Fuzzy Online Reputation Analysis Framework

Portmann, Edy; Nguyen, Tam; Sepúlveda, José; Cheok, Adrian David

doi:10.1007/978-3-642-33233-3_6

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…FO is a generalization of crisp ontology (CO) where fuzzy relations exist between crisp concepts. FO have been successfully implemented in several application areas such as news summarization, diet recommendation, flight booking, information retrieval, reputation management, collision avoidance, and knowledge mobilization …”

Section: Introductionmentioning

confidence: 99%

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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