Word-Sense Disambiguation for Ontology Mapping: Concept Disambiguation using Virtual Documents and Information Retrieval Techniques

Abstract

Ontology mapping is a crucial task for the facilitation of information exchange and data integration. A mapping system can use a variety of similarity measures to determine concept correspondences. This paper proposes the integration of word-sense disambiguation techniques into lexical similarity measures. We propose a disambiguation methodology which entails the creation of virtual documents from concept and sense definitions, including their neighbourhoods. The specific terms are weighted according to their origin within their respective ontology. The document similarities between the concept document and sense documents are used to disambiguate the concept meanings. First, we evaluate to what extent the proposed disambiguation method can improve the performance of a lexical similarity metric. We observe that the disambiguation method improves the performance of each tested lexical similarity metric. Next, we demonstrate the potential of a mapping system utilizing the proposed approach through the comparison with contemporary ontology mapping systems. We observe a high performance on a real-world data set. Finally, we evaluate how the application of several term-weighting techniques on the virtual documents can affect the quality of the generated alignments. Here, we observe that weighting terms according to their ontology origin leads to the highest performance.

Background knowledge in ontology matching: A survey

“…Agreement Maker (2009) [47] OWL-Ctx (2006) [ [311] Thayasivam & Doshi (2011) [326] X-SOM (2007) [50] MAMA (2011) [39] iG-Match (2007) [ Schadd & Roos (2015) [282] ALIN (2016) [51] CroLOM (2016) [172] CroMatcher (2016) [115] OMI-DL (2016) [ HSOMap (2016) [97] FCA-Map (2016) [383] KEPLER (2017) [162] ONTMAT (2017) [105] Xue et al (2017) [359,362,364] He et al (2017) [124] OIM-SIM I (2017) [377] SANOM (2018) [219] EVOCROS (2018) [56] FCA-MapX (2018) [44] Ochieng & Kyanda (2018) […”

Ontology mapping with auxiliary resources

“…This distance is then transformed into a similarity metric. For this evaluation a lexical similarity using WordNet as a lexical resource has been evaluated (Schadd and Roos, 2014b), as described in Chapter 4. Specifically, lsm 2 has been applied as similarity metric and A-MEAN as disambiguation policy.…”