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dc.contributor.authorSahay, Sauraven_US
dc.date.accessioned2012-02-17T19:23:47Z
dc.date.available2012-02-17T19:23:47Z
dc.date.issued2011-11-15en_US
dc.identifier.urihttp://hdl.handle.net/1853/42855
dc.description.abstractThe main contributions of this thesis revolve around development of an integrated conversational recommendation system, combining data and information models with community network and interactions to leverage multi-modal information access. We have developed a real time conversational information access community agent that leverages community knowledge by pushing relevant recommendations to users of the community. The recommendations are delivered in the form of web resources, past conversation and people to connect to. The information agent (cobot, for community/ collaborative bot) monitors the community conversations, and is 'aware' of users' preferences by implicitly capturing their short term and long term knowledge models from conversations. The agent leverages from health and medical domain knowledge to extract concepts, associations and relationships between concepts; formulates queries for semantic search and provides socio-semantic recommendations in the conversation after applying various relevance filters to the candidate results. The agent also takes into account users' verbal intentions in conversations while making recommendation decision. One of the goals of this thesis is to develop an innovative approach to delivering relevant information using a combination of social networking, information aggregation, semantic search and recommendation techniques. The idea is to facilitate timely and relevant social information access by mixing past community specific conversational knowledge and web information access to recommend and connect users with relevant information. Language and interaction creates usable memories, useful for making decisions about what actions to take and what information to retain. Cobot leverages these interactions to maintain users' episodic and long term semantic models. The agent analyzes these memory structures to match and recommend users in conversations by matching with the contextual information need. The social feedback on the recommendations is registered in the system for the algorithms to promote community preferred, contextually relevant resources. The nodes of the semantic memory are frequent concepts extracted from user's interactions. The concepts are connected with associations that develop when concepts co-occur frequently. Over a period of time when the user participates in more interactions, new concepts are added to the semantic memory. Different conversational facets are matched with episodic memories and a spreading activation search on the semantic net is performed for generating the top candidate user recommendations for the conversation. The tying themes in this thesis revolve around informational and social aspects of a unified information access architecture that integrates semantic extraction and indexing with user modeling and recommendations.en_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectUser modelingen_US
dc.subjectNatural languageen_US
dc.subjectConversationsen_US
dc.subjectWeb 2.0en_US
dc.subjectRecommendationsen_US
dc.subjectSemantic searchen_US
dc.subjectInformation extractionen_US
dc.subjectSocial searchen_US
dc.subjectInformation retrievalen_US
dc.subject.lcshCase-based reasoning
dc.subject.lcshExpert systems (Computer science)
dc.subject.lcshComputer science
dc.subject.lcshKnowledge acquisition (Expert systems)
dc.subject.lcshArtificial intelligence
dc.titleSocio-semantic conversational information accessen_US
dc.typeDissertationen_US
dc.description.degreePhDen_US
dc.contributor.departmentComputingen_US
dc.description.advisorCommittee Chair: Ram, Ashwin; Committee Member: Agichtein, Eugene; Committee Member: Braunstein, Mark; Committee Member: Navathe, Sham; Committee Member: Stasko, Johnen_US


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