NLP and ontology based clustering — An integrated approach for optimal information extraction from social web

Social Web is a heterogeneous collection of both structured and unstructured dataprimarily composed of contents from various social networking sites, blogs, online shopping sites and much more. The knowledge extracted from such data can be valuable for accuracy of search results in light of present explosion of information exchange over social web. The extraction of information patterns from unstructured data sets available at social networking sites viz. facebook, twitter, linkedin is a challenging task as it cannot be understood by machine to robotically process the data. Also, the major data source in the form of naive users triggers the significance of filtration of the relevant results. For effectual analysis of social web contents, this paper proposes integrated NLP and ontology based clustering TVC algorithm that generates semantically meaningful concepts from the social web content. The algorithm promises to optimize the web search results and to provide accuracy in searching the well treated unstructured social web contents.

[1]  V. Sureka,et al.  Approaches to Ontology Based Algorithms for Clustering Text Documents , 2012 .

[2]  Hemant Kumud.  Handling Unstructured Data for Semantic Web – A Natural Language Processing Approach , 2014 .

[3]  Rajiv Kishore,et al.  Ontologies: A Handbook of Principles, Concepts and Applications in Information Systems , 2007, Ontologies.

[4]  Waqas Malik,et al.  Visual Semantic Web.Ontology based E-learning management system , 2008 .

[5]  Albert Y. Zomaya,et al.  A survey on text mining in social networks , 2015, The Knowledge Engineering Review.

[6]  R.Gunasri,et al.  Natural Language Processing And Clustering Based Service Discovery , 2014 .

[7]  Sang-goo Lee,et al.  A semi-supervised document clustering technique for information organization , 2000, CIKM '00.

[8]  Zeynab Abbasi Khalifelu,et al.  Analysis and evaluation of unstructured data: text mining versus natural language processing , 2011, 2011 5th International Conference on Application of Information and Communication Technologies (AICT).

[9]  Kalina Bontcheva,et al.  Natural Language Generation from Ontologies , 2009, Semantic Knowledge Management.

[10]  Thomas R. Gruber,et al.  A Translation Approach to Portable Ontologies , 1993 .

[11]  Steffen Staab,et al.  eLearning based on the semantic web , 2001 .

[12]  Peter Dolog,et al.  Reasoning and Ontologies for Personalized E-Learning in the Semantic Web , 2004, J. Educ. Technol. Soc..

[13]  Barbara J. Grosz,et al.  Natural-Language Processing , 1982, Artificial Intelligence.

[14]  Jyoti,et al.  A Survey on Sentiment Analysis and Opinion Mining , 2016 .

[15]  Leif Grönqvist.  Exploring Latent Semantic Vector Models Enriched With N-grams , 2006 .

[16]  Harmunish Taneja,et al.  Web Objects Clustering Through Aggregation for Enhanced Search Results , 2011 .

[17]  S. Cakula.  E-Learning Developing Using Ontological Engineering , 2013 .

[18]  John Galletly,et al.  Towards E-learning via the semantic web , 2003, CompSysTech '03.

[19]  James A. Hendler,et al.  Towards the semantic web: knowledge representation in a dynamic, distributed environment , 2001 .

[20]  R. I Z W A N A I R F A N,et al.  A Survey on Text Mining in Social Networks , 2012 .

[21]  Xuanjing Huang,et al.  From Web Directories to Ontologies: Natural Language Processing Challenges , 2007, ISWC/ASWC.

[22]  Huajun Chen,et al.  The Semantic Web , 2011, Lecture Notes in Computer Science.

[23]  Shabina Dhuria.  Ontology Equipped Natural Language Processing for Real World Applications , 2014 .

[24]  J. Euzenat,et al.  Ontology Matching , 2007, Springer Berlin Heidelberg.