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Using Multi-Source Integration and Information Retrieval Technology to Program Question-and-Answering Chatbots.

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Title: Using Multi-Source Integration and Information Retrieval Technology to Program Question-and-Answering Chatbots.
Authors: SHAUI, AN-CHI1 ajs10348@psu.edu, MA, SHANG-PIN2 albert@ntou.edu.tw
Source: Journal of Information Science & Engineering. May2026, Vol. 42 Issue 3, p727-742. 16p.
Subjects: Chatbots, Data integration, Generative artificial intelligence, Information retrieval, Feature extraction, Computer programming education
Abstract: Despite rapid growth in the number of programming learners, the continuous influx of new information and a lack of human guidance has left many learners sifting through online resources for reliable content. Moreover, many learners struggle to frame questions that accurately capture their specific concerns. This study addressed the growing need for efficient learning resources by developing a microservice-based Chatbot to synthesize information from diverse sources for programming learners. The proposed MPAbot system utilizes keyword extraction and cross-referencing across multiple sourced posts to help users solve questions more effectively. MPAbot incorporates word embeddings, sentence similarity, Latent Dirichlet Allocation (LDA) topic modeling, and multi-criteria decision analysis to filter out redundant information, thereby reducing browsing time and enhancing learning efficiency. Besides, MPAbot also integrates generative AT to improve the accuracy of its answers to users' questions. Experimental results show that our OPT-ranked approach achieved a 52.43% higher mean rank score compared to purely GPT-generated responses in user evaluation tests, demonstrating the effectiveness of the proposed multisource integration approach. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Information Science & Engineering is the property of Institute of Information Science, Academia Sinica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="DE" term="%22Chatbots%22">Chatbots</searchLink><br /><searchLink fieldCode="DE" term="%22Data+integration%22">Data integration</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming+education%22">Computer programming education</searchLink>
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  Data: Despite rapid growth in the number of programming learners, the continuous influx of new information and a lack of human guidance has left many learners sifting through online resources for reliable content. Moreover, many learners struggle to frame questions that accurately capture their specific concerns. This study addressed the growing need for efficient learning resources by developing a microservice-based Chatbot to synthesize information from diverse sources for programming learners. The proposed MPAbot system utilizes keyword extraction and cross-referencing across multiple sourced posts to help users solve questions more effectively. MPAbot incorporates word embeddings, sentence similarity, Latent Dirichlet Allocation (LDA) topic modeling, and multi-criteria decision analysis to filter out redundant information, thereby reducing browsing time and enhancing learning efficiency. Besides, MPAbot also integrates generative AT to improve the accuracy of its answers to users' questions. Experimental results show that our OPT-ranked approach achieved a 52.43% higher mean rank score compared to purely GPT-generated responses in user evaluation tests, demonstrating the effectiveness of the proposed multisource integration approach. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Information Science & Engineering is the property of Institute of Information Science, Academia Sinica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Type: general
      – SubjectFull: Data integration
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      – SubjectFull: Generative artificial intelligence
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      – SubjectFull: Feature extraction
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              Text: May2026
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