Collective Entity Disambiguation Based on Hierarchical Semantic Similarity

Bingjing Jia, Hu Yang, Bin Wu and Ying Xing
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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
Ying Xing: Beijing University of Posts and Telecommunications, Beijing, China

International Journal of Data Warehousing and Mining (IJDWM), 2020, vol. 16, issue 2, 1-17

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.

Date: 2020
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