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Computing Semantic Similarity of Concepts in Knowledge Graphs

机译:计算知识图中概念的语义相似度

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This paper presents a method for measuring the semantic similarity between concepts in Knowledge Graphs (KGs) such as WordNet and DBpedia. Previous work on semantic similarity methods have focused on either the structure of the semantic network between concepts (e.g., path length and depth), or only on the Information Content (IC) of concepts. We propose a semantic similarity method, namely wpath, to combine these two approaches, using IC to weight the shortest path length between concepts. Conventional corpus-based IC is computed from the distributions of concepts over textual corpus, which is required to prepare a domain corpus containing annotated concepts and has high computational cost. As instances are already extracted from textual corpus and annotated by concepts in KGs, graph-based IC is proposed to compute IC based on the distributions of concepts over instances. Through experiments performed on well known word similarity datasets, we show that the wpath semantic similarity method has produced a statistically significant improvement over other semantic similarity methods. Moreover, in a real category classification evaluation, the wpath method has shown the best performance in terms of accuracy and F score.
机译:本文提出了一种用于测量知识图(KG)中的概念(如WordNet和DBpedia)之间语义相似性的方法。先前关于语义相似性方法的工作集中于概念之间的语义网络的结构(例如,路径长度和深度),或者仅关注概念的信息内容(IC)。我们提出一种语义相似性方法,即wpath,将这两种方法结合起来,使用IC加权概念之间的最短路径长度。传统的基于语料库的IC是根据概念在文本语料库上的分布来计算的,这是准备包含注释概念的领域语料库所需要的,并且具有很高的计算成本。由于已经从文本语料库中提取了实例,并由KG中的概念进行了注释,因此提出了基于图的IC,以基于概念在实例上的分布来计算IC。通过对众所周知的单词相似性数据集进行的实验,我们表明wpath语义相似性方法比其他语义相似性方法产生了统计学上的显着改进。此外,在真实类别分类评估中,wpath方法在准确性和F得分方面表现出最佳性能。

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