Conference proceeding
Profiling Topics on the Web
Proceedings of the WWW2007 Workshop i3: Identity, Identifiers, Identification. Banff, Canada, May 8, 2007, CEUR Workshop Proceedings
2007
Abstract
The availability of large-scale data on the Web motivates the development of automatic algorithms to analyze topics and identify relationships between topics. Various approaches have been proposed in the literature. Most focus on specific entities, such as people, and not on topics in general. They are also less flexible in how they represent topics/entities.
In this paper we study existing methods as well as describe preliminary research on a different approach, based on profiles, for representing general topics. Topic profiles consist of different types of features. We compare different methods for building profiles and evaluate them in terms of their information content and ability to predict relationships between topics. Our results suggest that profiles derived from the full text present in multiple pages are the most informative and that profiles derived from multiple pages are significantly better at predicting topic relationships than profiles derived from single pages.
Details
- Title: Subtitle
- Profiling Topics on the Web
- Creators
- Aditya K Sehgal - University of IowaPadmini Srinivasan - University of Iowa, Computer Science
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the WWW2007 Workshop i3: Identity, Identifiers, Identification. Banff, Canada, May 8, 2007, CEUR Workshop Proceedings
- ISSN
- 1613-0073
- Size
- 1-8
- Grant note
- This material is based upon work supported by the National Science Foundation under Grant No.0312356 awarded to Padmini Srinivasan. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
- Language
- English
- Date published
- 2007
- Academic Unit
- Business Analytics; Computer Science; Nursing
- Record Identifier
- 9984012899102771
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