This paper investigates the use of fuzzy logicmechanisms coming from the database community, namely graded inclusions, to model the information retrieval process. In this framework, documents and queries are represented by fuzzy sets, which are paired with operations like fuzzy implications and T-norms. Through different experiments, it is shown that only some among the wide range of fuzzy operations are relevant for information retrieval. When appropriate settings are chosen, it is possible to mimic classical systems, thus yielding results rivaling those of state-of-theart systems. These positive results validate the proposed approach, while negative ones give some insights on the properties needed by such a model.Moreover, this paper shows the added-value of this graded inclusion-based model, which gives new and theoretically grounded ways for a user to easily weight his query terms, to include negative information in his queries, or to expand them with related terms.
CITATION STYLE
Bosc, P., Claveau, V., Pivert, O., & Ughetto, L. (2009). Graded-inclusion-based information retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5478 LNCS, pp. 252–263). https://doi.org/10.1007/978-3-642-00958-7_24
Mendeley helps you to discover research relevant for your work.