Name
Abstract | ||
|---|---|---|
The identification and labelling of non-hierarchical relations are among the most challenging tasks in ontology learning. This paper describes a bottom-up approach for automatically suggesting ontology link types. The presented method extracts verb-vectors from semantic relations identified in the domain corpus, aggregates them by computing centroids for known relation types, and stores the centroids in a central knowledge base. Comparing verb-vectors extracted from unknown relations with the stored centroids yields link type suggestions. Domain experts evaluate these suggestions, refining the knowledge base and constantly improving the component's accuracy. A final evaluation provides a detailed statistical analysis of the introduced approach. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1504/IJMSO.2009.027755 | IJMSO |
Keywords | DocType | Volume |
domain ontology,challenging task,ontology learning,link type detection,domain expert,centroids yields link type,ontology extension,non-taxonomic relation,ontology link type,knowledge base,vector space model,non-hierarchical relations,method extracts verb-vectors,non-taxonomic relations,bottom-up approach,domain corpus,central knowledge base | Journal | 4 |
Issue | Citations | PageRank |
3 | 10 | 0.61 |
References | Authors | |
19 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Albert Weichselbraun | 1 | 291 | 28.39 |
| Gerhard Wohlgenannt | 2 | 73 | 14.78 |
| Arno Scharl | 3 | 696 | 67.13 |
| Michael Granitzer | 4 | 822 | 80.14 |
| Thomas Neidhart | 5 | 21 | 3.23 |
| Andreas Juffinger | 6 | 68 | 9.22 |