RiMOM: A Dynamic Multistrategy Ontology Alignment Framework

Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dynamic mul...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 21; H. 8; S. 1218 - 1232
Hauptverfasser: Li, Juanzi, Tang, Jie, Li, Yi, Luo, Qiong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.08.2009
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Alignment
Applied sciences
Benchmarking
Computer science; control theory; systems
Data engineering
Data processing. List processing. Character string processing
Dynamical systems
Dynamics
Exact sciences and technology
Heterogeneous databases
Information systems. Data bases
knowledge and data engineering tools and techniques
Matching
Memory organisation. Data processing
Natural languages
Ontologies
ontology languages
Search engines
Semantic Web
Similarity
Software
Strategy
Tasks
Taxonomy
Ontology languages
Knowledge reuse
Heterogeneous Databases
Engineering information systems
Knowledge engineering
Information integration
knowledge and data engineering tools and techniques
Ontology
Federated database
Text
ontology languages
Ontology mapping
Alignment
Semantics
Heterogeneous databases
Strategy
ISSN:1041-4347, 1558-2191
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Beschreibung
Zusammenfassung:Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM. The key insight in this framework is that similarity characteristics between ontologies may vary widely. We propose a systematic approach to quantitatively estimate the similarity characteristics for each alignment task and propose a strategy selection method to automatically combine the matching strategies based on two estimated factors. In the approach, we consider both textual and structural characteristics of ontologies. With RiMOM, we participated in the 2006 and 2007 campaigns of the Ontology Alignment Evaluation Initiative (OAEI). Our system is among the top three performers in benchmark data sets.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2008.202