Difference between revisions of "Wu and Weld WWW 2008"
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This is a [[Category::paper]] that introduces an autonomous system for refining Wikipedia’s | This is a [[Category::paper]] that introduces an autonomous system for refining Wikipedia’s | ||
| − | infobox information schema to create a cleanly-structured ontology. The [[AddressesProblem::ontology refinement]] problem is solved using both [[UsesMethod::Support Vector Machines]] and a more powerful joint-inference approach expressed in [[UsesMethod::Markov Logic Networks]]. | + | infobox information schema to create a cleanly-structured ontology. Advanced query capability, improved information extractors and semiautomatic generation of new infobox templates are shown as advantages of a refined ontology. The [[AddressesProblem::ontology refinement]] problem is solved using both [[UsesMethod::Support Vector Machines]] and a more powerful joint-inference approach expressed in [[UsesMethod::Markov Logic Networks]]. |
| + | |||
| + | The autonomous system, presented as Kylin Ontology Generator (KOG), is comprised of three modules: | ||
| + | * Schema Cleaner, which merges duplicate classes and attributes and prunes rarely-used ones | ||
| + | * Subsumption Detector, | ||
| + | * Schema Mapper, | ||
== Experimental result == | == Experimental result == | ||
Revision as of 15:07, 25 September 2011
Citation
Wu, F. and Weld, D. 2008. Automatically Refining the Wikipedia Infobox Ontology. In Proceedings of the 17th Conference of the World Wide Web, pp. 635-644, ACM, New York.
Online version
Summary
This is a paper that introduces an autonomous system for refining Wikipedia’s infobox information schema to create a cleanly-structured ontology. Advanced query capability, improved information extractors and semiautomatic generation of new infobox templates are shown as advantages of a refined ontology. The ontology refinement problem is solved using both Support Vector Machines and a more powerful joint-inference approach expressed in Markov Logic Networks.
The autonomous system, presented as Kylin Ontology Generator (KOG), is comprised of three modules:
- Schema Cleaner, which merges duplicate classes and attributes and prunes rarely-used ones
- Subsumption Detector,
- Schema Mapper,
Experimental result
...
Related papers
This paper is based on Wu and Weld CIKM 2007.
