Semantic similarity between concepts concerns expressing the degree of similarity in meaning between two concepts in a computational model. This problem has recently attracted considerable attention from researchers in attempting to automate the understanding of word meanings to expedite the classification of users’ opinions and attitudes embedded in text. In this article, a semantic similarity metric is presented. The proposed metric, namely, weighted information-content (wic), exploits the information content of the least common subsumer of two compared concepts and the depth information in knowledge graphs such as DBPedia and YAGO. The two similarity components were combined using calibrated cooperative contributions from both similarity components. A statistical test using the Spearman correlations on well-known human judgement word-similarity data sets showed that the wic metric produced more highly correlated similarities compared with state-of-the-art metrics. In addition, a real-world aspect category classification was evaluated, which exhibited further increased accuracy and recall.

概念之间的语义相似性涉及表达计算模型中两个概念之间意义的相似程度。这个问题最近引起了研究人员的极大关注，试图自动理解词义以加快对嵌入文本中的用户意见和态度的分类。在本文中，提出了语义相似性度量。建议的度量，即加权信息内容（wic)，利用了两个比较概念的最不常见的包含者的信息内容和知识图中的深度信息，如 DBPedia 和 YAGO。使用来自两个相似性分量的校准合作贡献来组合这两个相似性分量。在著名的人类判断词相似性数据集上使用 Spearman 相关性进行的统计测试表明，与最先进的指标相比，wic指标产生了更多高度相关的相似性。此外，评估了真实世界的方面类别分类，这进一步提高了准确性和召回率。