Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145817
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLi, Feien_US
dc.contributor.authorLiao, Lejianen_US
dc.contributor.authorZhang, Lanfangen_US
dc.contributor.authorZhu, Xinhuaen_US
dc.contributor.authorZhang, Boen_US
dc.contributor.authorWang, Zhengen_US
dc.date.accessioned2021-01-08T08:42:16Z-
dc.date.available2021-01-08T08:42:16Z-
dc.date.issued2020-
dc.identifier.citationLi, F., Liao, L., Zhang, L., Zhu, X., Zhang, B., & Wang, Z. (2020). An efficient approach for measuring semantic similarity combining WordNet and Wikipedia. IEEE Access, 8, 184318-184338. doi:10.1109/ACCESS.2020.3025611en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttps://hdl.handle.net/10356/145817-
dc.description.abstractThe measurement of semantic similarity between concepts is an important research topic in natural language processing. In the past, several approaches for measuring the semantic similarity between concepts have been proposed based on WordNet or Wikipedia. However, improvements in the measurement accuracy of most methods have led to a dramatic increase in time complexity, and the existing methods do not effectively integrate WordNet and Wikipedia. In this paper, we focus on designing an efficient semantic similarity method based on WordNet and Wikipedia. To improve the accuracy of WordNet edge-based measures, we propose an edge weight model for combining edge and density information, which assigns a weight to each edge adaptively based on the number of direct hyponyms of the subsumer. Second, to improve the computational efficiencies of the existing Wikipedia link vector-based measures, we propose a new Wikipedia link feature-based semantic similarity method that converts Wikipedia links into semantic knowledge and replaces the TF-IDF statistical weight model in the existing measures. In addition, we propose two new word disambiguation strategies to further improve the accuracy of Wikipedia link-based measures. Finally, to fully exploit the advantages of WordNet and Wikipedia, we propose two new aggregation schemas for combining WordNet “is-a” semantics and Wikipedia link semantics to replace the current aggregation schemas that combine WordNet “is-a” semantics with category semantics in Wikipedia. The experimental results show that our aggregation models are outstanding in terms of accuracy, efficiency and word coverage compared to state-of-the-art similarity measures.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Accessen_US
dc.rights© 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleAn efficient approach for measuring semantic similarity combining WordNet and Wikipediaen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/ACCESS.2020.3025611-
dc.description.versionPublished versionen_US
dc.identifier.volume8en_US
dc.identifier.spage184318en_US
dc.identifier.epage184338en_US
dc.subject.keywordsSemantic Similarity, Edge Weight Modelen_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:SCSE Journal Articles
Files in This Item:
File Description SizeFormat 
09201502.pdf2.34 MBAdobe PDFView/Open

Page view(s)

152
Updated on Jul 6, 2022

Download(s) 50

17
Updated on Jul 6, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.