Abstract
Customer support systems based on chatbots gain an increasing popularity. Chatbots are becoming more and more important to a plethora of applications not only for social services. Modern information retrieval (IR) chatbots are based on simple queries to a database and do not ensure intelligent dialogues with users. In this paper we propose an IR-chatbot model that incorporates a concept-based knowledge model and an index-guided traversal through it to ensure the discovery of information relevant for users and coherent to their preferences. The proposed approach not only supports a search session, but also helps users to discover properties of items and sequentially refine an imprecise query.
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Acknowledgements
Sections 3, 4.1 and 4.2 (algorithm to build a domain knowledge model, algorithm to navigate to a group of relevant objects) were written by Dmitry A. Ilvovsky and Tatiana Makhalova supported by the Russian Science Foundation under grant 17-11-01294 and performed at National Research University Higher School of Economics, Russia.
Sections 2 and 4.3 (chatbot investigations, model and algorithm of pattern structure walk) were prepared within the framework of the HSE University Basic Research Program and funded by the Russian Academic Excellence Project ‘5–100’. The rest of the paper was written and performed at Oracle Corp.
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Makhalova, T., Ilvovsky, D., Galitsky, B. (2019). Information Retrieval Chatbots Based on Conceptual Models. In: Endres, D., Alam, M., Şotropa, D. (eds) Graph-Based Representation and Reasoning. ICCS 2019. Lecture Notes in Computer Science(), vol 11530. Springer, Cham. https://doi.org/10.1007/978-3-030-23182-8_17
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