A Fuzzy Ontology Framework for Customized Assessment of Semantic Similarity (original) (raw)

Fuzzy ontologies in semantic similarity measures

2016 IEEE Congress on Evolutionary Computation (CEC), 2016

Ontologies are a fundamental part of the development of short text semantic similarity measures. The most known ontology used within the field was developed from the lexical database known as WordNet which is used as a semantic resource for determining word similarity using the semantic distance between words. The original WordNet does not include in its hierarchy fuzzy words-those which are subjective to humans and often context dependent. The recent development of fuzzy semantic similarity measures requires research into the development of different ontological structures which are suitable for the representation of fuzzy categories of words where quantification of words is undertaken by human participations. This paper proposes two different fuzzy ontology structures which are based on a human quantified scale for a collection of fuzzy words across six fuzzy categories. The methodology of ontology creation utilizes human participants to populate fuzzy categories and quantify fuzzy words. Each ontology is evaluated within a known fuzzy semantic similarity measure and experiments are conducted using human participants and two benchmark fuzzy word datasets. Correlations with human similarity ratings show only one ontological structure was naturally representative of human perceptions of fuzzy words.

A Combined Fuzzy Semantic Similarity Measure in OWL Ontologies

2008

An algorithm is presented in this paper to calculate a semantic similarity measure inside an OWL ontology. The formulation is based on a combined measure taking into account the two most important aspects involved in the similarity computation. These are the structural properties of a concept, and the information content inside the ontology. We define a fuzzy system to blend these information sources with a training process over some ontologies. Finding a similarity measure between concepts of an ontology is a fundamental topic to accomplish information exchange on the Web. Through this measure it is possible to perform sophisticated queries over the web where the user is able to request concepts with a predefined similarity (or even dissimilarity) degree.

2007 IEEE International Fuzzy Systems Conference, 2007

Ontology reuse is an important research issue. Ontology merging, integration, mapping, alignment and versioning are some of its subprocesses. A considerable research work has been conducted on them. One common issue to these subprocesses is the problem of defining similarity relations among ontologies components. Crisp ontologies become less suitable in all domains in which the concepts to be represented have vague, uncertain and imprecise definitions. Fuzzy ontologies are developed to cope with these aspects. They are equally concerned with the problem of ontology reuse. Defining similarity relations within fuzzy context may be realized basing on the linguistic similarity among ontologies components or may be deduced from their intentional definitions. The latter approach needs to be dealt with differently in crisp and fuzzy ontologies. This is the scope of this paper.

Towards semantics-based ontology similarity

2007

Abstract. As the Semantic Web emerges the problem of semantic heterogeneity is becoming more acute. Ontology matching techniques aim at tackling this problem by establishing correspondences between elements of the ontologies. These techniques rely on distance metrics, often called (dis) similarity measures, to assess the similarity of elements within the ontologies. Most of these approaches are either terminological, structural and/or extensional.

European Conference on Information Systems, 2005

In this paper we present a comprehensive framework for measuring similarity within and between ontologies as a basis for the collaboration across various application fields. In order to define such a framework, we base our work on an abstract ontology model that allows to adhere to various existing and evolv- ing ontology standards. The main characteristics of the framework is

An ontology-driven similarity algorithm

2004

Abstract. This paper presents our similarity algorithm between relations in a user query written in FOL (first order logic) and ontological relations. Our similarity algorithm takes two graphs and produces a mapping between elements of the two graphs (ie graphs associated to the query, a subsection of ontology relevant to the query). The algorithm assesses structural similarity and concept similarity. An evaluation of our algorithm using the KMi Planet ontology 1 is presented.

2006

The paper proposes an approach to assess the semantic similarity among instances of an ontology. It aims to define a sensitive measurement of semantic similarity, which takes into account different hints hidden in the ontology definition and explicitly considers the application context. The similarity measurement is computed by analyzing, combining and extending some of the existing similarity measures and tailoring them according to the criteria induced by specific application context.

2006

The paper proposes a framework to assess the semantic similarity among instances within an ontology. It aims to define a sensitive measurement of semantic similarity, which takes into account different hints hidden in the ontology definition and explicitly considers the application context. The similarity measurement is computed by combining and extending existing similarity measures and tailoring them according to the criteria induced by the context. Experiments and evaluation of the similarity assessment are provided.

A Fuzzy Ontology Framework for Customized Assessment of Semantic Similarity (original) (raw)

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