2008
DOI: 10.1016/j.knosys.2008.03.055
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Domain knowledge query conversation bots in instant messaging (IM)

Abstract: Goh, O.S., Fung, C.C. and Depickere, A. (2008) Domain knowledge query conversation bots in instant messaging (IM). AbstractIn this paper, we examine the use of knowledge query technology as applied to conversation bots in the instant messaging environment. Hence, we designed an artificial intelligent conversation robot or bots called Artificial Intelligence Natural language Identity (hereafter, AINI) to mimic human conversation. Our goal is to introduce a Domain Matrix Knowledge Model and an Automated Knowledg… Show more

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Cited by 10 publications
(5 citation statements)
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References 21 publications
(20 reference statements)
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“…AI conversational bots have been tested and used in instant messenger platforms, and results of diverse experiments and usage are promising (e.g. Goh et al, 2008). However, researchers are cautious as there are still differences in the quality and content of these conversations when compared to human-to-human interactions (Hill et al, 2015).…”
Section: Context and Conceptual Foundationsmentioning
confidence: 99%
“…AI conversational bots have been tested and used in instant messenger platforms, and results of diverse experiments and usage are promising (e.g. Goh et al, 2008). However, researchers are cautious as there are still differences in the quality and content of these conversations when compared to human-to-human interactions (Hill et al, 2015).…”
Section: Context and Conceptual Foundationsmentioning
confidence: 99%
“…Personal Search [37,38] Several examples [55] Synchronous Interactions Presence Indicator Gleams of People [127] Conversational Agent for IM AINI [39] Meeting Scheduling [22] [59]…”
Section: Discussionmentioning
confidence: 99%
“…In the Synchronous Interactions category, we identified five major Conversational Agent for IM AINI [39] Meeting Scheduling [22] [ 59,60] RESCH [62] MSRAC [8] [89]…”
Section: Synchronous Interactionsmentioning
confidence: 99%
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“…Many open-domain Q&A systems answer questions by first harvesting many candidate answers, and returned the most possible one [30]. Research activities involving QA has been greatly motivated by the introduction of the QA Track in TREC (Text Retrieval Conference) evaluations [31,32]. Leila Kosseim etc.…”
Section: Related Workmentioning
confidence: 99%