Integrated framework for semantic text mining and ontology construction using inference engine

Purnachand Kollapudi and G. Narsimha

International Journal of Data Science, 2017, vol. 2, issue 2, 138-154

Abstract: Traditional clustering algorithms are generally either keyword or index based but not semantic based. These algorithms are facing difficulties in identifying synonymies or polysemies due to high dimensionality of text data. Ontologies are identified to overcome these difficulties. In this paper, we propose a framework which automates the extraction of concepts or terms with support of: a) our proposed metric called term rank identifier (TRI), it measures the frequent terms; b) semantically enriched terms (SETs) clustering algorithm, it calculates the semantic relation between the terms with Word net; c) Ontology Building can be done automatically for the concepts extracted from SET Clustering using inference engines. The experimental results show that our proposed metric TRI and SET clustering algorithm performed significantly.

Keywords: clustering; TRI; term rank identifier; SETs; semantically enriched terms; ontology; inference engine; semantic relation; text classification; semantic based; knowledge terms. (search for similar items in EconPapers)
Date: 2017
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