Collective Knowledge Bases
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.
People
Publications
- Object identification with attribute-mediated dependencies (2005)
- iMAP: Discovering Complex Semantic Matches between Database Schemas (2004)
- Trust management for the Semantic Web (2003)
- Building large knowledge bases by mass collaboration (2003)
- Learning with knowledge from multiple experts (2003)
- Learning to Map between Ontologies on the Semantic Web (2002)
- Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach (2001)
Research Groups
Last changed Fri, 2012-11-16 11:28

cs.