A SAT-Based Algorithm for Context Matching

Abstract. (Semi-)automatic mapping -also called (semi-)automatic alignment -of ontologies is a core task to achieve interoperability when two agents or services use different ontologies. In the existing literature, the focus has so far been on improving the quality of mapping results. We here consider QOM, Quick Ontology Mapping, as a way to trade off between effectiveness (i.e. quality) and efficiency of the mapping generation algorithms. We show that QOM has lower run-time complexity than existing prominent approaches. Then, we show in experiments that this theoretical investigation translates into practical benefits. While QOM gives up some of the possibilities for producing high-quality results in favor of efficiency, our experiments show that this loss of quality is marginal.

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“…An interesting approach for schema and ontology mapping is presented by [20]. Explicit semantic rules are added for consideration.…”

Section: Related Workmentioning

confidence: 99%

QOM – Quick Ontology Mapping

Ehrig

Staab

2004

The Semantic Web – ISWC 2004

285

161

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“…Significant works [10,17,20,5] cover the first category while others [15,7,9,14,8] concern the second. In this paper we focus on the third.…”

Section: Ontology Translationmentioning

confidence: 99%

“…Our previous work on OntoMerge [13,14] has used first order bridging axioms to represent semantic mappings. It is well known that complex mapping rules can not be generated fully automatically; human experts may need to help the process based on the mapping suggestions from automatic or semi-automatic ontology mapping tools such as those described in [10,17,20,5].…”

Section: Ontology Translationmentioning

confidence: 99%

Towards Theory Translation

Dou

McDermott

2006

Declarative Agent Languages and Technologies IV

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Abstract. Ontologies play a key role in agent communication and the emerging Semantic Web to define a vocabulary of concepts and their relationships. Different agents and web services may use vocabularies from different ontologies to describe their data. The current research on ontology mapping and ontology translation mainly focuses on how to map and translate vocabularies and associated data instances from one ontology to another. However, more complicated true statements, such as axioms (rules), are used or being developed to describe the relationships among the concepts. When extending one ontology using complicated true statements (theory) from another, we must confront the problem of theory translation, which is difficult because of the asymmetry of translation. In this paper, using an inferential approach we call axiom derivation, we show how to translate complex axioms between different time ontologies. We also prove the validity of our algorithm.

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“…In fact, each node shares part of its resources on the network and is responsible of providing their ontological description. To enable resource sharing and semantic information interoperability in networked contexts with a multitude of autonomous ontologies, appropriate ontology matching techniques are thus required to determine semantic mappings between concepts of different ontologies that are semantically related [5,6,7]. In this paper, we describe H-Match, an algorithm for dynamically performing ontology matching at different levels of depth, with different degrees of flexibility and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Semantic Information Interoperability in Open Networked Systems

Castano

Ferrara

Montanelli

et al. 2004

Semantics of a Networked World. Semantics for Grid Databases

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Abstract. In open networked systems, each node shares part of its informational resources on the network and is responsible of providing an ontological description for them. To enable semantic information interoperability in networked contexts with a multitude of autonomous ontologies, appropriate matching techniques are required to determine semantic mappings between concepts of different ontologies. In this paper, we describe H-Match, an algorithm for dynamically performing ontology matching in open networked contexts. H-Match provides several matching models and metrics to work at different levels of depth, with different degrees of flexibility and accuracy, thus supporting semantic interoperability in a flexible way.

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A SAT-Based Algorithm for Context Matching

Cited by 24 publications

References 9 publications

QOM – Quick Ontology Mapping

QOM – Quick Ontology Mapping

Towards Theory Translation

Semantic Information Interoperability in Open Networked Systems

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