TRIGO, Luis, Martin VÍTA, Rui SARMENTO and Pavel BRÁZDIL. Retrieval, visualization and validation of affinities between documents. In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 3: KMIS. Lisbon; Portugal: SciTePress, 2015, p. 452-459. ISBN 978-989-758-158-8. Available from: https://dx.doi.org/10.5220/0005662904520459.
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Basic information
Original name Retrieval, visualization and validation of affinities between documents
Authors TRIGO, Luis (620 Portugal), Martin VÍTA (203 Czech Republic, guarantor, belonging to the institution), Rui SARMENTO (620 Portugal) and Pavel BRÁZDIL (203 Czech Republic).
Edition Lisbon; Portugal, Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 3: KMIS, p. 452-459, 8 pp. 2015.
Publisher SciTePress
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Portugal
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
RIV identification code RIV/00216224:14330/15:00087400
Organization unit Faculty of Informatics
ISBN 978-989-758-158-8
Doi http://dx.doi.org/10.5220/0005662904520459
Keywords in English Affinity network; Centrality measures; Comparison of rankings; Graph-based representation of documents; Information retrieval; Knowledge artifacts
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 3/5/2016 14:55.
Abstract
We present an Information Retrieval tool that facilitates the task of the user when searching for a particular information that is of interest to him. Our system processes a given set of documents to produce a graph, where nodes represent documents and links the similarities. The aim is to offer the user a tool to navigate in this space in an easy way. It is possible to collapse/expand nodes. Our case study shows affinity groups based on the similarities of text production of researchers. This goes beyond the already established communities revealed by co-authorship. The system characterizes the activity of each author by a set of automatically generated keywords and by membership to a particular affinity group. The importance of each author is highlighted visually by the size of the node corresponding to the number of publications and different measures of centrality. Regarding the validation of the method, we analyse the impact of using different combinations of titles, abstracts and keywords on capturing the similarity between researchers.
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