Semantic similarity assessment between concepts is an important task in many language related applications. In the past, many approaches to assess similarity of concepts have been proposed by using one knowledge source. In this paper, some limitations of the existing similarity measures are identified. To tackle these problems, we propose an extensive study for semantic similarity of concepts from which a unified framework for semantic similarity computation is presented. Based on our framework, we give some generic and flexible approaches to semantic similarity measures resulting from instantiations of the framework. In particular, we obtain some new approaches to similarity measures that existing methods cannot deal with by introducing multiple knowledge sources. The evaluation based on eight benchmarks, three widely used benchmarks (i.e., M&C, R&G, and WordSim-353 benchmarks) and five benchmarks developed in ourselves (i.e, Jiang-1, Jiang-2, Jiang-3, Jiang-4, and Jiang-5 benchmarks), sustains the intuitions with respect to human judgements. Overall, some methods proposed in this paper have a good human correlation (Pearson correlation with human judgments and Spearman correlation with human judgments) and constitute some effective ways of determining semantic similarity between concepts.

概念之间的语义相似性评估是许多语言相关应用程序中的一项重要任务。过去，已经提出了许多通过使用一个知识源来评估概念相似性的方法。在本文中，确定了现有相似性度量的一些局限性。为了解决这些问题，我们提出了对概念语义相似性的广泛研究，从中提出了语义相似性计算的统一框架。基于我们的框架，我们提供了一些通用且灵活的方法来衡量由框架实例化产生的语义相似性度量。特别是，我们通过引入多个知识源获得了一些现有方法无法处理的相似性度量的新方法。评估基于八个基准，三个广泛使用的基准（即 M& C、R&G 和 WordSim-353 基准）和我们自己开发的五个基准（即，Jiang-1、Jiang-2、Jiang-3、Jiang-4 和 Jiang-5 基准），维持了对人类判断的直觉. 总体而言，本文提出的一些方法具有良好的人类相关性（Pearson与人类判断相关和Spearman与人类判断相关），构成了一些确定概念之间语义相似性的有效方法。