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Collective Knowledge Bases
Overview
The production and use of knowledge is a collective enterprise, and
communication between its participants is the bottleneck. Some of the
costs of this bottleneck are duplicated work, misdirected work, slower
progress, and suboptimal decisions for lack of knowledge that is
actually available. The Internet has greatly reduced the physical
barriers to communication and coordination; our focus is to help
overcome the intellectual ones. In particular, we are developing
methods to improve the composability of knowledge by
semi-automatically learning to translate between the vocabularies of
different sources. This can potentially lead to an exponential
increase in the number of questions answerable by a collective
knowledge base. We are also developing methods to automatically learn
the quality of knowledge sources and elements, to properly take
advantage of sources of widely variable quality, to automatically
resolve inconsistencies between sources, and to automatically give
feedback, credit and guidance to contributors, such that a collective
knowledge base can grow and improve harmonically without centralized
control. We are beginning to implement these ideas in
BibServ, a collective
bibliography repository.
Publications
- R. Dhamankar, Y. Lee, A. Doan, A. Halevy and P. Domingos,
iMAP: Discovering Complex Semantic Matches between Database Schemas.
Proceedings of the 2004 ACM SIGMOD International Conference on
Management of Data (pp. 383-394), 2004. Paris, France: ACM Press.
- A. Doan, P. Domingos and A. Halevy,
Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach.
Proceedings of the 2001 ACM SIGMOD International Conference on
Management of Data (pp. 509-520), 2001. Santa Barbara, CA: ACM Press.
- A. Doan, J. Madhavan, P. Domingos and A. Halevy,
Learning to Map between Ontologies on the Semantic Web. Proceedings
of the Eleventh International World Wide Web Conference (pp. 662-673),
2002. Honolulu, HI: ACM Press.
- M. Richardson, R. Agrawal and P. Domingos,
Trust Management for the Semantic Web. Proceedings of the Second
International Semantic Web Conference (pp. 351-368), Sanibel Island, FL, 2003.
- M. Richardson and P. Domingos,
Building Large Knowledge Bases by Mass Collaboration. Proceedings
of the Second International Conference on Knowledge Capture (pp. 129-137),
Sanibel Island, FL, 2003.
- M. Richardson and P. Domingos,
Learning with Knowledge from Multiple Experts. Proceedings of the
Twentieth International Conference on Machine Learning (pp. 624-631), 2003.
Washington, DC: Morgan Kaufmann.
- P. Singla and P. Domingos,
Object Identification with Attribute-Mediated Dependences.
Proceedings of the Ninth European Conference on Principles and Practice of
Knowledge Discovery in Databases, 2005. Porto, Portugal: Springer.
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