A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
A vector space model for automatic indexing
Communications of the ACM
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Global Viewing of Heterogeneous Data Sources
IEEE Transactions on Knowledge and Data Engineering
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Comparison of Schema Matching Evaluations
Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
An information retrieval approach to ontology mapping
Data & Knowledge Engineering - Special issue: Application of natural language to information systems (NLDB04)
ACM SIGMOD Record
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
An approach to Ontology Mapping based on the Lucene search engine library
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
A string metric for ontology alignment
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
UFOme: An ontology mapping system with strategy prediction capabilities
Data & Knowledge Engineering
Semantic flow networks: semantic interoperability in networks of ontologies
JIST'11 Proceedings of the 2011 joint international conference on The Semantic Web
A differentor-based adaptive ontology-matching approach
Journal of Information Science
Matching Attributes across Overlapping Heterogeneous Data Sources Using Mutual Information
Journal of Database Management
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Ontology mapping is a key problem to be solved for the success of the Semantic Web and related technologies. An ontology mapping algorithm aims at finding correspondences (or mappings) between entities of the source and target ontologies by combining several matching components, i.e., individual matchers, that exploit one or more sources of information encoded within the ontologies. In this paper, we investigate linguistic techniques for ontology mapping and underline their importance in paving the way to other matching techniques. We define a general mapping model architecture and discuss an implementation in the Lucene ontology matcher (LOM). LOM leverages the features of the Lucene search engine library. The basic idea is to gather the different kinds of linguistic information of the source ontology entities in Lucene documents that will be stored into an index. Mappings are discovered by using the values of entities in the target ontology as search arguments against the index created from the source ontology. Extensive experimental results using a popular benchmark test suite show the effectiveness of this approach in terms of precision, recall, F-measure and execution time as compared to other linguistic approaches.