Annals of Computer Science and Information Systems, Volume 21

DOI: http://dx.doi.org/10.15439/2020F113

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 45–48 (2020)

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Abstract. Information Retrieval is about user queries and strategies executed by machines to find the documents that best suit the user's information need. However, this need reduced to a couple of words gives the retrieval system (IRS) a lot room for interpretation. In order to zero in on the user's need many a IRS expands the user query by implicitly adding or explicitly recommending the users further useful terms that help to specify their information need. Queries often do not comprise more than a handful of terms, which, in turn, do not sufficiently represent the user's need. In this paper, we propose and demonstrate an approach that enables users to resort to implicitly more complex query expressions. We call these semantic structures concept blueprints. Furthermore, users have the possibility to define the blueprints on their own. The purpose of the blueprints is to spot more precisely the text passage that fits the user's information need.

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