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Gauzy Knowledge Sharing In Conspiring Environment using Text Mining

Affiliations

  • Department of Information Technology, Sathyabama University, Chennai – 600119, Tamil Nadu, India

Abstract


Objective: The people all over the world are in need of gaining knowledge which is achieved by means of searching information through web services. Even though search results obtained from web services are relevant, the user is trying to obtain the best information providers. Methods: The existing approaches which is non-parametric generative model and infinite Hidden Markov Model (iHMM) of clustering technique does not yield better results for very large collections of web services. The iHMM model does not provide any security for datasets in a hierarchal structure. Thus to overcome such downsides of existing approach to a new technique can be used to help of Natural Language Processing (NLP). Findings: The new approach provides both tag recommendation and parental control. The tag recommendation is used to ping the best tags based on expert’s bookmarks, and parental control provided by group owner is used to resolve the privacy issues. The web filter denies the unauthorized access. Thus, using NLP technique in the conspiring environment enables the web user to achieve fine grained knowledge. Applications: The paper defines the efficient way to provide tag recommendation on the web for large dataset users.

Keywords

Bookmarks, NLP, Tag Recommendations, Tag Suppression, Text Mining, Web Services.

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References


  • Nagarajan S, Chandrasekaran RM. Design and implementation of expert clinical system for diagnosing diabetes using data mining techniques. Indian Journal of Science and Technology. 2015 Apr; 8(8):771–6. DOI: 10.17485/ijst/2015/v8i8/69272.
  • Beal M J, Ghahramani Z, Rasmussen CE. The infinite hidden Markov model. Proceedings Advances in Neural Information Process Systems; 2001. p. 1–3.
  • Guan Z, Yang S, Sun H, Srivatsa M, Yan X. Fine-grained knowledge sharing in collaborative environments. IEEE Transactions on Knowledge and Data Engineering. 2014; 27(8):2163–74.
  • Liu X, Croft WB, Koll M. Finding experts in community-based question-answering services. Proceedings 14th ACM International Conference on Information Knowledge Management; 2005. p. 315–16.
  • Weng C, Miotto R. Unsupervised mining of frequent tags for clinical eligibility text indexing Journal of Biomedical Informatics. 2013; 46(6): 1145–51.
  • Balog K, Azzopardi L, de Rijke M. Formal models for expert finding in enterprise corpora. Proceedings 29th Annual International ACM SIGIR Conference onResearch Development in Information Retrieval; 2006. p.43–50.
  • Fang Y, Si L, Mathur AP. Discriminative models of integrating document evidence and document-candidate associations for expert search. Proceedings 33rd Annual International ACM SIGIR Conference on Research Development Information Retrieval; 2010. p. 683–90 .
  • White RW, Bailey P, Chen L. Predicting user interests from contextual information. Proceedings 32nd Annual International ACM SIGIR Conference Research Developments Information Retrieval; 2009. p. 363–70.
  • Yagnik S, Thakkar P, Kotecha K. Recommending tags for new resource in social bookmarking system. International Journal of Data Mining and Knowledge Management Process. 2014; 4(1):19–32 .
  • Parra-Arnau J, Perego A, Ferrari E, Forne J, Rebollo-Monedero D. Privacy-preserving enhanced collaborative tagging. IEEE Transactions on Knowledge and Data Engineering. 2014; 26(1):180–93.
  • Serdyukov P, Rode H, Hiemstra D. Modeling multi-step relevance propagation for expert finding. Proceedings of the 17th ACM Conference on Information Knowledge Management; 2008. p. 1133–42.

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