Total views : 192

Identifying the Impact of Semantic Similarity in Semantic Social Media through Evolving Term for Effective Web Mining

Affiliations

  • Department of Computer Science and Engineering, Bharath University, Chennai - 600073, Tamil Nadu, India
  • Department of Computer Science and Engineering, Dhanalakshmi College Engineering, Manimangalam - 601301, Tamil Nadu, India

Abstract


Objective: Computing semantic similarity is a significant objective in Information Retrieval (IR) due to the emerging web searching process and the demand of fast and efficient result provisioning. Methods: Exploiting static lexical resources leads most of the conventional semantic similarity measurement techniques to ignore temporal aspects of concepts. Later, to resolve the temporal constraint, the research works exploit independent sources like Newspaper articles, television documentaries, and books. However, these methods meet the data-sparsity problem, since the independent sources are too restrictive and exclude articles that are worthy of notice. Findings: In order to provide the precise result for recent trend concepts, this paper utilizes the social media as the robust and dynamic source, often reflect public opinion. This work introduces an ideNtifying nEw concept Evolution thRough semantIC Social media temPoral analYsis (NEOTERICSPY) model incorporating two phases, namely semantic relatedness measurement in a time series and examining temporal fluctuations between time series. Initially, the NEOTERIC-SPY measures the semantic relatedness of a word-pair using normalized distance measurement in a time series and facilitates the identification of temporal fluctuations. It reduces the word-pair list of a concept among the overall possible word-pairs and provides a good trade-off between the execution time and accuracy. Secondly, it identifies the new concepts using temporal correlation analysis of the semantic similarity measurement between the time series. It employs the weighing function of the temporal dynamics to find the recent changes than the ancient changes of the concepts. The experimental results show that the NEOTERIC-SPY approach significantly outperforms the conventional method and illustrates the consistent improvements of its performance. Application/ Improvements: The NEOTERIC-SPY leads the IR system to provide the desired information to the users in a short-time period, ensuring the precise result pertaining to the recent concept.

Keywords

Semantic Similarity, Social Media, Temporal.

Full Text:

 |  (PDF views: 147)

References


  • Bar-Yossef Z, Gurevich M. Random sampling from a search engine's index. Proceedings of 15th International World Wide Web Conference; 2006. p. 1–10.
  • Wan X, Yang J, Xiao J. Towards a unified approach to document similarity search using manifold-ranking of block. Elsevier Transaction on Information Processing and Management. 2008; 44(3):1032–48.
  • Bollegala D, Matsuo Y, Ishizuka M. Measuring semantic similarity between words using web search engines. WWW '07 Proceedings of the 16th international conference on World Wide Web; 2007. p. 757–66.
  • Eytan A, Teevan J, Dumais ST, Elsas JL. The web changes everything: Understanding the dynamics of web content. ACM Proceedings of the Second ACM International Conference on Web Search and Data Mining, USA; 2009. p. 282–91.
  • Deerwester S, Dumais S, Furnas G, Landauer T, Harshman R. Indexing by latent semantic analysis. Journal of the American Society for Information Science. 1990; 41(6):391–407.
  • Mller ZTC, Gurevych I. Using wiktionary for computing semantic relatedness. Proceedings of the Twenty-Third. AAAI Conference on Artificial Intelligence; 2008. p. 861–6.
  • Gabrilovich E, Markovitch S. Computing semantic relatedness using wikipedia-based explicit semantic analysis. Proceedings of the 20th International Joint Conference on Artificial Intelligence, San Francisco; 2007. p. 1606–11.
  • Kalthoum R, Mhiri H, Ghédira K. Theoretical formulas of semantic measure: A survey. Journal of Emerging Technologies in Web Intelligence. 2013; 5(4):333–42.
  • Asur S, Huberman BA, Szbao G, Wang C. Trends in social media: Persistence and decay, International Conference on Web and Social Media; 2011. p. 434–7.
  • Budanitsky A, Hirst G. Evaluating wordnet-based measures of lexical semantic relatedness. Computational Linguistics. 2006; 32(1):13–47.
  • Banerjee S, Pedersen T. Extended gloss overlaps as a measure of semantic relatedness, International Joint Conference on Artificial Intelligence; 2003. p. 805–10.
  • Strube M, Ponzetto SP. WikiRelate! Computing semantic relatedness using Wikipedia. Association for the Advancement of Artificial Intelligence; 2006. p. 1419–24.
  • Soleimandarabi MN, Mirroshandel SA. A novel approach for computing semantic relatedness of geographic terms. Indian Journal of Science and Technology. 2015; 8(27):1–11.
  • Meenakshi A, Suganthi P, Aghila R, Nirmala S. Information retrieval using dynamic decision quadtree in soil database. Indian Journal of Science and Technology. 2016; 9(10):1–7.
  • Matsuo Y, Mori J, Hamasaki M, Ishida K, Nishimura T, Takeda H, Hasida K, Ishizuka M. Polyphonet: An advanced social network extraction system. Elsevier Transaction on the Proceedings of 15th International World Wide Web Conference. 2006; 5(4):262–78.
  • Mika P. Ontologies are us: A unified model of social networks and semantics. Springer Transaction on the Proceedings of ISWC; 2005. p. 522–36.
  • Liu X, Zhou Y, Zheng R. Sentence similarity based on dynamic time warping. International Conference on Semantic Computing; 2007. p. 250–6.
  • Lin L, Hu X, Hu X, Wang J, Zhou YM. Measuring sentence similarity from different aspects. International Conference on Machine Learning and Cybernetics. 2009; 4:2244–9.
  • Sahu SK, Mohapatra DP, Balabantaray RC. Information retrieval in the context of checking semantic similarity in web: Vision of Future Web. Indian Journal of Science and Technology. 2016; 9(32):1–12.
  • Vigneshwari S, Aramudhan M. Social information retrieval based on semantic annotation and hashing upon the multiple ontologies. Indian Journal of Science and Technology. 2015; 8(2):1–5.
  • Vagena Z, Vlachos M, Meek C, Gunopulos D. Identifying similarities, periodicities and bursts for online search queries. SIGMOD, USA; 2004. p. 132–42.
  • He J, Yan H, Suel T. Compact full-text indexing of versioned document collections. Conference on Information and Knowledge Management, NY; 2009. p. 415–24.
  • Elsas JL, Dumais ST. Leveraging temporal dynamics of document content in relevance ranking. WSDM, NY; 2010. p. 1–10.
  • Sanchez D, Batet M, Valls A, Gibert K. Ontology-driven web-based semantic similarity. Journal of Intelligent Information Systems. 2010; 35(3):383–413.
  • Yu W, Agichtein E. Temporal latent semantic analysis for collaboratively generated content: preliminary results. Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, USA; 2011. p. 1145–6.
  • Wang X, Zhai C, Hu X, Sproat R. Mining correlated bursty topic patterns from coordinated text streams. KDD; 2007. p. 784–93.
  • Zhou D, Councill I, Zha H, Giles CL. Discovering temporal communities from social network documents. Industrial Conference on Data Mining, PA; 2007. p. 745–50.
  • Qiu J, Lin Z, Tang C, Qiao S. Discovering organizational structure in dynamic social network. Industrial Conference on Data Mining, GA; 2009. p. 932–7.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.