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Collective Entity Disambiguation Based on Hierarchical Semantic Similarity
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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: Eric Pardede (La Trobe University, Australia)and Kiki Adhinugraha (La Trobe University, Australia)
DOI: 10.4018/IJDWM.2020040101
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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.
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