Semantic Similarity from Natural Language and Ontology Analysis

Abstract

Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli.In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning -intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-theart approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesaurus or ontologies.Semantic measures are widely used today to compare units of language, concepts, instances, or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterisation of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity towards the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.keywords: Semantic similarity, semantic relatedness, semantic measures, distributional measures, domain ontology, knowledge-based semantic measure.iii iv

DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors

“…Features are generated using a broad range of techniques (Anam et al, 2015;Harispe et al, 2015), ranging from the exploitation of terminological information, including structural similarities and logical constraints, such as datatype properties, cardinality constraints, etc.…”

A Novel Approach to Semantic Similarity Measurement Based on a Weighted Concept Lattice: Exemplifying Geo-Information

“…In Equation (23), sim (a, b)/sim (a, a) represents the relative proportion of the absolute semantic similarity in the concept a. The range of this parameter is from negative infinity to one, which means that sim (a, b)/sim (a, a) ∈ (−∞, 1].…”

“…In this specific field, four main sets of approaches are used to achieve this goal [23]. The most popular method is the structural approach which uses the network model and relies on graph traversal.…”

“…However, the aforementioned approaches have several limitations. All these approaches require a taxonomy or ontology structure describing the elements to compare [23]. The structural approaches require knowledge to be modeled in a specific manner in the graph and are not designed to take non-binary relationships into account.…”

An Approach to Measuring Semantic Relatedness of Geographic Terminologies Using a Thesaurus and Lexical Database Sources

“…Semantic similarity/relatedness measures are used to solve problems in a broad range of applications and domains. The domains of application include: (i) Natural Language Processing, (ii) Knowledge Engineering/Semantic Web and Linked Data [5], (iii) Information retrieval, (iv) Artificial intelligence [6], and so on. In this article, to accurately present our research, our study is restricted to semantic relatedness.…”