Difference between revisions of "Wu and Weld WWW 2008"
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The autonomous system, presented as Kylin Ontology Generator (KOG), is comprised of three modules: | The autonomous system, presented as Kylin Ontology Generator (KOG), is comprised of three modules: | ||
* a schema cleaner, which merges duplicate classes and attributes and prunes rarely-used ones; | * a schema cleaner, which merges duplicate classes and attributes and prunes rarely-used ones; | ||
| − | * a subsumption detector, which identifies | + | * a subsumption detector, which identifies [http://en.wikipedia.org/wiki/Is-a IS-A] relations between infobox classes (e.g. "volleyball player" IS-A "athlete"); |
* and a schema mapper, which builds attribute mappings between related infobox classes. | * and a schema mapper, which builds attribute mappings between related infobox classes. | ||
| − | The detection of subsumption relations is modeled as a binary classification problem. | + | The detection of subsumption relations is modeled as a binary classification problem and several features are used, such as a, b, and c. |
== Experimental result == | == Experimental result == | ||
Revision as of 15:56, 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:
- a schema cleaner, which merges duplicate classes and attributes and prunes rarely-used ones;
- a subsumption detector, which identifies IS-A relations between infobox classes (e.g. "volleyball player" IS-A "athlete");
- and a schema mapper, which builds attribute mappings between related infobox classes.
The detection of subsumption relations is modeled as a binary classification problem and several features are used, such as a, b, and c.
Experimental result
...
Related papers
This paper is based on Wu and Weld CIKM 2007.
