Concept Embedding for Information Retrieval

Abstract: Concepts are used to solve the term-mismatch problem. However, we need an effective similarity measure between concepts. Word embedding presents a promising solution. We present in this study three approaches to build concepts vectors based on words vectors. We use a vector-based measure to estimate inter-concepts similarity. Our experiments show promising results. Furthermore, words and concepts become comparable. This could be used to improve conceptual indexing process

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource, 563-569 S.
• Language: Englisch
• Notes: Postprint ; begutachtet (peer reviewed) ; In: Pasi, Gabriella (Hg.), Piwowarski, Benjamin (Hg.), Azzopardi, Leif (Hg.), Hanbury, Allan (Hg.): Advances in Information Retrieval: 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018 ; Proceedings. 2018. S. 563-569. ISBN 978-3-319-76941-7

References and Relationships

• Bibliographic citation: Advances in Information Retrieval: 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018 ; Proceedings ; Bd. 10772 ; Lecture Notes in Computer Science (LNCS) ; Bd. 10772

Classification and Topics

• Classification: Bibliotheks- und Informationswissenschaft

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Mannheim
• (who): SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
• (when): 2018
• Event: Veröffentlichung
• (where): Cham
• (who): Springer International Publishing
• (when): 2018
• Creator: Abdulahhad, Karam
• Contributor: Pasi, Gabriella ; Piwowarski, Benjamin ; Azzopardi, Leif ; Hanbury, Allan

Further information

• DOI: 10.1007/978-3-319-76941-7_45
• URN: urn:nbn:de:0168-ssoar-70719-0
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:28 AM CEST