User modelling on semi-structured documents
User modelling on semi-structured documents
We present a new approach that makes use of the embedded structural information of the documents that a user frequently refers to for deriving a personalized concept hierarchy and for identifying user preferences concerning document searching and browsing. In contrast with conventional methods that ignore the distribution of structural elements, our approach accepts semantic clues defined in the structural tags, so that it takes full advantage of the textual and structural information. Formal concept analysis theory is applied to define semantic relationships among the concepts, and reinforcement learning is employed to unobtrusively adapt to individualized information needs. Given a user’s query, the personal ontology and user model maximize their knowledge bases to present the relevant documents in a prioritized order, one which the user prefers to browse. We demonstrate the practicability of our approach through two experiments. The former showed significantly improved performance results compared to that of a flat vector model. The second experiment compared the performance accuracy of the structure elements exploited by four users and thereby demonstrated the diversity of user preferences.
Personal ontology, Supported browsing, Structured document, Reinforcement Learning, Formal concept analysis
Kim, Sanghee
9e0e5909-9fbe-4c37-9606-2fdea35eac12
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c
Keane, Andy
26d7fa33-5415-4910-89d8-fb3620413def
2002
Kim, Sanghee
9e0e5909-9fbe-4c37-9606-2fdea35eac12
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c
Keane, Andy
26d7fa33-5415-4910-89d8-fb3620413def
Kim, Sanghee, Hall, Wendy and Keane, Andy
(2002)
User modelling on semi-structured documents
Record type:
Monograph
(Project Report)
Abstract
We present a new approach that makes use of the embedded structural information of the documents that a user frequently refers to for deriving a personalized concept hierarchy and for identifying user preferences concerning document searching and browsing. In contrast with conventional methods that ignore the distribution of structural elements, our approach accepts semantic clues defined in the structural tags, so that it takes full advantage of the textual and structural information. Formal concept analysis theory is applied to define semantic relationships among the concepts, and reinforcement learning is employed to unobtrusively adapt to individualized information needs. Given a user’s query, the personal ontology and user model maximize their knowledge bases to present the relevant documents in a prioritized order, one which the user prefers to browse. We demonstrate the practicability of our approach through two experiments. The former showed significantly improved performance results compared to that of a flat vector model. The second experiment compared the performance accuracy of the structure elements exploited by four users and thereby demonstrated the diversity of user preferences.
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Published date: 2002
Keywords:
Personal ontology, Supported browsing, Structured document, Reinforcement Learning, Formal concept analysis
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 256741
URI: http://eprints.soton.ac.uk/id/eprint/256741
PURE UUID: e85a02f5-a786-4aab-adce-aa76cae96fe5
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Date deposited: 27 Jun 2003
Last modified: 15 Mar 2024 02:52
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Contributors
Author:
Sanghee Kim
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