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59
Learning and Inference in WEIGHTED LOGIC WITH APPLICATION TO NATURAL LANGUAGE PROCESSING
, 2008
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Decomposing description logic ontologies
, 2010
"... Recent years have seen the advent of large and complex ontologies, most notably in the medical domain. As a consequence, structuring mechanisms for ontologies are nowadays viewed as an indispensible tool. A basic such mechanism is the automatic decomposition of the vocabulary of an ontology into ind ..."
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Recent years have seen the advent of large and complex ontologies, most notably in the medical domain. As a consequence, structuring mechanisms for ontologies are nowadays viewed as an indispensible tool. A basic such mechanism is the automatic decomposition of the vocabulary of an ontology into independent parts. In this paper, we study decompositions that are syntax independent in the sense that the resulting partitioning depends only on the meaning of the vocabulary items, but not on the concrete syntactic form of the axioms in the ontology. We present the first systematic investigation of decompositions of this type in the context of ontologies. Specifically, we focus on ontologies formulated in description logics and provide a variety of results that range from theorems stating the existence of unique finest decompositions to complexity results and algorithms computing decompositions. We also investigate the relationship between the existence of unique finite decompositions and a variant of the Craig interpolation property called parallel interpolation.
D.: A Survey of SemanticsBased Approaches for Context Reasoning
 in Ambient Intelligence. In: Constructing Ambient Intelligence  AmI 2007 Workshops, CCIS 11
, 2008
"... Abstract. A key issue in the study of Ambient Intelligence is reasoning about context. The aim of context reasoning is to deduce new knowledge, based on the available context data. The endmost goal is to make the ambient services more ”intelligent”; closer to the specific needs of their users. The m ..."
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Abstract. A key issue in the study of Ambient Intelligence is reasoning about context. The aim of context reasoning is to deduce new knowledge, based on the available context data. The endmost goal is to make the ambient services more ”intelligent”; closer to the specific needs of their users. The main challenges of this effort derive from the imperfect context information, and the dynamic and heterogeneous nature of the ambient environments. In this paper, we focus on semanticsbased approaches for reasoning about context. We describe how each approach addresses the requirements of ambient environments, identify their limitations, and propose possible future research directions. 1
Pushing Efficient Evaluation of HEX Programs by Modular Decomposition ⋆
"... Abstract. The evaluation of logic programs with access to external knowledge sources requires to interleave external computation and model building. Deciding where and how to stop with one task and proceed with the next is a difficult problem, and existing approaches have severe scalability limitati ..."
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Cited by 13 (10 self)
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Abstract. The evaluation of logic programs with access to external knowledge sources requires to interleave external computation and model building. Deciding where and how to stop with one task and proceed with the next is a difficult problem, and existing approaches have severe scalability limitations in many realworld application scenarios. We introduce a new approach for organizing the evaluation of logic programs with external knowledge sources and describe a configurable framework for dividing the nonground program into overlapping possiblysmaller parts called evaluation units. These units will then be processed by interleaving external evaluations and model building according to an evaluation and a model graph, and by combining intermediate results. Experiments with our prototype implementation show a significant improvement of this technique compared to existing approaches. Interestingly, even for ordinary logic programs (with no external access), our decomposition approach speeds up existing state of the art ASP solvers in some cases, showing its potential for wider usage. 1
Distributed Resolution for ALC
"... Abstract. The use of Description Logic as the basis for Semantic Web Languages has led to new requirements with respect to scalable and nonstandard reasoning. In this paper, we address the problem of scalable reasoning by proposing a distributed, complete and terminating algorithm that decides satis ..."
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Cited by 12 (0 self)
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Abstract. The use of Description Logic as the basis for Semantic Web Languages has led to new requirements with respect to scalable and nonstandard reasoning. In this paper, we address the problem of scalable reasoning by proposing a distributed, complete and terminating algorithm that decides satisfiability of terminologies in ALC. The algorithm is based on recent results on applying resolution to description logics. We show that the resolution procedure proposed by Tammet can be distributed amongst multiple resolution solvers by assigning unique sets of literals to individual solvers. This results provides the basis for a highly scalable reasoning infrastructure for Description logics. 1
Integrating Natural Language, Knowledge Representation and Reasoning, and Analogical Processing to Learn by Reading
"... Learning by reading requires integrating several strands of AI research. We describe a prototype system, Learning Reader, which combines natural language processing, a largescale knowledge base, and analogical processing to learn by reading simplified language texts. We outline the architecture of ..."
