ontology matching state of the art and future challenges pdf

Ontology matching state of the art and future challenges pdf

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Ten Challenges for Ontology Matching

Ontology matching: state of the art and future challenges

Ontology matching: state of the art and future challenges (2013)

Object Property Matching Utilizing the Overlap between Imported Ontologies

Ten Challenges for Ontology Matching

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Ontology matching: state of the art and future challenges

Large scale Linked Data is often based on relational databases and thereby tends to be modeled with rich object properties, specifying the exact relationship between two objects, rather than a generic is-a or part-of relationship. We study this phenomenon on government issued statistical data, where a vested interest exists in matching such object properties for data integration. We leverage the fact that while the labeling of the properties is often heterogeneous, e. State-of-the-art ontology matching tools do not use this effect and therefore tend to miss the possible correspondences. We enhance the state-of-the-art matching process by aligning the individuals of such imported ontologies separately and computing the overlap between them to improve the matching of the object properties. The matchers themselves are used as black boxes and are thus interchangeable. The new correspondences found with this method lead to an increase of recall up to 2.

Toggle navigation. Have you forgotten your login? Journal articles. Hide details. Abstract : After years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue some further research?


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Ontology matching: state of the art and future challenges (2013)

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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.

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. Unable to display preview.

Object Property Matching Utilizing the Overlap between Imported Ontologies

Abstract Ontology matching systems take a prominent position in solving semantic heterogeneity problems to facilitate sharing and reuse of ontologies. The process of generating ontology alignments through ontology matching techniques purely lies on how the concepts and relationships are modeled. This paper focuses on designing an ontology matching system in which concepts are modeled based on cognitive units of knowledge comprising of objects, attributes and relationships. The proposed cognitive based ontology matching system COGOM identifies semantically related concepts by aggregating the attribute similarity degree, structural similarity degree and semantic conception degree. The similarity computation is adapted from the Tversky psychological model of similarity. The proposed ontology matching system is adaptive in nature because of the cognitive based knowledge expression and the computational overhead of generating alignments is improved by forming quality clusters of semantically correlating concepts thus reducing the concept match space. The precision and recall metrics are used for evaluation of the proposed system using the benchmark data sets of OAEI

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Ontology Matching: State of the Art and Future Challenges Abstract: After years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations.

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  • Bevis R. 31.05.2021 at 16:38

    1. Ontology matching: state of the art and future challenges. Pavel Shvaiko and Jérôme Euzenat. Abstract—After years of research on ontology.

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