Results 1 - 10 of 333

Efficient semantic matching

by Fausto Giunchiglia, Mikalai Yatskevich, Enrico Giunchiglia , 2004
"... We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. We concentrate on classifications with tree structures. In semantic matching, correspondences are discovered by translating the natural language labels of nodes into prop ..."
Abstract - Cited by 855 (68 self) - Add to MetaCart
We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. We concentrate on classifications with tree structures. In semantic matching, correspondences are discovered by translating the natural language labels of nodes into propositional formulas, and by codifying matching into a propositional unsatisfiability problem. We distinguish between problems with conjunctive formulas and problems with disjunctive formulas, and present various optimizations. For instance, we propose a linear time algorithm which solves the first class of problems. According to the tests we have done so far, the optimizations substantially improve the time performance of the system.

Modular Reuse of Ontologies: Theory and Practice

by Bernardo Cuenca Grau, Ian Horrocks, Yevgeny Kazakov, Ulrike Sattler - JAIR , 2008
"... In this paper, we propose a set of tasks that are relevant for the modular reuse of ontologies. In order to formalize these tasks as reasoning problems, we introduce the notions of conservative extension, safety and module for a very general class of logic-based ontology languages. We investigate th ..."
Abstract - Cited by 139 (22 self) - Add to MetaCart
In this paper, we propose a set of tasks that are relevant for the modular reuse of ontologies. In order to formalize these tasks as reasoning problems, we introduce the notions of conservative extension, safety and module for a very general class of logic-based ontology languages. We investigate the general properties of and relationships between these notions and study the relationships between the relevant reasoning problems we have previously identified. To study the computability of these problems, we consider, in particular, Description Logics (DLs), which provide the formal underpinning of the W3C Web Ontology Language (OWL), and show that all the problems we consider are undecidable or algorithmically unsolvable for the description logic underlying OWL DL. In order to achieve a practical solution, we identify conditions sufficient for an ontology to reuse a set of symbols “safely”—that is, without changing their meaning. We provide the notion of a safety class, which characterizes any sufficient condition for safety, and identify a family of safety classes–called locality—which enjoys a collection of desirable properties. We use the notion of a safety class to extract modules from ontologies, and we provide various modularization algorithms that are appropriate to the properties of the particular safety class in use. Finally, we show practical benefits of our safety checking and module extraction algorithms. 1.
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Using the Semantic Web as background knowledge for ontology mapping

by Marta Sabou, Mathieu d'Aquin, Enrico Motta - IN PROC. OF THE INT. WORKSHOP ON ONTOLOGY MATCHING (OM-2006 , 2006
"... While current approaches to ontology mapping produce good results by mainly relying on label and structure based similarity measures, there are several cases in which they fail to discover important mappings. In this paper we describe a novel approach to ontology mapping, which is able to avoid thi ..."
Abstract - Cited by 102 (37 self) - Add to MetaCart
While current approaches to ontology mapping produce good results by mainly relying on label and structure based similarity measures, there are several cases in which they fail to discover important mappings. In this paper we describe a novel approach to ontology mapping, which is able to avoid this limitation by using background knowledge. Existing approaches relying on background knowledge typically have one or both of two key limitations: 1) they rely on a manually selected reference ontology; 2) they suffer from the noise introduced by the use of semi-structured sources, such as text corpora. Our technique circumvents these limitations by exploiting the increasing amount of semantic resources available online. As a result, there is no need either for a manually selected reference ontology (the relevant ontologies are dynamically selected from an online ontology repository), or for transforming background knowledge in an ontological form. The promising results from experiments on two real life thesauri indicate both that our approach has a high precision and also that it can find mappings, which are typically missed by existing approaches.

Ontology matching: state of the art and future challenges

by Pavel Shvaiko, et al. , 2013
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Abstract - Cited by 99 (1 self) - Add to MetaCart
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G.: Constructing Virtual Documents for Ontology Matching

by Hang Zhang, Wei Hu, Yuzhong Qu - In: 15th International World Wide Web Conference , 2006
"... Abstract. Ontology matching is a crucial task for data integration and management on the Semantic Web. The ontology matching techniques today can solve many problems from heterogeneity of ontologies to some extent. However, for matching large ontologies, most ontology match-ers take too long run tim ..."
Abstract - Cited by 79 (9 self) - Add to MetaCart
Abstract. Ontology matching is a crucial task for data integration and management on the Semantic Web. The ontology matching techniques today can solve many problems from heterogeneity of ontologies to some extent. However, for matching large ontologies, most ontology match-ers take too long run time and have strong requirements on running environment. Based on the MapReduce framework and the virtual doc-ument technique, in this paper, we propose a 3-stage MapReduce-based approach called V-Doc+ for matching large ontologies, which signifi-cantly reduces the run time while keeping good precision and recall. Firstly, we establish four MapReduce processes to construct virtual doc-ument for each entity (class, property or instance), which consist of a simple process for the descriptions of entities, an iterative process for the descriptions of blank nodes and two processes for exchanging the descriptions with neighbors. Then, we use a word-weight-based partition method to calculate similarities between entities in the corresponding re-ducers. We report our results from two experiments on an OAEI dataset and a dataset from the biology domain. Its performance is assessed by comparing with existing ontology matchers. Additionally, we show how run time is reduced with increasing the size of cluster. 1
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Ten challenges for ontology matching

