Determining Automatically the Size of Learned Ontologies

Authors:
Elias Zavitsanos;Sergios Petridis;Georgios Paliouras;George A. Vouros
Affiliations:
Inst. of Informatics and Telecommunications, NCSR “Demokritos”, Greece, email: izavits@iit.demokritos.gr and University of Aegean, Dpt. of Information and Communication Systems Enginee ...;Inst. of Informatics and Telecommunications, NCSR “Demokritos”, Greece, email: petridis@iit.demokritos.gr;Inst. of Informatics and Telecommunications, NCSR “Demokritos”, Greece, email: paliourg@iit.demokritos.gr;University of Aegean, Dpt. of Information and Communication Systems Engineering, Greece, email: georgev@aegean.gr
Venue:
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Year:
2008

Citing 2
Cited 2

Latent dirichlet allocation

The Journal of Machine Learning Research
Discovering Subsumption Hierarchies of Ontology Concepts from Text Corpora

WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence

Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes

The Journal of Machine Learning Research
On finding the natural number of topics with latent dirichlet allocation: some observations

PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Determining the size of an ontology that is automatically learned from texts is an open issue. In this paper, we study the similarity between ontology concepts at different levels of a taxonomy, quantifying in a natural manner the quality of the ontology attained. Our approach is integrated in a method for language-neutral learning of ontologies from texts, which relies on conditional independence tests over thematic topics that are discovered using LDA.