IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Collective Entity Disambiguation Based on Hierarchical Semantic Similarity

Collective Entity Disambiguation Based on Hierarchical Semantic Similarity
View Sample PDF
Author(s): Bingjing Jia (Beijing University of Posts and Telecommunications and Anhui Science and Technology University, Beiging and Huainan, Anhui, China), Hu Yang (Beijing University of Posts and Telecommunications, Beijing China), Bin Wu (Beijing University of Posts and Telecommunications, Beijing, China) and Ying Xing (Beijing University of Posts and Telecommunications, Beijing, China)
Copyright: 2020
Volume: 16
Issue: 2
Pages: 17
Source title: International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: David Taniar (Monash University, Australia)
DOI: 10.4018/IJDWM.2020040101

Purchase

View Collective Entity Disambiguation Based on Hierarchical Semantic Similarity on the publisher's website for pricing and purchasing information.

Abstract

Entity disambiguation involves mapping mentions in texts to the corresponding entities in a given knowledge base. Most previous approaches were based on handcrafted features and failed to capture semantic information over multiple granularities. For accurately disambiguating entities, various information aspects of mentions and entities should be used in. This article proposes a hierarchical semantic similarity model to find important clues related to mentions and entities based on multiple sources of information, such as contexts of the mentions, entity descriptions and categories. This model can effectively measure the semantic matching between mentions and target entities. Global features are also added, including prior popularity and global coherence, to improve the performance. In order to verify the effect of hierarchical semantic similarity model combined with global features, named HSSMGF, experiments were carried out on five publicly available benchmark datasets. Results demonstrate the proposed method is very effective in the case that documents have more mentions.

Related Content

Fatma Abdelhedi, Amal Ait Brahim, Gilles Zurfluh. © 2021. 14 pages.
Sami Belkacem, Kamel Boukhalfa. © 2021. 24 pages.
Abdelilah Balamane. © 2021. 18 pages.
I.Jeena Jacob, Betty Paulraj, P. Ebby Darney, Hoang Viet Long, Tran Manh Tuan, Harold Robinson Yesudhas, Vimal Shanmuganathan, Golden Julie Eanoch. © 2021. 17 pages.
Neha Gupta, Sakshi Jolly. © 2021. 18 pages.
Christie I. Ezeife, Vignesh Aravindan, Ritu Chaturvedi. © 2020. 21 pages.
Diego Vilela Monteiro, Rafael Duarte Coelho dos Santos, Karine Reis Ferreira. © 2020. 17 pages.
Body Bottom