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Augmenting human intelligence via externalized knowledge representation and intelligent information retrieval

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dc.contributor.advisor Patrick H. Winston. en_US
dc.contributor.author Kuznetsov, Gleb en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.date.accessioned 2011-10-17T19:49:19Z
dc.date.available 2011-10-17T19:49:19Z
dc.date.copyright 2011 en_US
dc.date.issued 2011 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/66311
dc.description Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. en_US
dc.description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. en_US
dc.description Cataloged from student submitted PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 77-78). en_US
dc.description.abstract With a representation of a user's mental model in hand, a computer system can continuously query against a knowledge base of past work sessions or information on the Web and generate a set of recommended resources for the user to consider. In this thesis, I have developed an interface and a representation that allows a computer system to build a model of a user's intent and generate recommendations. I have designed, prototyped, and deployed the Mental Model Browser, a web application that infers a user's intent during a Web browsing session and provides recommendations for related URLs. The application includes a web browser extension for recording the URLs a user visits and a feedback interface that hosts a dialogue between the computer system and the user. The Mental Model Browser identifies important concepts in the session by leveraging an API provided by the Delicious web bookmarking service, a rich data corpus of crowd-sourced web page tags. The identified concepts are presented to the user to confirm their validity and trigger a query for recommended web pages. I conducted a pilot study with 22 active participants who engaged in 56 web browsing sessions. The results of the study show that users were able to readily adapt to the workflow of the application. Users reported quickly discovering how to help shape the system's model of the session through the use of tags. Several users reported receiving valuable recommendations that they did not find through search alone. Finally, I lay out visions for near-future technologies such as multi-modal knowledge capture and activation, as well as knowledge-based social networks that are enabled by the concepts I have explored in this thesis. en_US
dc.description.statementofresponsibility by Gleb Kuznetsov. en_US
dc.format.extent 78 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Augmenting human intelligence via externalized knowledge representation and intelligent information retrieval en_US
dc.type Thesis en_US
dc.description.degree M.Eng. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 755615239 en_US


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