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Semantic Information Processing into Enriched WordNet Using Word Sense Disambiguation

D. Sindhu Suvitha, R. Janarthanan

Abstract


The Semantic Web is a mesh of information linked up
in such a way as to be easily processable by machines, on a global scale. It is an evolving extension of the World Wide Web in which the semantics of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. Semantic information processing is used to construct knowledge base at the human level The most fundamental step in semantic information processing (SIP is to construct knowledge base (KB) at the human level. WordNet has been built to be the most systematic and as close to the human leveland is being applied actively in various works. Consequently, search results corresponding to different meanings may be retrieved, making identifying relevant results inconvenient and time-consuming. It has been found that a semantic gap exists between concept pairs ofWordNet and those of real world. A study on the enrichment methodto build a Knowledge Base was proposed here. A rule based methodusing WordNet‟s glossaries and an inference method using axioms for WordNet relations are applied for the enrichment and an enriched WordNet (E-WordNet) is built as the result. Moreover, WSDSemNet a new word sense disambiguation method in which EWordNet is applied for Semantic Information Processing.


Keywords


Knowledge Base Management, Text Analysis, Dictionaries, Semantic Relation Networks

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References


R. Hemayati, W. Meng, and C. Yu, “Semantic-Based Grouping of

Search Engine Results Using WordNet,” Advances in Data and Web

Management, pp. 678-686, 2007, doi: 10.1007/11925231.

S. Liu, F. Liu, C. Yu, and W. Meng, “An Effective Approach to

Document Retrieval via Utilizing WordNet and Recognizing Phrases,”

Proc. Conf. Special Interest Group on Information Retrieval (SIGIR

‟04), pp. 266-272, 2004, doi:10.1145/1008992.1009039.

R. Navigli and P. Velardi, “Structural Semantic Interconnections: A

Knowledge-Based Approach to Word Sense Disambiguation,” IEEE

Trans. Pattern Analysis and Machine Intelligence, special issue on

syntactic and structural pattern recognition, vol. 27, no. 7, pp. 1075-

, July 2005, doi:10.1109/TPAMI.2005.149.

K. Toutanova and C. Manning,“Enriching the Knowledge Sources Used

in a Maximum Entropy Part-of-Speech Tagger,” Proc. Joint SIGDAT

Conf. Empirical Methods in Natural Language Processing and Very

Large Corpora (EMNLP/VLC ‟00), pp. 63-70, 2000,

doi:10.3115/1117794.1117802.

M. Missikoff, P. Velardi, and P. Fabriani, “Text Mining Techniques to

Automatically Enrich a Domain Ontology,” Applied Intelligence, vol.

, no. 3, pp. 323-340, 2003, doi: 10.1023/A: 1023254205945.

M.G. Hwang, C. Choi, B. Youn, and P.K. Kim, “Word Sense

Disambiguation Based on Relation Structure,” Proc. Seventh Int‟l Conf.

Advanced Language Processing and Web Information Technology

(ALPIT ‟08), pp. 15-20, 2008, doi: 10.1109/ALPIT.2008.52.

M.G. Hwang and P.K. Kim, “Adapted Relation Structure Algorithm for

Word Sense Disambiguation,” Proc. Third Int‟l Conf. Digital

Information Management (ICDIM ‟08), pp. 684-688, 2008,

doi:10.1109/ICDIM.2008.4746825.

M. Saiz-Noeda, A. Suarez, and M. Palomar, “Semantic Pattern Learning

through Maximum Entropy-Based WSD Technique,” Proc. Workshop

Computational Natural Language Learning, 2001, doi:

3115/1117822. 1455624


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