Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Data integration: a theoretical perspective
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Building a Pragmatic Semantic Web
IEEE Intelligent Systems
SPARQL query rewriting for implementing data integration over linked data
Proceedings of the 2010 EDBT/ICDT Workshops
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In this paper, when we use the term ontology, we are primarily referring to linked data in the form of RDF(S). The problem of ontology mapping has attracted considerable attention over the last few years, as the deployment of ontologies is increasing with the advent of the Web of Data. We identify two sharply distinct goals for ontology mapping, based on real-world use cases. These goals are: (i) ontology development, and (ii) facilitating interoperability. We systematically analyze the goals, side-by-side, and contrast them for the first time. Our analysis demonstrates the implications of the goals on ontology mapping and mapping representation. Many studies on ontology mapping have focused on ontology merging. Ontology merging is an ontology development task (goal i). With the increase in the number of web-based information systems that utilize ontologies, the need for facilitating interoperability between these systems is becoming more visible (goal ii). We show the consequences of focusing on interoperability with illustrative examples and provide an in-depth comparison to the information integration problem in databases. The consequences include: (i) an emphasis on class matching, as a critical part of facilitating interoperability, and (ii) an emphasis on the representation of correspondences, since the merging of ontologies is not suitable for interoperability. For class matching, various class similarity metrics are formalized and an algorithm which utilizes these metrics is designed. For representation, we present a novel W3C-compliant representation, named skeleton. An algorithm for creating the skeleton, for interoperability between ontologies, is also developed. Finally, we experimentally evaluate the effectiveness of the class similarity metrics on real-world ontologies.