Abstract
This paper proposes a new approach for indexing biomedical documents based on the combination of a Possibilistic Network and a Vector Space Model. This later carries out partial matching between documents and biomedical vocabularies. The main contribution of the proposed approach is to combine the cosine similarity and the two measures of possibility and necessity to enhance the estimation of the similarity between a document and a given concept. The possibility estimates the extent to which a document is not similar to the concept. The necessity allows the confirmation that the document is similar to the concept. Experiments were carried out on the OSHUMED corpora and showed encouraging results.
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Chebil, W., Soualmia, L.F., Omri, M.N., Darmoni, S.J. (2015). Biomedical Concepts Extraction Based on Possibilistic Network and Vector Space Model. In: Holmes, J., Bellazzi, R., Sacchi, L., Peek, N. (eds) Artificial Intelligence in Medicine. AIME 2015. Lecture Notes in Computer Science(), vol 9105. Springer, Cham. https://doi.org/10.1007/978-3-319-19551-3_29
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DOI: https://doi.org/10.1007/978-3-319-19551-3_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19550-6
Online ISBN: 978-3-319-19551-3
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