Difference between revisions of "Wu and Weld WWW 2008"
From Cohen Courses
Jump to navigationJump to search| Line 14: | Line 14: | ||
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 '''[http://en.wikipedia.org/wiki/is-a is-a]''' relations between infobox classes (e.g. "volleyball player" is-a "athlete"); | + | * 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 subsumption detection task is modeled as a binary classification problem and several intuitive indicators are used as features to train the classifiers: | The subsumption detection task is modeled as a binary classification problem and several intuitive indicators are used as features to train the classifiers: | ||
| − | * | + | * Similarity measure: the similarity between two infobox classes, measured using the TF/IDF scores between bags of words taken from their attribute set, the first sentence of each of their instances (articles) and their category tags. |
| + | * Class-name string inclusion: whether the name of a class is a substring of another one (e.g. "English public school" '''is-a''' "public school"). | ||
| + | * Category tags: whether the name of a class is found in the infobox template category tag. | ||
| + | * Edit history: the edit pattern of an instance, because a Wikipedia author tends to specialize rather than generalize when changing the type of an article. | ||
| + | * Hearst patterns: the number of Google hits for match phrases of the form "Class1, like Class2" or "Class1 such as Class2" (e.g. "...''scientists'' such as ''chemists'', ''phsycists''..."). | ||
| + | * Wordnet mapping: a bunch of heuristics is used to compute a mapping between a WordNet node and an infobox class and whether a corresponding node of another class is also used as a feature for classification. | ||
== Experimental result == | == Experimental result == | ||
Revision as of 00:30, 26 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 subsumption detection task is modeled as a binary classification problem and several intuitive indicators are used as features to train the classifiers:
- Similarity measure: the similarity between two infobox classes, measured using the TF/IDF scores between bags of words taken from their attribute set, the first sentence of each of their instances (articles) and their category tags.
- Class-name string inclusion: whether the name of a class is a substring of another one (e.g. "English public school" is-a "public school").
- Category tags: whether the name of a class is found in the infobox template category tag.
- Edit history: the edit pattern of an instance, because a Wikipedia author tends to specialize rather than generalize when changing the type of an article.
- Hearst patterns: the number of Google hits for match phrases of the form "Class1, like Class2" or "Class1 such as Class2" (e.g. "...scientists such as chemists, phsycists...").
- Wordnet mapping: a bunch of heuristics is used to compute a mapping between a WordNet node and an infobox class and whether a corresponding node of another class is also used as a feature for classification.
Experimental result
...
Related papers
This paper is based on Wu and Weld CIKM 2007.
