Automatically refining the wikipedia infobox ontology
Proceedings of the 17th international conference on World Wide Web
Qualitative geocoding of persistent web pages
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Amplifying community content creation with mixed initiative information extraction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Information extraction challenges in managing unstructured data
ACM SIGMOD Record
Using Wikipedia to bootstrap open information extraction
ACM SIGMOD Record
Efficiently incorporating user feedback into information extraction and integration programs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Language-model-based ranking for queries on RDF-graphs
Proceedings of the 18th ACM conference on Information and knowledge management
Crowdsourcing systems on the World-Wide Web
Communications of the ACM
Building Mashups by Demonstration
ACM Transactions on the Web (TWEB)
Improving Wikipedia with DBpedia
Proceedings of the 21st international conference companion on World Wide Web
Building, maintaining, and using knowledge bases: a report from the trenches
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Hi-index | 0.02 |
The rapid growth of Web communities has motivated many solutions for building community data portals. These solutions follow roughly two approaches. The first approach (e.g., Libra, Citeseer, Cimple) employs semi-automatic methods to extract and integrate data from a multitude of data sources. The second approach (e.g., Wikipedia, Intellipedia) deploys an initial portal in wiki format, then invites community members to revise and add material. In this paper we consider combining the above two approaches to building community portals. The new hybrid machine-human approach brings significant benefits. It can achieve broader and deeper coverage, provide more incentives for users to contribute, and keep the portal more up-to-date with less user effort. In a sense, it enables building "community wikipedias", backed by an underlying structured database that is continuously updated using automatic techniques. We outline our ideas for the new approach, describe its challenges and opportunities, and provide initial solutions. Finally, we describe a real-world implementation and preliminary experiments that demonstrate the utility of the new approach.