Rule-Based Semantic Content Extraction in Image using Fuzzy Ontology


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Abstract


The combinations of different concepts with respect to its associative relationship, in a quantitative sense is said to be valid information knowledge base. Relevant information retrieval through cognitive process is our main objective of this paper. In this paper, the concept of information retrieval was explored and visualized with respect to handling the uncertain knowledge of World Wide Web. In order to narrow the searches in the web, the semantic plays an important role. In this paper, a concept of fuzzy ontology is introduced with respect to textual and image search. The specification of concept with its relation in an organized way is said to be ontology. Here, we use Cognition-based ontology for information retrieval, which can be implemented in a semantic-based information retrieval system. For textual search, a combination of formal context and ontology were used, whereas for image search, low-level feature determination and fuzzy membership function mapping were used. In this paper, the domain concept of basketball game was considered. For this game, the actions are listed and respective image features are linked with respect to Type-2 level fuzzy metrics. To extract information from the created ontology, probability rule-based reasoning techniques are used for ontology learning and reasoning. This rule-based method is enriched by Fuzzy learning system
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Keywords


Ontology; Fuzziness; Concept Learning; Knowledge Representation and Formalization; Intelligent System; OWL; Fuzzy Ontology

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References


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