by Pavel Shvaiko, Jérôme Euzenat , 2008
"... This paper aims at analyzing the key trends and challenges of the ontology matching field. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which is robust enough ..."
Abstract - Cited by 76 (3 self) - Add to MetaCart
This paper aims at analyzing the key trends and challenges of the ontology matching field. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which is robust enough to be the basis for future development, and which is usable by non expert users. In this paper we first provide the basics of ontology matching with the help of examples. Then, we present general trends of the field and discuss ten challenges for ontology matching, thereby aiming to direct research into the critical path and to facilitate progress of the field.
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Semantic matching: Algorithms and implementation

by Fausto Giunchiglia, Mikalai Yatskevich, Pavel Shvaiko - JOURNAL ON DATA SEMANTICS , 2007
"... We view match as an operator that takes two graph-like structures (e.g., classifications, XML schemas) and produces a mapping between the nodes of these graphs that correspond semantically to each other. Semantic matching is based on two ideas: (i) we discover mappings by computing semantic relation ..."
Abstract - Cited by 75 (29 self) - Add to MetaCart
We view match as an operator that takes two graph-like structures (e.g., classifications, XML schemas) and produces a mapping between the nodes of these graphs that correspond semantically to each other. Semantic matching is based on two ideas: (i) we discover mappings by computing semantic relations (e.g., equivalence, more general); (ii) we determine semantic relations by analyzing the meaning (concepts, not labels) which is codified in the elements and the structures of schemas. In this paper we present basic and optimized algorithms for semantic matching, and we discuss their implementation within the S-Match system. We evaluate S-Match against three state of the art matching systems, thereby justifying empirically the strength of our approach.
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A logical framework for modularity of ontologies

by Bernardo Cuenca Grau, Ian Horrocks, Yevgeny Kazakov, Ulrike Sattler - In Proc. IJCAI-2007 , 2007
"... Modularity is a key requirement for collaborative ontology engineering and for distributed ontology reuse on the Web. Modern ontology languages, such as OWL, are logic-based, and thus a useful notion of modularity needs to take the semantics of ontologies and their implications into account. We prop ..."
Abstract - Cited by 60 (7 self) - Add to MetaCart
Modularity is a key requirement for collaborative ontology engineering and for distributed ontology reuse on the Web. Modern ontology languages, such as OWL, are logic-based, and thus a useful notion of modularity needs to take the semantics of ontologies and their implications into account. We propose a logic-based notion of modularity that allows the modeler to specify the external signature of their ontology, whose symbols are assumed to be defined in some other ontology. We define two restrictions on the usage of the external signature, a syntactic and a slightly less restrictive, semantic one, each of which is decidable and guarantees a certain kind of “black-box ” behavior, which enables the controlled merging of ontologies. Analysis of real-world ontologies suggests that these restrictions are not too onerous. 1

Ontologies and databases: The DL-Lite approach

by Diego Calvanese, Giuseppe De Giacomo, Domenico Lembo, Maurizio Lenzerini, Antonella Poggi, Mariano Rodriguez-muro, Riccardo Rosati - IN REASONING WEB, VOLUME 5689 OF LNCS , 2009
"... Ontologies provide a conceptualization of a domain of interest. Nowadays, they are typically represented in terms of Description Logics (DLs), and are seen as the key technology used to describe the semantics of information at various sites. The idea of using ontologies as a conceptual view over d ..."
Abstract - Cited by 58 (34 self) - Add to MetaCart
Ontologies provide a conceptualization of a domain of interest. Nowadays, they are typically represented in terms of Description Logics (DLs), and are seen as the key technology used to describe the semantics of information at various sites. The idea of using ontologies as a conceptual view over data repositories is becoming more and more popular, but for it to become widespread in standard applications, it is fundamental that the conceptual layer through which the underlying data layer is accessed does not introduce a significant overhead in dealing with the data. Based on these observations, in recent years a family of DLs, called DL-Lite, has been proposed, which is specifically tailored to capture basic ontology and conceptual data modeling languages, while keeping low complexity of reasoning and of answering complex queries, in particular when the complexity is measured w.r.t. the size of the data. In this article, we present a detailed account of the major results that have been achieved for the DL-Lite family. Specifically, we concentrate on DL-LiteA,id, an expressive member of this family, present algorithms for reasoning and query answering over DL-LiteA,id ontologies,

A Bayesian Network Approach to Ontology Mapping

by Rong Pan, Zhongli Ding, Yang Yu, Yun Peng - In: Proceedings ISWC 2005 , 2005
"... Abstract. This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Baye ..."
Abstract - Cited by 55 (5 self) - Add to MetaCart
Abstract. This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs. Probabilities needed for constructing conditional probability tables (CPT) during translation and for measuring semantic similarity during mapping are learned using text classification techniques where each concept in an ontology is associated with a set of semantically relevant text documents, which are obtained by ontology guided web mining. The basic ideas of this approach are validated by positive results from computer experiments on two small real-world ontologies. 1
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