On Measuring Similarity for Conceptual Querying

Bulskov, Henrik; Knappe, Rasmus; Andreasen, Troels

doi:10.1007/3-540-36109-x_8

Cited by 45 publications

(33 citation statements)

References 4 publications

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“…sim(a,b) = sim(b,a)). An asymmetric similarity measure has proposed in [19]. It argued that while concept inclusion (is-a) intuitively implies strong similarity in the opposite direction of inclusion (specialization), the direction of inclusion (generalization) must contribute with some degree of similarity.…”

Section: Ontological Semantic Similarity Methodsmentioning

confidence: 99%

Enhancing Keyword Search Over Relational Databases Using Ontologies

Elsayed¹,

Eldin²,

Zanfaly³

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Section: Ontological Semantic Similarity Methodsmentioning

confidence: 99%

Enhancing Keyword Search Over Relational Databases Using Ontologies

Elsayed¹,

Eldin²,

Zanfaly³

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…. Note that the penalisation of paths inducing complex semantics, e.g., which involves multiple types of relationships, was already introduced in (Hirst and St-Onge, 1998;Bulskov et al, 2002).…”

Section: Structural Approachmentioning

confidence: 99%

Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

156

102

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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

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“…SP: Shortest Path [56]. This measure is based on the simple observation that the closer two nodes k, r ∈ C, the more similar they result.…”

Section: Disease Semantic Similaritiesmentioning

confidence: 99%