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  • × author_ss:"Braghin, S."
  1. Bozzato, L.; Braghin, S.; Trombetta, A.: ¬A method and guidelines for the cooperation of ontologies and relational databases in Semantic Web applications (2012) 0.00 
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    Abstract
    Ontologies are a well-affirmed way of representing complex structured information and they provide a sound conceptual foundation to Semantic Web technologies. On the other hand, a huge amount of information available on the web is stored in legacy relational databases. The issues raised by the collaboration between such worlds are well known and addressed by consolidated mapping languages. Nevertheless, to the best of our knowledge, a best practice for such cooperation is missing: in this work we thus present a method to guide the definition of cooperations between ontology-based and relational databases systems. Our method, mainly based on ideas from knowledge reuse and re-engineering, is aimed at the separation of data between database and ontology instances and at the definition of suitable mappings in both directions, taking advantage of the representation possibilities offered by both models. We present the steps of our method along with guidelines for their application. Finally, we propose an example of its deployment in the context of a large repository of bio-medical images we developed.
    Source
    Proceedings of the 2nd International Workshop on Semantic Digital Archives held in conjunction with the 16th Int. Conference on Theory and Practice of Digital Libraries (TPDL) on September 27, 2012 in Paphos, Cyprus [http://ceur-ws.org/Vol-912/proceedings.pdf]. Eds.: A. Mitschik et al
  2. Datta, A.; Yong, J.T.T.; Braghin, S.: ¬The zen of multidisciplinary team recommendation (2014) 0.00 
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    Abstract
    It is often necessary to compose a team consisting of experts with diverse competencies to accomplish complex tasks. However, for its proper functioning, it is also preferable that a team be socially cohesive. A team recommendation system, which facilitates the search for potential team members, can be of great help both for (a) individuals who need to seek out collaborators and for (b) managers who need to build a team for some specific tasks. Such a decision support system that readily helps summarize multiple metrics indicating a team (and its members) quality, and possibly rank the teams in a personalized manner according to the end users' preferences, thus serves as a tool to cope with what would otherwise be an information avalanche. In this work, we present Social Web Application for Team Recommendation, a general-purpose framework to compose various information retrieval and social graph mining and visualization subsystems together to build a composite team recommendation system, and instantiate it for a case study of academic teams.

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