Fuzzy Logics for Multiple Choice Question Answering

Martinez-Gil, Jorge Fuzzy Logics for Multiple Choice Question Answering., 2022 [Preprint]

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English abstract

We have recently witnessed how solutions based on neural-inspired architectures are the most popular in terms of Multiple-Choice Question Answering. However, solutions of this kind are difficult to interpret, require many resources for training, and present obstacles to transferring learning. In this work, we move away from this mainstream to explore new methods based on fuzzy logic that can cope with these problems. The results that can be obtained are in line with those of the neural cutting solutions, but with advantages such as their ease of interpretation, the low cost concerning the resources needed for training as well as the possibility of transferring the knowledge acquired in a much more straightforward and more intuitive way.

Item type: Preprint
Keywords: Knowledge engineering, Fuzzy Logics, Multiple Choice Question Answering
Subjects: I. Information treatment for information services > ID. Knowledge representation.
I. Information treatment for information services > IK. Design, development, implementation and maintenance
Depositing user: Dr Jorge Martinez-Gil
Date deposited: 31 Jan 2022 07:44
Last modified: 31 Jan 2022 07:44
URI: http://hdl.handle.net/10760/42831

References

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Jorge Martinez-Gil: Automated knowledge base management: A survey. Comput. Sci. Rev. 18: 1-9 (2015).

Jorge Martinez-Gil: CoTO: A novel approach for fuzzy aggregation of semantic similarity measures. Cogn. Syst. Res. 40: 8-17 (2016).

Monika Rani, Maybin K. Muyeba, O. P. Vyas: A Hybrid Approach using Ontology Similarity and Fuzzy Logic for Semantic Question Answering. CoRR abs/1709.09214 (2017).

Lotfi A. Zadeh: From search engines to question-answering systems the need for new tools. FUZZ-IEEE 2003: 1107-1109.

Lotfi A. Zadeh: A note on web intelligence, world knowledge and fuzzy logic. Data Knowl. Eng. 50(3): 291-304 (2004).


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