A Comprehensive Framework for Ontology based Classifier using Unstructured Data
1Dr.M Thangaraj, Professor ,Department of Computer Science, School of Infor- mation Technology, Madurai Kamaraj University, India.
2Ms. M Sivakami, Research Scholar Department of Computer Science, School of Information Technology, Madurai Kamaraj University, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 6918-6925 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2042109119/2019©BEIESP | DOI: 10.35940/ijeat.A2042.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The knowledge contained within the natural language data can be used to build expert systems. Classifying unstructured data using ontology and text classification algorithms to extract information is one way of approaching the problem of building intelligent systems. One major problem with text processing is most data generated is unstructured and ambiguous, as, data with a structure helps to identify meaningful patterns and eventually exhibit the latent knowledge. Ambiguity in natural language affects accuracy of categorization. Also, Natural Language Processing techniques when combined with semantic data modeling through ontological knowledge will also solve the problem of domain knowledge representation thereby enabling improved data classification facilities, particularly in large datasets where number of features scale to unmanageable proportions. In this paper, the domain knowledge is presented as a knowledge graph, derived from the semantic data modeling. Further, to achieve better Multi Class classification, Multinomial Naive Bayes algorithm is applied to categorize items in their respective classes. For the experiments, Data about various news groups were used for testing the accuracy of the model. Experimental results have proved that the proposed classifier performs better compared to existing systems.
Keywords: Text categorization, Multiclass classification, Ontology, Knowledge Graphs, Feature Hashing, Topic Modeling.