Ontology based search result optimisation using singular matrix

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    In recent era, today a many firms share their service/product descriptions. With that, many meaningful information  in the textual form is hidden under the unstructured text. Algorithms like information extraction enable the identification of structured relations and they does not produce an optimal result and it is much costlier to operate with headlines of a text which has no examples of the target structured information. We propose a new approach which facilitates the formation of a structured metadata by recognizing documents which are likely to have some type and this information is to be subsequently used for both segregation and search process. Our approach is based on an idea that some attributes of a text will match with the query object which acts as identifier both for segregation as well as for storage and retrieval. Our implementation results show that our approach provides some superior results than existing approaches which rely only on query content or on textual information, to discover the key attributes.

  • Keywords

    Semantic Analysis, Segregation Index Creation And Recommender System.

  • References

      [1] R.T. Clemen and R.L. Winkler, “Unanimity and Compromiseamong Probability Forecasters,” Management Science, vol. 36,pp.767779,

      [2] C.D. Manning, P. Raghavan, and H. Schu¨ tze, Introduction toInformation Retrieval, first ed. Cambridge Univ. Press,

      [3] P.G. Ipeirotis, F. Provost, and J. Wang, “Quality Management onAmazon Mechanical Turk,” Proc. ACM SIGKDD Workshop Human Computation (HCOMP ’10), pp. 64-67,

      [4] R. Fagin, A. Lotem, and M. Naor, “Optimal AggregationAlgorithms for Middleware,” J. Computer Systems Sciences,vol. 66, pp. 614-656, http://portal.acm.org/citation. cfm?id=861182.861185, June 2003.

      [5] K.C.-C. Chang and S.-w. Hwang, “Minimal Probing: Supporting Expensive Predicates for Top-K Queries,” Proc. ACM SIGMOD Int’l Conf. Management Data, 2002.

      [6] G. Tsoumakas and I. Vlahavas, “Random K-Labelsets: An Ensemble Method for Multilabel Classification,” Proc. 18th European Conf. Machine Learning (ECML ’07), pp. 406-417,

      [7] M. Miah, G. Das, V. Hristidis, and H. Mannila, “Standing out in a Crowd: Selecting Attributes for Maximum Visibility,” Proc. Int’lConf. Data Eng. (ICDE), 2008.

      [8] P. Heymann, D. Ramage, and H. Garcia-Molina, “Social Tag Prediction,” Proc. 31st Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’08), pp. 531-538,

      [9] Y. Song, Z. Zhuang, H. Li, Q. Zhao, J. Li, W.-C. Lee, and C.L. Giles,“Real-Time Automatic Tag Recommendation,” Proc. 31st Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’08), pp. 515-522, http://doi.acm.org/10.1145/1390334.1390423, 2008.

      [10] D. Eck, P. Lamere, T. Bertin-Mahieux, and S. Green, “Automatic Generation of Social Tags for Music Recommendation,” Proc.Advances in Neural Information Processing Systems 20, 2008.

      [11] B. Sigurbjo¨rnsson and R. van Zwol, “Flickr Tag Recommendation Based on Collective Knowledge,” Proc. 17th Int’l Conf. World Wide Web (WWW ’08), pp. 327-336, http://doi.acm.org/10.1145/1367497.1367542, 2008.




Article ID: 8983
DOI: 10.14419/ijet.v7i1.3.8983

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.