xMatcher: Matching extensible markup language schemas using semantic-based techniques

TitrexMatcher: Matching extensible markup language schemas using semantic-based techniques
Publication TypeJournal Article
Year of Publication2020
AuthorsYousfi, A, Yazidi, MHEl, Zellou, A
JournalInternational Journal of Advanced Computer Science and Applications
Volume11
Pagination655-665
Mots-clésCritical steps, Data integration, Data integration system, Hypertext systems, Labor time, Matching accuracy, Matching system, Matchings, Ontology, Schema matching, Semantic relatedness, Semantic similarity, Semantics, Wordnet, XML
Abstract

Schema matching is a critical step in data integration systems. Most recent schema matching systems require a manual double-check of the matching results to add missed matches and remove incorrect matches. Manual correction is labor-intensive and time-consuming, however without it the results accuracy is significantly lower. In this paper, we present xMatcher, an approach to automatically match XML schemas. Given two schemas S and S2, xMatcher identifies semantically similar schema elements between S and S2. To obtain correct matches, xMatcher first transforms S and S2 into sets of words; then, it uses a context-based measure to identify the meanings of words in their contexts; next, it captures semantic relatedness between sets of words in different schemas; finally, it uses WordNet information to calculate the similarity values between semantically related sets and matches the pairs of sets whose similarity values are greater than or equal to 0:8. The results show that xMatcher provides superior matching accuracy compared to the state of the art matching systems. Overall, our proposal can be a stepping stone towards decreasing human assistance and overcoming the weaknesses of current matching initiatives in terms of matching accuracy. © 2020, Science and Information Organization.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85091018595&doi=10.14569%2fIJACSA.2020.0110880&partnerID=40&md5=dff26b18d541472b808a6d6c85f08039
DOI10.14569/IJACSA.2020.0110880
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