Learning Taxonomic Relations from Heterogeneous Sources of Evidence

  title={Learning Taxonomic Relations from Heterogeneous Sources of Evidence},
  author={Philipp Cimiano and Aleksander Pivk and Lars Schmidt-Thieme and Steffen Staab},
We present a novel approach to learning taxonomic relations between terms by considering multiple and heterogeneous sources of vidence. In order to derive an optimal combination of these sources, we exploit a machine-learning approach, representing all the sources of evidence as first-or der features and training standard classifiers. We consider in particular different f a ures derived from WordNet, an approach matching Hearst-style patterns in a corpus and on the Web as well as further… CONTINUE READING
Highly Influential
This paper has highly influenced 15 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 193 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.
Showing 1-10 of 112 extracted citations

Knowledge-based interaction in software development

Intelligent Decision Technologies • 2011
View 5 Excerpts
Highly Influenced

Leveraging Software Reuse with Knowledge Management in Software Development

International Journal of Software Engineering and Knowledge Engineering • 2011
View 8 Excerpts
Highly Influenced

A Survey of Domain Ontology Engineering: Methods and Tools

Advances in Intelligent Tutoring Systems • 2010
View 4 Excerpts
Highly Influenced

194 Citations

Citations per Year
Semantic Scholar estimates that this publication has 194 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 29 references

Deriving Concept Hierarchies from Text

View 8 Excerpts
Highly Influenced

Eva lu tion of ontolearn

P. Velardi, R. Navigli, A. Cuchiarelli, F. Neri
a methodology for automatic population of domain ontologies. In P. Buitel aar, P. Cimiano, and B. Magnini, editors,Ontology Learning from Text: Methods, Applications and Eva luation. IOS Press • 2005
View 3 Excerpts

Similar Papers

Loading similar papers…