Welcome to the InfoSci Platform
Reference Hub3
Collective Entity Disambiguation Based on Hierarchical Semantic Similarity

Collective Entity Disambiguation Based on Hierarchical Semantic Similarity

Bingjing Jia, Hu Yang, Bin Wu, Ying Xing
Copyright: © 2020 |Volume: 16 |Issue: 2 |Pages: 17
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799804987|DOI: 10.4018/IJDWM.2020040101
Cite Article Cite Article

MLA

Jia, Bingjing, et al. "Collective Entity Disambiguation Based on Hierarchical Semantic Similarity." IJDWM vol.16, no.2 2020: pp.1-17. http://doi.org/10.4018/IJDWM.2020040101

APA

Jia, B., Yang, H., Wu, B., & Xing, Y. (2020). Collective Entity Disambiguation Based on Hierarchical Semantic Similarity. International Journal of Data Warehousing and Mining (IJDWM), 16(2), 1-17. http://doi.org/10.4018/IJDWM.2020040101

Chicago

Jia, Bingjing, et al. "Collective Entity Disambiguation Based on Hierarchical Semantic Similarity," International Journal of Data Warehousing and Mining (IJDWM) 16, no.2: 1-17. http://doi.org/10.4018/IJDWM.2020040101

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.