Developing type-2 fuzzy FCA for similarity reasoning in the semantic web

Safaeipour, Hoda; Zarandi, M. H. Fazel; Türkşen, I.B.

doi:10.1109/ifsa-nafips.2013.6608620

Cited by 4 publications

(4 citation statements)

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“… Information‐based approaches calculates statistical specification of concepts based on a corpus or other data sources . Some of common statistical specifications in the literature are concept frequencies, which refers to the number of occurrences in the corpus and co‐occurrences, which considers simultaneous occurrence of two concepts in the same file. FO‐based similarity assessment is mainly obtained by fuzzy FCA (FFCA). FFCA‐based approaches lie in feature‐based approaches since they consider common features of concepts in the lattice to assess their similarity.…”

Section: Discussionmentioning

confidence: 99%

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Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

Safaeipour

Zarandi

Bastani

2017

Int. J. Intell. Syst.

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Assessing semantic similarity is a fundamental requirement for many AI applications. Crisp ontology (CO) is one of the knowledge representation tools that can be used for this purpose. Thanks to the development of semantic web, CO‐based similarity assessment has become a popular approach in recent years. However, in the presence of vague information, CO cannot consider uncertainty of relations between concepts. On the other hand, fuzzy ontology (FO) can effectively process uncertainty of concepts and their relations. This paper aims at proposing an approach for assessing concept similarity based on FO. The proposed approach incorporates fuzzy relation composition in combination with an edge counting approach to assess the similarity. Accordingly, proposed measure relies on taxonomical features of an ontology in combination with statistical features of concepts. Furthermore, an evaluation approach for the FO‐based similarity measure named as FOSE is proposed. Considering social network data, proposed similarity measure is evaluated using FOSE. The evaluation results prove the dominance of proposed approach over its respective CO‐based measure.

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Section: Discussionmentioning

confidence: 99%

“…A highlighted work of this domain is Ref. , which has used FFCA method in combination with information content theory to assess similarity. Despite its popularity, FCA‐based approach is of high computational complexity.…”

Section: Discussionmentioning

confidence: 99%

Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

Safaeipour

Zarandi

Bastani

2017

Int. J. Intell. Syst.

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“…In [30] the need for IT2 fuzzy analytical systems for the development of the Semantic Web is emphasized, and a similarity measure for IFCA is proposed. It is based on the similarity measure for IT2 FSs defined in [35], the approach presented in [11], and relies on the experimental results given in [13].…”

Section: Related Workmentioning

confidence: 99%

“…In this subsection we address FCA contexts where grades of memberships are intervals, and in particular words. Indeed, words are closer to human judgment when we need to quantify "how much" an object is described by an attribute or, vice versa, an attribute applies to an object [5,30]. Possible words representing grades of membership are: {Fully, Very Much, Very, Few , Very Few , Not at all }.…”

Section: Fca With Interordinal Scalingmentioning

confidence: 99%

Concept Similarity in Formal Concept Analysis with Many-Valued Contexts

Formica

2021

cai

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Formal Concept Analysis (FCA) is a mathematical framework which can also support critical activities for the development of the Semantic Web. One of them is represented by Similarity Reasoning, i.e., the identification of different concepts that are semantically close, that allows users to retrieve information on the Web more efficiently. In order to model uncertainty information, in this paper FCA with many-valued contexts is addressed, where attribute values are intervals, which is referred to as FCA with Interordinal scaling (IFCA). In particular, a method for evaluating concept similarity in IFCA is proposed, which is a problem that has not been adequately investigated, although the increasing interest in the literature in this topic.

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Similarity reasoning in formal concept analysis: from one- to many-valued contexts

Formica

2018

Knowl Inf Syst

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Developing type-2 fuzzy FCA for similarity reasoning in the semantic web

Cited by 4 publications

References 25 publications

Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

Concept Similarity in Formal Concept Analysis with Many-Valued Contexts

Similarity reasoning in formal concept analysis: from one- to many-valued contexts

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