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Generic Schema Matching, Ten Years Later
"... In a paper published in the 2001 VLDB Conference, we proposed treating generic schema matching as an independent problem. We developed a taxonomy of existing techniques, a new schema matching algorithm, and an approach to comparative evaluation. Since then, the field has grown into a major research ..."
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Cited by 15 (0 self)
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In a paper published in the 2001 VLDB Conference, we proposed treating generic schema matching as an independent problem. We developed a taxonomy of existing techniques, a new schema matching algorithm, and an approach to comparative evaluation. Since then, the field has grown into a major research topic. We briefly summarize the new techniques that have been developed and applications of the techniques in the commercial world. We conclude by discussing future trends and recommendations for further work. 1.
Evolution of the COMA Match System
"... Abstract. The schema and ontology matching systems COMA and COMA++ are widely used in the community as a basis for comparison of new match approaches. We give an overview of the evolution of COMA during the last decade. In particular we discuss lessons learned on strong points and remaining weakness ..."
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Cited by 8 (2 self)
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Abstract. The schema and ontology matching systems COMA and COMA++ are widely used in the community as a basis for comparison of new match approaches. We give an overview of the evolution of COMA during the last decade. In particular we discuss lessons learned on strong points and remaining weaknesses. Furthermore, we outline the design and functionality of the upcoming COMA 3.0. 1
Matching Large Ontologies Based on Reduction Anchors
- PROCEEDINGS OF THE TWENTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
"... Matching large ontologies is a challenge due to the high time complexity. This paper proposes a new matching method for large ontologies based on reduction anchors. This method has a distinct advantage over the divide-and-conquer methods because it dose not need to partition large ontologies. In par ..."
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Cited by 6 (0 self)
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Matching large ontologies is a challenge due to the high time complexity. This paper proposes a new matching method for large ontologies based on reduction anchors. This method has a distinct advantage over the divide-and-conquer methods because it dose not need to partition large ontologies. In particular, two kinds of reduction anchors, positive and negative reduction anchors, are proposed to reduce the time complexity in matching. Positive reduction anchors use the concept hierarchy to predict the ignorable similarity calculations. Negative reduction anchors use the locality of matching to predict the ignorable similarity calculations. Our experimental results on the real world data sets show that the proposed method is efficient for matching large ontologies.
Review of Ontology Matching Approaches and Challenges
, 2015
"... Ontology mapping aims to solve the semantic heterogeneity problems such as ambiguous entity names, different entity granularity, incomparable categorization, and various instances of different ontologies. The mapping helps to search or query data from different sources. Ontology mapping is necessary ..."
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Ontology mapping aims to solve the semantic heterogeneity problems such as ambiguous entity names, different entity granularity, incomparable categorization, and various instances of different ontologies. The mapping helps to search or query data from different sources. Ontology mapping is necessary in many applications such as data integration, ontology evolution, data warehousing, e-commerce and data exchange in various domains such as purchase order, health, music and e-commerce. It is performed by ontology matching approaches that find semantic correspondences between ontology entities. In this paper, we review state of the art ontology matching approaches. We describe the approaches according to instance-based, schema-based, instance and schema-based, usage-based, element-level, and structure-level. The analysis of the existing approaches will assist us in revealing some challenges in ontology mapping such as handling ontology matching errors, user involvement and reusing previous match operations. We explain the way of handling the challenges using new strategy in order to increase the performance.
Feature-based Clustering of Web Data Sources
"... The proliferation of web data sources increasingly demands the integration of these sources. To facilitate the integration process, a pre-analysis step is required to classify and group data sources into their correct domains. In this paper, we propose a feature-based clustering approach for cluster ..."
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The proliferation of web data sources increasingly demands the integration of these sources. To facilitate the integration process, a pre-analysis step is required to classify and group data sources into their correct domains. In this paper, we propose a feature-based clustering approach for clustering web data sources without any human intervention and based only on features extracted from the source schemas. In particular, we make use of both linguistic and structural schema features. We experimentally demonstrate the effectiveness of the proposed approach in terms of both the clustering quality and runtime.
