Experion: A framework for contextualizing evidence in expert finding

  • Rodrigo Gonçalves Universidade Federal de Santa Catarina (UFSC)
  • Carina F. Dorneles Universidade Federal de Santa Catarina (UFSC)

Resumo


Expert finding is traditionally related to a subject of research in information retrieval and, often, is taken to mean "expertise retrieval within a specific organization". The task involves finding an expert in an expertise topic. Even though there are interesting proposals in the literature, they do not consider the context in which a given expertise is bound. This Ph.D. thesis introduces the concept of a framework that chronologically contextualizes search results in expert finding. Our motivation is to provide more accurate results of search processes related to finding experts in a given topic, contextualizing the expertise on professional/academic activities, an open research topic. In this paper, we present the main concepts of the framework we are developing and a general overview of its operation. At the moment, we are using the Lattes platform as a data source, for which we developed a process to extract expertise evidence, supported by the Crossref database.

Palavras-chave: expertise, expertise retrieval, expert finding, data retrieval

Referências

Balog, K., Fang, Y., de Rijke, M., Serdyukov, P., and Si, L. (2012). Expertise retrieval. Foundations and Trends® in Information Retrieval, 6(2–3):127–256.

Chen, H.-H., Treeratpituk, P., Mitra, P., and Giles, C. L. (2013). Csseer: An expert recommendation system based on citeseerx. In Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL ’13, pages 381–382, New York, NY, USA. ACM.

Goncalves, R. and Dorneles, C. F. (2019). Automated expertise retrieval: A taxonomy-based survey and open issues. ACM Comput. Surv., 52(5).

Pal, A. (2015). Discovering experts across multiple domains. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’15, pages 923–926, New York, NY, USA. ACM

Punnarut, R. and Sriharee, G. (2010). A researcher expertise search system using ontology-based data mining. In Proceedings of the Seventh Asia-Pacific Conference on Conceptual Modelling - Volume 110, APCCM ’10, pages 71–78, Darlinghurst, Australia, Australia. Australian Computer Society, Inc.

Tho, Q. T., Hui, S. C., and Fong, A. C. M. (2003). A web mining approach for finding expertise in research areas. In Proceedings. 2003 International Conference on Cyberworlds, pages 310–317.

Sateli, B., L ̈offler, F., K ̈onig-Ries, B., and Witte, R. (2017). Scholarlens: Extracting competences from research publications for the automatic generation of semantic user profiles. PeerJ Computer Science, 2017.
Publicado
04/10/2021
Como Citar

Selecione um Formato
GONÇALVES, Rodrigo; DORNELES, Carina F.. Experion: A framework for contextualizing evidence in expert finding. In: WORKSHOP DE TESES E DISSERTAÇÕES (WTDBD) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 36. , 2021, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 113-119. DOI: https://doi.org/10.5753/sbbd_estendido.2021.18172.