Consensus reasoning for intelligent web searches

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dc.contributor.advisorBylander, Tom
dc.contributor.authorO'Hara, Steven A.
dc.contributor.committeeMemberBylander, Tom
dc.contributor.committeeMemberRobbins, Kay
dc.contributor.committeeMemberZhang, Weining
dc.contributor.committeeMembervon Ronne, Jeffery
dc.contributor.committeeMemberHuhns, Michael
dc.date.accessioned2024-02-12T19:30:58Z
dc.date.available2024-02-12T19:30:58Z
dc.date.issued2014
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractSuppose you wanted to know the answer to a specific question, such as "What is the atomic mass of Copper?" or "What is the diameter of Pluto?" Certainly, you could Google it and get many different answers. You could also ask experts or go to specific websites that you would expect to know the answer, such as NASA or Wikipedia, or you could even run a piece of software to get the answer. Now, suppose you were able to ask all of these sources at the same time. You'd probably find that you'd get many different, often conflicting, answers. How can those answers be combined into a single "best" consensus answer that is most likely to be closest to the "true" answer? With the immense amount of information available on the internet, it is possible to ask a single question and receive many different answers immediately. Expecting any single source to have the best answers for a wide range of questions or expecting all the sources to agree is quite unreasonable. The ideal approach is to collect answers from multiple sources, each with differing degrees of reliability, and consolidate them together into a single answer. The fundamental thesis of this research is that answer accuracy can be improved by processing candidate answers from multiple sources, using suitable methods to extract answers, detect consensus, handle conflicts, and identify source reliability.
dc.description.departmentComputer Science
dc.format.extent193 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9781303920417
dc.identifier.urihttps://hdl.handle.net/20.500.12588/4958
dc.languageen
dc.subjectIntelligent searching
dc.subjectMultiple correct answers
dc.subjectQuestion answering
dc.subjectText retrieval
dc.subject.classificationComputer science
dc.subject.lcshQuestion-answering systems
dc.subject.lcshWeb search engines
dc.subject.lcshArtificial intelligence
dc.titleConsensus reasoning for intelligent web searches
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentComputer Science
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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