Meta-analysis of the TAEE project applying social network analysis

The social network analysis (SNA) is an approach that can be applied as a complement to other analysis (such as statistical) in order to obtain other valuable information. The social network analysis has been used in several initiatives showing that it is an approach that can contribute in building the semantic web. Within the project Technologies Applied to Electronics Teaching (TAEE) there are biannual conferences (it has been organized since 1996) and have accumulated a significant amount of data resulting from the conferences held. All of this information constitutes a data source that should be exploited and that can provide meaningful information. In this document we describe, how to social network analysis has been used on data sources generated by user communities, in order to obtain some semantic artifacts, like ontologies. Also describes how to was applied the social network analysis and its metrics on the information generated in the TAEE congresses to answer a set of questions (What are the relationships and the level of cohesion of the different organizations (at the level of Spain and across continents) involved in TAEE? How have evolutioned the thematics covered in the conference?, What are the new ontological additions in technology over the years?, and How have evolutioned the thematics in the research and studies related to teaching electronics?) formulated by the organizers of the congresses and that through other approaches would have been a large task and complicated. The answers to the questions can provide us important information about the behavior and characteristics of the elements present in TAEE conferences, furthermore being an element for making decisions on future initiatives with the same style of TAEE.

[1]  Vladimir Batagelj,et al.  Network Analysis of Repositories , 2007, LODE.

[2]  Amit P. Sheth,et al.  Semantic Association Identification and Knowledge Discovery for National Security Applications , 2005, J. Database Manag..

[3]  Amit P. Sheth,et al.  Scalable semantic analytics on social networks for addressing the problem of conflict of interest detection , 2008, TWEB.

[4]  Gregorio Robles-Martínez,et al.  Empirical Software Engineering Research on Libre Software: Data Sources, Methodologies and Results , 2012 .

[5]  Yan Zhao,et al.  Analyzing Actors and Their Discussion Topics by Semantic Social Network Analysis , 2006, Tenth International Conference on Information Visualisation (IV'06).

[6]  Amit P. Sheth,et al.  SPARQ2L: towards support for subgraph extraction queries in rdf databases , 2007, WWW '07.

[7]  Vladimir Batagelj,et al.  Pajek Program for Analysis and Visualization of Large Networks , 2007 .

[8]  Peter Mika Ontologies Are Us: A Unified Model of Social Networks and Semantics , 2005, International Semantic Web Conference.

[9]  Bethany S. Dohleman Exploratory social network analysis with Pajek , 2006 .

[10]  Jesús M. González-Barahona,et al.  Applying Social Network Analysis Techniques to Community-Driven Libre Software Projects , 2006, Int. J. Inf. Technol. Web Eng..

[11]  Juan Martínez-Romo,et al.  Using Social Network Analysis Techniques to Study Collaboration between a FLOSS Community and a Company , 2008, OSS.

[12]  Martin H. Levinson Linked: The New Science of Networks , 2004 .

[13]  Alan F. Smeaton,et al.  Analysis of papers from twenty-five years of SIGIR conferences: what have we been doing for the last quarter of a century? , 2002, SIGF.

[14]  Vladimir Batagelj,et al.  Exploratory Social Network Analysis with Pajek: Cohesion , 2005 .

[15]  Jesús M. González-Barahona,et al.  Applying Social Network Analysis to the Information in CVS Repositories , 2004, MSR.

[16]  Krys J. Kochut,et al.  SPARQLeR: Extended Sparql for Semantic Association Discovery , 2007, ESWC.

[17]  Amit P. Sheth,et al.  Discovering and Ranking Semantic Associations over a Large RDF Metabase , 2004, VLDB.