is an inference task that can be used to support the "bottomup " construction of knowledge bases for KR systems based on description logics. Previous work on how to compute the lcs has concentrated on description logics that allow for univ ..."
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Cited by 121 (30 self)
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Computing the least common subsumer (lcs) is an inference task that can be used to support the &quot;bottomup &quot; construction of knowledge bases for KR systems based on description logics. Previous work on how to compute the lcs has concentrated on description logics that allow for universal value restrictions, but not for existential restrictions. The main new contribution of this paper is the treatment of description logics with existential restrictions. Our approach for computing the lcs is based on an appropriate representation of concept descriptions by certain trees, and a characterization of subsumption by homomorphisms between these trees. The lcs operation then corresponds to the product operation on trees.
Learning the CLASSIC Description Logic: Theoretical and Experimental Results
 In Principles of Knowledge Representation and Reasoning: Proceedings of the Fourth International Conference (KR94
, 1994
"... We present a series of theoretical and experimental results on the learnability of description logics. We first extend previous formal learnability results on simple description logics to CClassic, a description logic expressive enough to be practically useful. We then experimentally evaluate two e ..."
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Cited by 107 (8 self)
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We present a series of theoretical and experimental results on the learnability of description logics. We first extend previous formal learnability results on simple description logics to CClassic, a description logic expressive enough to be practically useful. We then experimentally evaluate two extensions of a learning algorithm suggested by the formal analysis. The first extension learns CClassic descriptions from individuals. (The formal results assume that examples are themselves descriptions.) The second extension learns disjunctions of CClassic descriptions from individuals. The experiments, which were conducted using several hundred target concepts from a number of domains, indicate that both extensions reliably learn complex natural concepts. 1 INTRODUCTION One wellknown family of formalisms for representing knowledge are description logics, sometimes also called terminological logics or KLONEtype languages. Description logics have been applied in a number of contexts...
A Temporal Description Logic for Reasoning about Actions and Plans
 Journal of Artificial Intelligence Research
, 1998
"... A class of intervalbased temporal languages for uniformly representing and reasoning about actions and plans is presented. Actions are represented by describing what is true while the action itself is occurring, and plans are constructed by temporally relating actions and world states. The tempo ..."
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Cited by 100 (20 self)
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A class of intervalbased temporal languages for uniformly representing and reasoning about actions and plans is presented. Actions are represented by describing what is true while the action itself is occurring, and plans are constructed by temporally relating actions and world states. The temporal languages are members of the family of Description Logics, which are characterized by high expressivity combined with good computational properties. The subsumption problem for a class of temporal Description Logics is investigated and sound and complete decision procedures are given. The basic language TLF is considered #rst: it is the composition of a temporal logic TL # able to express interval temporal networks # together with the nontemporal logic F # a Feature Description Logic. It is proven that subsumption in this language is an NPcomplete problem. Then it is shown how to reason with the more expressive languages TLUFU and TLALCF . The former adds disjunction both at...
Least Common Subsumers and Most Specific Concepts in a Description Logic with Existential Restrictions and Terminological Cycles
, 2003
"... Computing least common subsumers (Ics) and most specific concepts (msc) are inference tasks that can support the bottomup construction of knowledge bases in description logics. In description logics with existential restrictions, the most specific concept need not exist if one restricts the attenti ..."
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Cited by 97 (20 self)
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Computing least common subsumers (Ics) and most specific concepts (msc) are inference tasks that can support the bottomup construction of knowledge bases in description logics. In description logics with existential restrictions, the most specific concept need not exist if one restricts the attention to concept descriptions or acyclic TBoxes. In this paper, we extend the notions les and msc to cyclic TBoxes. For the description logic EC (which allows for conjunctions, existential restrictions, and the topconcept), we show that the les and msc always exist and can be computed in polynomial time if we interpret cyclic definitions with greatest fixpoint semantics.
NearSynonymy and Lexical Choice
 Computational Linguistics
, 2002
"... We develop a new computational model for representing the finegrained meanings of nearsynonyms and the differences between them. We also develop a sophisticated lexicalchoice process that can decide which of several nearsynonyms is most appropriate in a particular situation. This research has di ..."
