# Semantic Similarity Based on Taxonomies
###### Abstract  
The evaluation of the semantic similarity of concepts organized according to taxonomies is a long-standing problem in computer science and has attracted great attention from researchers over the decades. In this regard, the notion of information content plays a key role, and semantic similarity measures based on it are still on the rise. In this review, we address the methods for evaluating the semantic similarity between either concepts or sets of concepts belonging to a taxonomy that, often, in the literature, adopt different notations and formalisms. The results of this systematic literature review provide researchers and academics with insight into the notions that the methods discussed have in common through the use of the same notation, as well as their differences, overlaps, and dependencies, and, in particular, the role of the notion of information content in the evaluation of semantic similarity. Furthermore, in this review, a comparative analysis of the methods for evaluating the semantic similarity between sets of concepts is provided. semantic similarity; knowledge-based methods; taxonomy; information content +
Footnote †: journal: _Sci._  
002320333131959990  
methods, the types of entities to be compared are usually concepts or sets of concepts. In particular, semantic annotations of real-world resources, for instance, commercial products and scientific papers, are in general sets of concepts. Furthermore, in many cases, resources are tagged with sets of concepts belonging to a taxonomy. The approach of evaluating the similarity of resources by using semantic annotations is particularly useful in the case the content of the resources is not available. For instance, many scientific journals require categorizing articles with keywords from a classification system organized according to a taxonomy like the Association for Computing Machinery (ACM) Computing Classification System ([[https://dl.acm.org/ccs](https://dl.acm.org/ccs)]([https://dl.acm.org/ccs](https://dl.acm.org/ccs)), accessed on 29 October 2023), or the Physics Subject Headings classification system ([[https://physh.org/browse](https://physh.org/browse)]([https://physh.org/browse](https://physh.org/browse)), accessed on 29 October 2023). But while the keywords associated with the articles and classification systems are publicly available, the articles' contents are not. Then, in order to search for an article addressing given topics, taxonomy-based semantic similarity is a significant opportunity. Therefore, the objective of this paper is to provide an overview of taxonomy-based methods for computing the semantic similarity between either concepts or sets of concepts. Also, it has shown that taxonomy-based methods for computing the semantic similarity between sets of concepts rely on the ones to compute the similarity between concepts. Furthermore, since many of these methods use an information theoretic approach, the notion of information content for deriving the informativeness of a concept in a taxonomy, and methods for computing it are also introduced. The results of this review aim to provide researchers and academics with an insight into the notions that the methods proposed in the literature have in common, using the same notation, the differences of these methods, their overlaps and dependencies, and in particular the role of the notion of information content in assessing semantic similarity. The work is organized as follows. Section 2 introduces the reader to the notion of information content and the methods for computing its value. Sections 3 and 4 show the taxonomy-based methods for computing the similarity between concepts and sets of concepts, respectively. Section 5 focuses on one of the methods for evaluating the semantic similarity between sets of concepts, which, according to the current literature, outperforms the other mentioned approaches. Section 6 provides a discussion about the illustrated methods and also about the results of a recent experiment. Section 7 presents the conclusions and future directions.