%0 Journal Article %1 wang2009probabilistic %A Wang, Wei %A Barnaghi, Payam %A Bargiela, Andrzej %C Los Alamitos, CA, USA %D 2009 %I IEEE Computer Society %J IEEE Transactions on Knowledge and Data Engineering %K toread ol_web2.0 toread_dbe methods_from_text %N RapidPosts %R http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.122 %T Probabilistic Topic Models for Learning Terminological Ontologies %V 99 %X Probabilistic topic models were originally developed and utilised for document modeling and topic extraction in Information Retrieval. In this paper we describe a new approach for automatic learning of terminological ontologies from text corpus based on such models. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-document interpreted in terms of probability distributions. We propose two algorithms for learning terminological ontologies using the principle of topic relationship and exploiting information theory with the probabilistic topic models learned. Experiments with different model parameters were conducted and learned ontology statements were evaluated by the domain experts. We have also compared the results of our method with two existing concept hierarchy learning methods on the same dataset. The study shows that our method outperforms other methods in terms of recall and precision measures. The precision level of the learned ontology is sufficient for it to be deployed for the purpose of browsing, navigation, and information search and retrieval in digital libraries.

@article{wang2009probabilistic, abstract = {Probabilistic topic models were originally developed and utilised for document modeling and topic extraction in Information Retrieval. In this paper we describe a new approach for automatic learning of terminological ontologies from text corpus based on such models. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-document interpreted in terms of probability distributions. We propose two algorithms for learning terminological ontologies using the principle of topic relationship and exploiting information theory with the probabilistic topic models learned. Experiments with different model parameters were conducted and learned ontology statements were evaluated by the domain experts. We have also compared the results of our method with two existing concept hierarchy learning methods on the same dataset. The study shows that our method outperforms other methods in terms of recall and precision measures. The precision level of the learned ontology is sufficient for it to be deployed for the purpose of browsing, navigation, and information search and retrieval in digital libraries.}, added-at = {2011-02-17T17:43:22.000+0100}, address = {Los Alamitos, CA, USA}, author = {Wang, Wei and Barnaghi, Payam and Bargiela, Andrzej}, biburl = {https://www.bibsonomy.org/bibtex/2478d8613c72a970a99837fa59989cfff/dbenz}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.122}, file = {wang2009probabilistic.pdf:wang2009probabilistic.pdf:PDF}, groups = {public}, interhash = {c8f8ce2267199bd80eeda718833ba55e}, intrahash = {478d8613c72a970a99837fa59989cfff}, issn = {1041-4347}, journal = {IEEE Transactions on Knowledge and Data Engineering}, journalpub = {1}, keywords = {toread ol_web2.0 toread_dbe methods_from_text}, number = {RapidPosts}, publisher = {IEEE Computer Society}, timestamp = {2013-07-31T15:39:42.000+0200}, title = {Probabilistic Topic Models for Learning Terminological Ontologies}, username = {dbenz}, volume = 99, year = 2009 }