[Objective] This paper aims to solve the ambiguity problems arising from mapping multiple entities of the same name with different meanings to a knowledge base. It improves the accuracy of entity disambiguation. [Methods] We proposed a multi-dimensional similarity fusion method. It utilizes the semantic similarity of entity context, the entity attributes' background similarity, and the topic words' semantic similarity to characterize entities. [Results] We examined the new model on the agricultural dataset from Wikipedia. The proposed method achieved an accuracy of 89.7%, outperforming traditional methods. [Limitations] The proposed method is only applicable in specific fields. [Conclusions] The new method addresses the entity disambiguation issues in specific fields. It can be applied to a broader range of entity disambiguation scenarios.
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Shi Shuiqian, Jin Jing, Shen Gengyu, Wang Baojia, Ren Ni. Chinese Named Entity Disambiguation Based on Multivariate Similarity Fusion. Data Analysis and Knowledge Discovery, 2024, 8(2): 56-64.
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