A Hybrid Modified Semantic Matching Algorithm Based on Instances Detection With Case Study on Renewable Energy

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-8 Number-1                          
Year of Publication : 2014
Authors :  Ahmad Khader Haboush
  10.14445/22315381/IJETT-V8P204

MLA 

Ahmad Khader Haboush ,"A Hybrid Modified Semantic Matching Algorithm Based on Instances Detection With Case Study on Renewable Energy", International Journal of Engineering Trends and Technology(IJETT), 8(1),14-23 February 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

This Matching input keywords with historical or information domain is an important point in modern computations in order to find the best match information domain for specific input queries. Matching algorithms represents hot area of researches in computer science and artificial intelligence. In the area of text matching, it is more reliable to study semantics of the pattern and query in terms of semantic matching. This paper improves the semantic matching results between input queries and information ontology domain. The contributed algorithm is a hybrid technique that is based on matching extracted instances from booth, the queries and in information domain. The instances extraction algorithm that is presented in this paper are contributed which is based on mathematical and statistical analysis of objects with respect to each other and also with respect to marked objects. The instances that are instances from the queries and information domain are subjected to semantic matching to find the best match, match percentage, and to improve the decision making process. An application case was studied in this paper which is related to renewable energy, where the input queries represents the customer requirements input and the knowledge domain is renewable energy vendors profiles. The comparison was made with most known recent matching researches.

References

[1] Zhibiao Wu and Martha Palmer. 1994. Verb semantics and lexical selection. In 32nd. Annual Meeting of the Association for Computational Linguistics, pages 133 –138, New Mexico State University, Las Cruces, New Mexico.
[2] Fei Wu and Dan Weld. 2007. Autonomously semantifying Wikipedia. In Proceedings of the International Conference on Information and Knowledge Management (CIKM)
[3] Kedar Bellare and Andrew McCallum. 2007. Learning extractors from unlabeled text using relevant databases. In Proceedings of the Sixth International Workshop on Information Extraction on the Web.
[4] Mike Mintz, Steven Bills, Rion Snow, and Daniel Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics
[5] O. Etzioni, M. J. Cafarella, D. Downey, A.-M. Popescu, T. Shaked, S. Soderland, D. S. Weld, and A. Yates. Unsupervised named-entity extraction from the web: An experimental study. Artif. Intell., 165(1):91{134, 2005.
[6] M. A. Hearst. Automatic acquisition of hyponyms from large text corpora. In In Proceedings of the 14th International Conference on Computational Linguistics, pages 539{545, 1992.
[7] Zornitsa Kozareva, Ellen Riloff, and Eduard Hovy. 2008. Semantic class learning from the web with hyponym pattern linkage graphs. In Proceedings of ACL-08: HLT, pages 1048–1056, Columbus, Ohio, June. Association for Computational Linguistics.
[8] M. Pa»sca. Organizing and searching the world wide web of facts { step two: harnessing the wisdom of the crowds. In WWW '07: Proceedings of the 16thinternational conference on World Wide Web, pages 101{110, New York, NY, USA, 2007. ACM.
[9] R. C. Wang, N. Schlaefer, W. W. Cohen, and E. Nyberg. Automatic set expansion for list question answering. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 947{954, Honolulu, Hawaii, October 2008. Association for Computational Linguistics.
[10] Ahmad Kayed Mohammad Nizar, Mohammed Alfayoumi Ontology Concepts for Requirements Engineering Process in E-Government Applications [Conference]. - [s.l.] : IEEE, 2010.
[11] Zornitsa Kozareva. 2006. Bootstrapping named entity recognition with automatically generated gazetteer lists. In EACL. The Association for Computer Linguistics.
[12] Marius Pas¸ca. 2007a. Organizing and searching the world wide web of facts – step two: harnessing the wisdom of the crowds. In WWW ’07: Proceedings of the 16th international conference on World Wide Web, pages 101–110, New York, NY, USA. ACM.
[13] Rion Snow, Daniel Jurafsky, and Andrew Y. Ng. 2006. Semantic taxonomy induction from heterogenous evidence. In ACL ’06: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL, pages 801–808, Morristown, NJ, USA. Association for Computational Linguistics.
[14] Richard C. Wang and William W. Cohen. 2007. Language-independent set expansion of named entities using the web. In ICDM, pages 342–350. IEEE Computer Society.
[15] Richard C. Wang and William W. Cohen. 2008. Iterative set expansion of named entities using the web. In ICDM, pages 1091–1096. IEEE Computer Society.
[16] Satanjeev Banerjee and Ted Pedersen. 2003. Extended gloss overlaps as a measure of semantic relatedness. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pages 805–810.
[17] Long Qiu, Min-Yen Kan, and Tat-Seng Chua. 2006. Paraphrase recognition via dissimilarity significance classification. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 18–26, Sydney, Australia, July. Association for Computational Linguistics.

Keywords
Semantic matching; Instances extraction; Decision making; Ontology; Renewable energy