Artificial Intelligence and Decision Making
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Artificial Intelligence and Decision Making, 2021, Issue 4, Pages 35–49
DOI: https://doi.org/10.14357/20718594210204 (Mi iipr117) 		 
Analysis of textual and graphical information

Open information extraction from texts. Part III. Question answering system

E. V. Chistovaa, D. S. Larionovb, E. A. Latypovac, A. O. Shelmanova, I. V. Smirnova

a Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
b HSE University, Moscow, Russia
c Moscow Institute of Physics and Technology (National Research University), Moscow, Russia
Full-text PDF (724 kB)
DOI: https://doi.org/10.14357/20718594210204
Abstract: In this paper, we propose a question answering system prototype that works on top of an au-tomatically generated knowledge base. For the knowledge base construction, methods of open infor-mation extraction are used, as well as unsupervised learning methods. In particular, various deep clus-tering methods are investigated and applied. Using open information extraction methods, triplets of the form (object1; predicate; object2) are extracted, which are then clustered into semantic relations. The clustered triplets are collected into a graph database, which is a source of information to generate an answer. This study demonstrates the applicability of unsupervised relation extraction methods.
Keywords: knowledge base question answering, information extraction, unsupervised machine learn-ing, neural networks, autoencoder, question classification.
Funding agency Grant number
Russian Foundation for Basic Research 16-29-12937 офи_м
17-07-01477 А
18-29-22027 мк
English version:
Scientific and Technical Information Processing, 2022, Volume 49, Issue 6, Pages 416–426
DOI: https://doi.org/10.3103/S014768822206003X
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: E. V. Chistova, D. S. Larionov, E. A. Latypova, A. O. Shelmanov, I. V. Smirnov, “Open information extraction from texts. Part III. Question answering system”, Artificial Intelligence and Decision Making, 2021, no. 4, 35–49; Scientific and Technical Information Processing, 49:6 (2022), 416–426
Citation in format AMSBIB
\Bibitem{ChiLarLat21}
\by E.~V.~Chistova, D.~S.~Larionov, E.~A.~Latypova, A.~O.~Shelmanov, I.~V.~Smirnov
\paper Open information extraction from texts. Part III.~Question answering system
\jour Artificial Intelligence and Decision Making
\yr 2021
\issue 4
\pages 35--49
\mathnet{http://mi.mathnet.ru/iipr117}
\crossref{https://doi.org/10.14357/20718594210204}
\elib{https://elibrary.ru/item.asp?id=47367817}
\transl
\jour Scientific and Technical Information Processing
\yr 2022
\vol 49
\issue 6
\pages 416--426
\crossref{https://doi.org/10.3103/S014768822206003X}
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  • https://www.mathnet.ru/eng/iipr/y2021/i4/p35
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