Towards Next Generation CiteSeer: A Flexible Architecture for Digital Library Deployment
CiteSeer began as the first search engine for scientific literature to incorporate Autonomous Citation Indexing, and has since grown to be a well-used, open archive for computer and information science publications, currently indexing over 730,000 academic documents. However, CiteSeer currently faces significant challenges that must be overcome in order to improve the quality of the service and guarantee that CiteSeer will continue to be a valuable, up-to-date resource well into the foreseeable future. This paper describes a new architectural framework for CiteSeer system deployment, named CiteSeer Plus. The new framework supports distributed indexing and storage for load balancing and fault-tolerance as well as modular service deployment to increase system flexibility and reduce maintenance costs. In order to facilitate novel approaches to information extraction, a blackboard framework is built into the architecture.
KeywordsDigital Library Information Extraction Conditional Random Field Master Node Slave Node
Unable to display preview. Download preview PDF.
- 1.Buteau, B.L.: A generic framework for distributed, cooperating blackboard systems. In: Proceedings of the 1990 ACM annual conference on Cooperation, February 20-22, pp. 358–365 (1990)Google Scholar
- 6.Han, H., Lee Giles, C., Manavoglu, E., Zha, H., Zhang, Z., Fox, E.A.: Automatic Document Metadata Extraction using Support Vector Machines. In: Proceedings of the 2003 Joint Conference on Digital Libraries, JCDL 2003 (2003)Google Scholar
- 7.Lafferty, J., McCallum, A., Pereira, F.: Conditional Random Fields: Probabilistic models for segmenting and labeling sequence data. In: International Conference on Machine Learning (2001)Google Scholar
- 9.Leek, T.R.: Information extraction using hidden Markov models. Masters thesis, UC San Diego (1997)Google Scholar
- 10.Penny Nii, H.: Blackboard systems: The blackboard model of problem solving and the evolution of blackboard architectures. The AI Magazine VII(2), 38–53 (summer 1986)Google Scholar
- 11.O’Reilly, T.: What Is Web 2.0 Design Patterns and Business Models for the Next Generation of Software, http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html
- 12.Peng, F., McCallum, A.: Accurate information extraction from research papers using conditional random fields. In: Proceedings of Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics (HLT-NAACL), pp. 329–336 (2004)Google Scholar
- 13.Petinot, Y., Lee Giles, C., Bhatnagar, V., Teregowda, P.B., Han, H., Councill, I.: A Service-Oriented Architecture for Digital Libraries. In: ICSOC 2004, November 15-19 (2004)Google Scholar
- 14.Seymore, K., McCallum, A., Rosenfeld, R.: Learning hidden Markov model structure for information extraction. In: Papers from the AAAI 1999 Workshop on Machine Learning for Information Extration, July 1999, pp. 37–42 (1999)Google Scholar
- 16.Van de Sompel, H., Hochstenbach, P.: Reference linking in a hybrid library environment. Part 1: Frameworks for linking. D-Lib Magazine 5(4) (1999)Google Scholar