A STUDY OF DATA MINING TECHNIQUES FOR AUTOMATION ONTOLOGY

Computer Science

Ontologies have grown in popularity and renown on the semantic web as a result of its extensive usage in Internet-based applications. Ontologies are widely recognised as a useful source of semantics and interoperability in all artificially intelligent systems. In academic circles, the explosion of unstructured internet data has created an urgent need for automated ontology extraction from unstructured text. Several strategies using a range of techniques from diverse domains (machine learning, text mining, knowledge representation and reasoning, information retrieval, and natural language processing) are being presented to help automate the process of acquiring ontologies from unstructured text. — Semantic Data mining is a term used to describe activities that use domain knowledge, such as formal semantics, as part of the process. In the past, several studies have shown the importance of incorporating domain knowledge into data mining. A simultaneous development in Knowledge Engineering, notably formal semantics and Semantic Web ontologies, has widened the domain knowledge family yet more still.

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