This article presents a measure of semantic similarity in an IS-A taxonomy
based on the notion of shared information content. Experimental evaluation
against a benchmark set of human similarity judgments demonstrates that the
measure performs better than the traditional edge-counting approach. The
article presents algorithms that take advantage of taxonomic similarity in
resolving syntactic and semantic ambiguity, along with experimental results
demonstrating their effectiveness.

本文提出了一种基于信息内容共享概念的 IS-A 分类法中语义相似度的度量方法。实验评估结果表明，该方法的性能表现优于传统的边计数方法，并展示了利用分类相似性解决句法和语义歧义的算法及其有效性的实验结果。

分类系统中的语义相似性：一种基于信息的度量及其在自然语言歧义问题上的应用

Semantic Similarity in a Taxonomy: An Information-Based Measure and its  Application to Problems of Ambiguity in Natural Language

This paper presents a new measure of semantic similarity in an IS-A taxonomy,
based on the notion of information content. Experimental evaluation suggests
that the measure performs encouragingly well (a correlation of r = 0.79 with a
benchmark set of human similarity judgments, with an upper bound of r = 0.90
for human subjects performing the same task), and significantly better than the
traditional edge counting approach (r = 0.66).

本研究基于信息内容的概念，提出了一种新的 IS-A 分类法语义相似性的度量方式，并通过实验评估表明，该方式表现良好，与人类相似度判断数据相关系数为 0.79，显著优于传统的边缘计数方法。