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Intelligent conversation system using multiple classification ripple down rules and conversational context


Herbert, D and Kang, BH, Intelligent conversation system using multiple classification ripple down rules and conversational context, Expert Systems With Applications, 112 pp. 342-352. ISSN 0957-4174 (2018) [Refereed Article]


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Copyright 2018 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

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DOI: doi:10.1016/j.eswa.2018.06.049


We introduce an extension to Multiple Classification Ripple Down Rules (MCRDR), called Contextual MCRDR (C-MCRDR). We apply C-MCRDR knowledge-base systems (KBS) to the Textual Question Answering (TQA) and Natural Language Interface to Databases (NLIDB) paradigms in restricted domains as a type of spoken dialog system (SDS) or conversational agent (CA). C-MCRDR implicitly maintains topical conversational context, and intra-dialog context is retained allowing explicit referencing in KB rule conditions and classifications. To facilitate NLIDB, post-inference C-MCRDR classifications can include generic query referencing – query specificity is achieved by the binding of pre-identified context. In contrast to other scripted, or syntactically complex systems, the KB of the live system can easily be maintained courtesy of the RDR knowledge engineering approach. For evaluation, we applied this system to a pedagogical domain that uses a production database for the generation of offline course-related documents. Our system complemented the domain by providing a spoken or textual question-answering alternative for undergraduates based on the same production database. The developed system incorporates a speech-enabled chatbot interface via Automatic Speech Recognition (ASR) and experimental results from a live, integrated feedback rating system showed significant user acceptance, indicating the approach is promising, feasible and further work is warranted. Evaluation of the prototype’s viability found the system responded appropriately for 80.3% of participant requests in the tested domain, and it responded inappropriately for 19.7% of requests due to incorrect dialog classifications (4.4%) or out of scope requests (15.3%). Although the semantic range of the evaluated domain was relatively shallow, we conjecture that the developed system is readily adoptable as a CA NLIDB tool in other more semantically-rich domains and it shows promise in single or multi-domain environments.

Item Details

Item Type:Refereed Article
Keywords:knowledgebase systems, textual question answering, MCRDR case based reasoning, pattern matching
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Knowledge representation and reasoning
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Herbert, D (Dr David Herbert)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:126966
Year Published:2018
Web of Science® Times Cited:12
Deposited By:Information and Communication Technology
Deposited On:2018-07-04
Last Modified:2019-02-26
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