Ontology Matching with Semantic Verification

By Yves R. Jean-Mary, E. Patrick Shironoshita, Mansur R. Kabuka. Journal of Web Semantics, 2009

437 citations recorded by the source corpus.

ASMOV (Automated Semantic Matching of Ontologies with Verification) is a novel algorithm that uses lexical and structural characteristics of two ontologies to iteratively calculate a similarity measure between them, derives an alignment, and then verifies it to ensure that it does not contain semantic inconsistencies. In this paper, we describe the ASMOV algorithm, and then present experimental results that measure its accuracy using the OAEI 2008 tests, and that evaluate its use with two different thesauri: WordNet, and the Unified Medical Language System (UMLS). These results show the increased accuracy obtained by combining lexical, structural and extensional matchers with semantic verification, and demonstrate the advantage of using a domain-specific thesaurus for the alignment of specialized ontologies.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/618dcac7bb882e28b0b0529cedfabe98901bdad8

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Citations in the corpus

Ontology Matching: State of the Art and Future Challenges (2013, background): “A notable progress has been made between 2004 and 2005 by Falcon; and the results of 2005 were repeated in 2009 by both ASMOV and Lily.”
Ontology Alignment Evaluation Initiative: Six Years of Experience (2011, methodology): “These systems are AFlood [ 56 ] , Agree-mentMaker [ 11 ] as well as an extension of the system, AROMA [ 13 ] , Asmov [ 37 ] , Kosimap [ 52 ] and DSSim [ 50 ] .”
Ontology matching: A literature review (2015, background): “In this period the following articles describe its features and performance: Jean-Mary and Kabuka (2007), Jean-Mary, Shironoshita, and Kabuka (2009) (2010) and Behkamal et al. (2010).”
Generic schema matching, ten years later (2011, methodology): “Figure 2 shows a comparative overview of selected tools: Cupid [45], COMA++ [3][19][20], ASMOV [40], Falcon-AO [39], RiMON [44], AgreementMaker [16], OII Harmony [67].”
LogMap: Logic-Based and Scalable Ontology Matching (2011, methodology): “, ASMOV [11], KOSIMap [21], CODI [19,20]) incorporate techniques for ‘on the fly’ semantic verification.”
A self-training approach for resolving object coreference on the semantic web (2011, methodology): “ASMOV [16] and CODI [20] employed similarity-based matchers to obtain coreferent URIs and performed logical inference to remove inconsistent results.”
Ontology Matching (2007, background): “ASMOV (Automatic Semantic Matching of Ontologies with Verification) is an automatic approach for ontology matching that targets information integration for bioinformatics (Jean-Mary et al. 2009).”
PARIS: Probabilistic Alignment of Relations, Instances, and Schema (2011, methodology): “The approaches that align the classes are manifold, using techniques such as sense clustering [14], lexical and structural characteristics [21], or composite approaches [3].”