Ebrahim Bagheri – Publication Page

Semantic tagging and linking of software engineering social content

Ebrahim Bagheri and Faezeh Ensan
Reference:
Ebrahim Bagheri and Faezeh Ensan Semantic tagging and linking of software engineering social content. In Autom. Softw. Eng., 23 (2): 147-190, 2016.
Links to Publication: [doi][www]
Abstract:
Social online communities and platforms play a significant role in the activities of software developers either as an integral part of the main activities or through complimentary knowledge and information sharing. As such techniques become more prevalent resulting in a wealth of shared information, the need to effectively organize and sift through the information becomes more important. Top-down approaches such as formal hierarchical directories have shown to lack scalability to be applicable to these circumstanes. Light-weight bottom-up techniques such as community tagging have shown promise for better organizing the available content. However, in more focused communities of practice, such as software engineering and development, community tagging can face some challenges such as textittag explosion, locality of tags and textitinterpretation differences, to name a few. To address these challenges, we propose a semantic tagging approach that benefits from the information available in Wikipedia to semantically ground the tagging process and provide a methodical approach for tagging social software engineering content. We have shown that our approach is able to provide high quality tags for social software engineering content that can be used not only for organizing such content but also for making meaningful and relevant content recommendation to the users both within a local community and also across multiple social online communities. We have empirically validated our approach through four main research questions. The results of our observations show that the proposed approach is quite effective in organizing social software engineering content and making relevant, helpful and novel content recommendations to software developers and users of social software engineering communities.
Bibtex Entry:
@article{DBLP:journals/ase/BagheriE16, author = {Ebrahim Bagheri and Faezeh Ensan}, title = {Semantic tagging and linking of software engineering social content}, journal = {Autom. Softw. Eng.}, volume = {23}, number = {2}, pages = {147--190}, year = {2016}, url = {http://dx.doi.org/10.1007/s10515-014-0146-2}, doi = {10.1007/s10515-014-0146-2}, timestamp = {Tue, 15 Mar 2016 15:41:07 +0100}, biburl = {http://dblp.uni-trier.de/rec/bib/journals/ase/BagheriE16}, bibsource = {dblp computer science bibliography, http://dblp.org} abstract = {Social online communities and platforms play a significant role in the activities of software developers either as an integral part of the main activities or through complimentary knowledge and information sharing. As such techniques become more prevalent resulting in a wealth of shared information, the need to effectively organize and sift through the information becomes more important. Top-down approaches such as formal hierarchical directories have shown to lack scalability to be applicable to these circumstanes. Light-weight bottom-up techniques such as community tagging have shown promise for better organizing the available content. However, in more focused communities of practice, such as software engineering and development, community tagging can face some challenges such as \textit{tag explosion, locality of tags} and \textit{interpretation differences}, to name a few. To address these challenges, we propose a semantic tagging approach that benefits from the information available in Wikipedia to semantically ground the tagging process and provide a methodical approach for tagging social software engineering content. We have shown that our approach is able to provide high quality tags for social software engineering content that can be used not only for organizing such content but also for making meaningful and relevant content recommendation to the users both within a local community and also across multiple social online communities. We have empirically validated our approach through four main research questions. The results of our observations show that the proposed approach is quite effective in organizing social software engineering content and making relevant, helpful and novel content recommendations to software developers and users of social software engineering communities.} }




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