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Cited by 58 (8 self)
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We develop a new computational model for representing the finegrained meanings of nearsynonyms and the differences between them. We also develop a sophisticated lexicalchoice process that can decide which of several nearsynonyms is most appropriate in a particular situation. This research has direct applications in machine translation and text generation. We first identify the problems of representing nearsynonyms in a computational lexicon and show that no previous model adequately accounts for nearsynonymy. We then propose a preliminary theory to account for nearsynonymy, relying crucially on the notion of granularity of representation, in which the meaning of a word arises out of a contextdependent combination of a contextindependent core meaning and a set of explicit differences to its nearsynonyms. That is, nearsynonyms cluster together. We then develop a clustered model of lexical knowledge, derived from the conventional ontological model. The model cuts off the ontology at a coarse grain, thus avoiding an awkward proliferation of languagedependent concepts in the ontology, and groups nearsynonyms into subconceptual clusters that are linked to the ontology. A cluster differentiates nearsynonyms in terms of finegrained aspects of denotation, implication, expressed attitude, and style. The model is general enough to account for other types of variation, for instance, in collocational behaviour. An efficient, robust, and flexible finegrained lexicalchoice process is a consequence of a clustered model of lexical knowledge. To make it work, we formalize criteria for lexical choice as preferences to express certain concepts with varying indirectness, to express attitudes, and to establish certain styles. The lexicalchoice process itself works on two tiers: between clusters and between nearsynonyns of clusters. We describe our prototype implementation of the system, called ISaurus.
Explaining Subsumption in Description Logics
, 1994
"... This paper explores the explanation of subsumption reasoning in Description Logics that are implemented using normalization methods, focusing on the perspective of knowledge engineers. The notion of explanation is specified using a prooftheoretic framework for presenting the inferences supported in ..."
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Cited by 52 (12 self)
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This paper explores the explanation of subsumption reasoning in Description Logics that are implemented using normalization methods, focusing on the perspective of knowledge engineers. The notion of explanation is specified using a prooftheoretic framework for presenting the inferences supported in these systems. The problem of overly long explanations is addressed by decomposing them into smaller, independent steps, using the notions of “atomic description” and “atomic justification”. Implementation aspects are explored by considering the design space and some desiderata for explanation modules. This approach has been implemented for the classic knowledge representation system.
Rewriting concepts using terminologies
 Proceedings of the Seventh International Conference on Knowledge Representation and Reasoning (KR2000
, 2000
"... The problem of rewriting a concept given a terminology can informally be stated as follows: given a terminology T (i.e., a set of concept definitions) and a concept description C that does not contain concept names defined in T, can this description be rewritten into a "related better & ..."
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Cited by 45 (6 self)
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The problem of rewriting a concept given a terminology can informally be stated as follows: given a terminology T (i.e., a set of concept definitions) and a concept description C that does not contain concept names defined in T, can this description be rewritten into a &quot;related better &quot; description E by using (some of) the names defined in T? In this paper, we first introduce a general framework for the rewriting problem in description logics, and then concentrate on one specific instance of the framework, namely the minimal rewriting problem (where &quot;better &quot; means shorter, and &quot;related &quot; means equivalent). We investigate the complexity of the decision problem induced by the minimal rewriting problem for the languages FL 0, ALN, ALE, and ALC, and then introduce an algorithm for computing (minimal) rewritings for the language ALE. (In the full paper, a similar algorithm is also developed for ALN.) Finally, we sketch other interesting instances of the framework.
The Learnability of Description Logics with Equality Constraints
 Machine Learning
, 1994
"... Although there is an increasing amount of experimental research on learning concepts expressed in firstorder logic, there are still relatively few formal results on the polynomial learnability of firstorder representations from examples. Most previous analyses in the pacmodel have focused on s ..."
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Cited by 37 (3 self)
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Although there is an increasing amount of experimental research on learning concepts expressed in firstorder logic, there are still relatively few formal results on the polynomial learnability of firstorder representations from examples. Most previous analyses in the pacmodel have focused on subsets of Prolog, and only a few highly restricted subsets have been shown to be learnable. In this paper, we will study instead the learnability of the restricted firstorder logics known as "description logics", also sometimes called "terminological logics" or "KLONEtype languages". Description logics are also subsets of predicate calculus, but are expressed using a different syntax, allowing a different set of syntactic restrictions to be explored. We first define a simple description logic, summarize some results on its expressive power, and then analyze its learnability. It is shown that the full logic cannot be tractably learned. However, syntactic restrictions exist that enable tractable learning from positive examples alone, independent of the size of the vocabulary used to describe examples. The learnable sublanguage appears to be incomparable in expressive power to any subset of firstorder logic previously known to be learnable.