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Terminological ontology learning and population using latent Dirichlet allocation.

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Title: Terminological ontology learning and population using latent Dirichlet allocation.
Authors: Colace, Francesco1 fcolace@unisa.it, De Santo, Massimo1 desanto@unisa.it, Greco, Luca1 lgreco@unisa.it, Amato, Flora2 flora.amato@unisa.it, Moscato, Vincenzo2 vmoscato@unisa.it, Picariello, Antonio2 picus@unisa.it
Source: Journal of Visual Languages & Computing. Dec2014, Vol. 25 Issue 6, p818-826. 9p.
Subjects: Machine learning, Data structures, Dirichlet problem, Natural language processing, Semantic Web
Abstract: The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is still a lack of general methodologies for ontology automatic learning and population, i.e. the generation of domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques In this paper, the authors present an ontology learning and population system that combines both statistical and semantic methodologies. Several experiments have been carried out, demonstrating the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Visual Languages & Computing is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Colace%2C+Francesco%22">Colace, Francesco</searchLink><relatesTo>1</relatesTo><i> fcolace@unisa.it</i><br /><searchLink fieldCode="AR" term="%22De+Santo%2C+Massimo%22">De Santo, Massimo</searchLink><relatesTo>1</relatesTo><i> desanto@unisa.it</i><br /><searchLink fieldCode="AR" term="%22Greco%2C+Luca%22">Greco, Luca</searchLink><relatesTo>1</relatesTo><i> lgreco@unisa.it</i><br /><searchLink fieldCode="AR" term="%22Amato%2C+Flora%22">Amato, Flora</searchLink><relatesTo>2</relatesTo><i> flora.amato@unisa.it</i><br /><searchLink fieldCode="AR" term="%22Moscato%2C+Vincenzo%22">Moscato, Vincenzo</searchLink><relatesTo>2</relatesTo><i> vmoscato@unisa.it</i><br /><searchLink fieldCode="AR" term="%22Picariello%2C+Antonio%22">Picariello, Antonio</searchLink><relatesTo>2</relatesTo><i> picus@unisa.it</i>
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Dirichlet+problem%22">Dirichlet problem</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Semantic+Web%22">Semantic Web</searchLink>
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  Data: The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is still a lack of general methodologies for ontology automatic learning and population, i.e. the generation of domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques In this paper, the authors present an ontology learning and population system that combines both statistical and semantic methodologies. Several experiments have been carried out, demonstrating the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Visual Languages & Computing is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.jvlc.2014.11.001
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        Text: English
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              Text: Dec2014
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