基于潜在语义索引的Wikidata机构实体聚类研究

Clustering Wikidata’s Organizational Entities with Latent Semantic Index

【目的】Wikidata机构类目范畴树中,存在实例数目过多而使类目的外延过大、不能明确指示及类分资源的问题。为系统化机构名称层级体系,需将这些实例进行划分,使其均衡分布在机构范畴树的各层。【方法】将无监督的层次聚类算法用于解决无类别标签的机构实例的自动聚簇问题。为消除机构实体名称中特征词共现对聚类算法的影响,引入Wikidata中机构实体的相关属性作为其上下文环境。同时聚类算法对数据的维度十分敏感,因此采用潜在语义索引作为文本表示模型,通过奇异值分解将高维数据映射到潜在的低维语义空间。【结果】本文方法在实验数据集上的聚类准确率达到87.3%,取得了较好的聚类效果。【局限】仅在小样本数据集上进行验证。【结论】为机构名称提供上下文环境有利于同类机构的聚集,基于潜在语义索引模型的层次聚类算法对于高维度的文本聚类问题是有效的。

[Objective]This paper proposes a model to classify institutions in Wikidata’s category trees,aiming to better organize these entities.[Methods]We used an unsupervised hierarchical clustering algorithm to automatically cluster the institutional instances without proper tags.To eliminate the influence of the co-occurring feature words,we introduced the relevant attributes of the organizational entities in Wikidata.The clustering algorithm is sensitive to the data dimensions,hence,used the Latent Semantic Index to represent the texts.We also mapped the high-dimensional data to the potential low-dimensional semantic spaces through the singular value decomposition.[Results]The accuracy rate of the proposed clustering method on the experimental dataset reached 87.3%.[Limitations]The sample data sets need to be expanded.[Conclusions]The proposed model could effectively aggregate names of similar institutions and address the clustering issues of high-dimensional texts.