Contextual web searches in Facebook using learning materials and discussion messages

The web is nowadays one of the main information sources, and information search is an important area in which many advances have been registered. One approach to improve web search results is to consider contextual information. Usually, information about context has been provided through user logs on previous searches or the monitoring of clicks on first results, but different approaches can be used in specific environments. In a web based learning environment, existing documents and exchanged messages could provide contextual information. So, the main goal of this work is to provide a contextual web search engine based on shared documents and messages posted in a social network used for collaborative learning. Contextual search is provided through query expansion using learning documents (material provided by the teacher) and discussion messages (posts, links and comments that result from the participants' interactions). A prototype was implemented and used in a learning scenario to acquire the context in a learning community. The proposed approach makes the context acquisition faster and more dynamic as it considers an automatic approach over text processing of documents and discussions. In addition, the results of the query engine with and without the contextual information were compared and the proposed approach using contextual information showed improvements in the precision of the results.

[1]  Michael Zimmer,et al.  Web Search Studies: Multidisciplinary Perspectives on Web Search Engines , 2009 .

[2]  Judith A. Parker.  Blogs, Wikis, podcasts, and other powerful tools for the classrooms , 2007, Internet High. Educ..

[3]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[4]  Sean W. M. Siqueira,et al.  Improving the Efficiency of Web Searches in Collaborative Learning Platforms , 2011, WSKS.

[5]  Freddy Y. Y. Choi.  Advances in domain independent linear text segmentation , 2000, ANLP.

[6]  Marti A. Hearst.  Text Tiling: Segmenting Text into Multi-paragraph Subtopic Passages , 1997, CL.

[7]  Mykola Pechenizkiy,et al.  Handbook of Educational Data Mining , 2010 .

[8]  Danah Boyd,et al.  Social network sites: definition, history, and scholarship , 2007, IEEE Engineering Management Review.

[9]  Mitchell P. Marcus,et al.  Topic segmentation: algorithms and applications , 1998 .

[10]  C. Cutilli.  Seeking Health Information: What Sources Do Your Patients Use? , 2010, Orthopedic nursing.

[11]  Sean W. M. Siqueira,et al.  Contextual Query based on Segmentation and Clustering of Selected Documents for Acquiring Web Documents for Supporting Knowledge Management , 2011, AMCIS.

[12]  Wan-Shiou Yang,et al.  Mining Social Networks for Targeted Advertising , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[13]  Xiaojun Wan,et al.  Using Proportional Transportation Distances for Measuring Document Similarity , 2006, ECIR.

[14]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[15]  Sean W. M. Siqueira,et al.  Using Educational Resources to Improve the Efficiency of Web Searches for Additional Learning Material , 2011, 2011 IEEE 11th International Conference on Advanced Learning Technologies.

[16]  R. Hanneman.  Introduction to Social Network Methods , 2001 .

[17]  Doreen Böhnstedt,et al.  Recommending and Finding Multimedia Resources in Knowledge Acquisition Based on Web Resources , 2010, 2010 Proceedings of 19th International Conference on Computer Communications and Networks.

[18]  Hossein N Yarandi,et al.  The Digital Divide and Urban Older Adults , 2010, Computers, informatics, nursing : CIN.

[19]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[20]  Jon Kleinberg,et al.  The link prediction problem for social networks , 2003, CIKM '03.

[21]  Marco Dorigo,et al.  On the Performance of Ant-based Clustering , 2003, HIS.

[22]  Hitoshi Isahara,et al.  A Statistical Model for Domain-Independent Text Segmentation , 2001, ACL.

[23]  Willard Richardson,et al.  Blogs, Wikis, Podcasts, and Other Powerful Web Tools for Classrooms , 2006 .

[24]  M. Volman,et al.  The Web as an Information Resource in K–12 Education: Strategies for Supporting Students in Searching and Processing Information , 2005 .

[25]  Katarzyna Musial,et al.  User position measures in social networks , 2009, SNA-KDD '09.

[26]  J. Wiebe.  Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference , 2000 .

[27]  Chuan Wang,et al.  A User Motivation Model for Web Search Engine , 2009, 2009 Ninth International Conference on Hybrid Intelligent Systems.

[28]  John-Paul Hatala.  Social Network Analysis in Human Resource Development: A New Methodology , 2006 .

[29]  T. Santhanam,et al.  Automatic Recommendation of Web Pages in Web Usage Mining , 2010 .

[30]  Karen Sparck Jones.  Automatic keyword classification for information retrieval , 1971 .

[31]  Myeong-Cheol Ko,et al.  A Term Cluster Query Expansion Model Based on Classification Information in Natural Language Information Retrieval , 2010, 2010 International Conference on Artificial Intelligence and Computational Intelligence.

[32]  Olfa Nasraoui,et al.  Semantic Information Retrieval for Personalized E-Learning , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.

[33]  Johanna D. Moore,et al.  Latent Semantic Analysis for Text Segmentation , 2001, EMNLP.

[34]  Tristram Hooley,et al.  Careering through the Web: the potential of Web 2.0 and 3.0 technologies for career development and career support services , 2011 .

[35]  Karl Gyllstrom,et al.  A comparison of query and term suggestion features for interactive searching , 2009, SIGIR.

[36]  Been-Chian Chien,et al.  Intelligent Information Retrieval Applying Automatic Constructed Fuzzy Ontology , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[37]  Claudio Carpineto,et al.  A Survey of Automatic Query Expansion in Information Retrieval , 2012, CSUR.

[38]  Sebastián Ventura,et al.  Preface to the special issue on data mining for personalised educational systems , 2011, User Modeling and User-Adapted Interaction.

[39]  Mohamed Jemni,et al.  Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval , 2008, 2008 Eighth IEEE International Conference on Advanced Learning Technologies.

[40]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[41]  Roman Kern,et al.  Efficient linear text segmentation based on information retrieval techniques , 2009, MEDES.

[42]  P. Smith,et al.  A review of ontology based query expansion , 2007, Inf. Process. Manag..

[43]  Mirela Mihić,et al.  Business Intelligence: The Role of the Internet in Marketing Research and Business Decision-Making , 2010 .

[44]  Matteo Magnani,et al.  I N F O R M a T I O N R E T R I E V a L F O R S O C I a L M E D I a Conversation Retrieval for Microblogging Sites , 2022 .

[45]  Norbert M. Seel,et al.  Encyclopedia of the sciences of learning , 2012 .

[46]  Ryan S. Baker,et al.  The State of Educational Data Mining in 2009: A Review and Future Visions. , 2009, EDM 2009.

[47]  Caroline Haythornthwaite,et al.  Studying Online Social Networks , 2006, J. Comput. Mediat. Commun..

[48]  Alexander Clark,et al.  An Analysis of Quantitative Aspects in the Evaluation of Thematic Segmentation Algorithms , 2009, SIGDIAL Workshop.

[49]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[50]  Athula Ginige,et al.  Improving information retrieval effectiveness by assigning context to documents , 2004, ISICT.

[51]  Orland Hoeber,et al.  Web Information Retrieval Support Systems: The Future of Web Search , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.