SemTag and Seeker: Bootstrapping the semantic web via automated semantic annotation (2003)
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| Venue: | Proceedings of the 12 th International Conference on World Wide Web (WWW’03 |
| Citations: | 120 - 4 self |
BibTeX
@INPROCEEDINGS{Dill03semtagand,
author = {Stephen Dill and Nadav Eiron and David Gibson and Daniel Gruhl and R. Guha and Anant Jhingran and Tapas Kanungo and Sridhar Rajagopalan and Andrew Tomkins and John A. Tomlin and Jason Y. Zien},
title = {SemTag and Seeker: Bootstrapping the semantic web via automated semantic annotation},
booktitle = {Proceedings of the 12 th International Conference on World Wide Web (WWW’03},
year = {2003}
}
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Abstract
This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. The final version of this paper will reflect new data labeling one billion pages, rather than the 264 million pages reported on herein. To our knowledge, this is the largest scale semantic tagging effort to date. We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web. 1.