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Cited by 11 (6 self)
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Learning by reading requires integrating several strands of AI research. We describe a prototype system, Learning Reader, which combines natural language processing, a largescale knowledge base, and analogical processing to learn by reading simplified language texts. We outline the architecture of Learning Reader and some of systemlevel results, then explain how these results arise from the components. Specifically, we describe the design, implementation, and performance characteristics of a natural language understanding model (DMAP) that is tightly coupled to a knowledge base three orders of magnitude larger than previous attempts. We show that knowing the kinds of questions being asked and what might be learned can help provide more relevant, efficient reasoning. Finally, we show that analogical processing provides a means of generating useful new questions and conjectures when the system ruminates offline about what it has read.
Knowledge Representation and Classical Logic
, 2007
"... Mathematical logicians had developed the art of formalizing declarative knowledge long before the advent of the computer age. But they were interested primarily in formalizing mathematics. Because of the important role of nonmathematical knowledge in AI, their emphasis was too narrow from the perspe ..."
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Mathematical logicians had developed the art of formalizing declarative knowledge long before the advent of the computer age. But they were interested primarily in formalizing mathematics. Because of the important role of nonmathematical knowledge in AI, their emphasis was too narrow from the perspective of knowledge representation, their formal languages were not sufficiently expressive. On the other hand, most logicians were not concerned about the possibility of automated reasoning; from the perspective of knowledge representation, they were often too generous in the choice of syntactic constructs. In spite of these differences, classical mathematical logic has exerted significant influence on knowledge representation research, and it is appropriate to begin this handbook with a discussion of the relationship between these fields. The language of classical logic that is most widely used in the theory of knowledge representation is the language of firstorder (predicate) formulas. These are the formulas that John McCarthy proposed to use for representing declarative knowledge in his advice taker paper [176], and Alan Robinson proposed to prove automatically using resolution [236]. Propositional logic is, of course, the most important subset of firstorder logic; recent
Grounding for model expansion in kguarded formulas with inductive definitions
 In IJCAI
, 2007
"... Mitchell and Ternovska [2005] proposed a constraint programming framework based on classical logic extended with inductive definitions. They formulate a search problem as the problem of model expansion (MX), which is the problem of expanding a given structure with new relations so that it satisfies ..."
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Cited by 10 (5 self)
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Mitchell and Ternovska [2005] proposed a constraint programming framework based on classical logic extended with inductive definitions. They formulate a search problem as the problem of model expansion (MX), which is the problem of expanding a given structure with new relations so that it satisfies a given formula. Their longterm goal is to produce practical tools to solve combinatorial search problems, especially those in NP. In this framework, a problem is encoded in a logic, an instance of the problem is represented by a finite structure, and a solver generates solutions to the problem. This approach relies on propositionalisation of highlevel specifications, and on the efficiency of modern SAT solvers. Here, we propose an efficient algorithm which combines grounding with partial evaluation. Since the MX framework is based on classical logic, we are able to take advantage of known results for the socalled guarded fragments. In the case of kguarded formulas with inductive definitions under a natural restriction, the algorithm performs much better than naive grounding by relying on connections between kguarded formulas and tree decompositions. 1
Distributed Reasoning Services for Multiple Ontologies
, 2004
"... The main goal of this paper is to propose a distributed paradigm for reasoning with multiple ontologies connected by semantic mappings. The contribution of the paper to this goal is twofold. From the theoretical point of view we characterize the problem of global subsumption (i.e. the problem of ..."
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Cited by 8 (2 self)
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The main goal of this paper is to propose a distributed paradigm for reasoning with multiple ontologies connected by semantic mappings. The contribution of the paper to this goal is twofold. From the theoretical point of view we characterize the problem of global subsumption (i.e. the problem of subsumption in a set of local ontologies connected by semantic mappings) as a suitable fixpoint combination of operators that compute subsumptions in the local ontologies. This allows us to define a sound and complete algorithm for global subsumptions which calls blackboxes subroutines for local subsumptions. The second contribution is the description of a prototype implementation of such algorithm in a peertopeer architecture.