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JACIII Vol.25 No.3 pp. 326-334
doi: 10.20965/jaciii.2021.p0326
(2021)

Paper:

Representation Learning with LDA Models for Entity Disambiguation in Specific Domains

Shengchen Jiang*,**, Yantuan Xian*,**, Hongbin Wang*,**,†, Zhiju Zhang*,**, and Huaqin Li*,**

*Faculty of Information Engineering and Automation, Kunming University of Science and Technology
No.727 Jingming South Road, Chenggong New Area, Kunming, Yunnan 650504, China

**Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology
No.727 Jingming South Road, Chenggong New Area, Kunming, Yunnan 650504, China

Corresponding author

Received:
July 6, 2020
Accepted:
February 25, 2021
Published:
May 20, 2021
Keywords:
entity disambiguation, doc2vec, LDA topic model, k-means algorithm, domain knowledge base
Abstract
Representation Learning with LDA Models for Entity Disambiguation in Specific Domains

Structure of entity disambiguation representation learning model based on LDA model

Entity disambiguation is extremely important in knowledge construction. The word representation model ignores the influence of the ordering between words on the sentence or text information. Thus, we propose a domain entity disambiguation method that fuses the doc2vec and LDA topic models. In this study, the doc2vec document is used to indicate that the model obtains the vector form of the entity reference item and the candidate entity from the domain corpus and knowledge base, respectively. Moreover, the context similarity and category referential similarity calculations are performed based on the knowledge base of the upper and lower relation domains that are constructed. The LDA topic model and doc2vec model are used to obtain word expressions with different meanings of polysemic words. We use the k-means algorithm to cluster the word vectors under different topics to obtain the topic domain keywords of the text, and perform the similarity calculations under the domain keywords of the different topics. Finally, the similarities of the three feature types are merged and the candidate entity with the highest similarity degree is used as the final target entity. The experimental results demonstrate that the proposed method outperforms the existing model, which proves its feasibility and effectiveness.

Cite this article as:
Shengchen Jiang, Yantuan Xian, Hongbin Wang, Zhiju Zhang, and Huaqin Li, “Representation Learning with LDA Models for Entity Disambiguation in Specific Domains,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.3, pp. 326-334, 2021.
Data files:
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Last updated on Jun. 22, 2021