Ontology Learning Through Focused Crawling and Information Extraction. Luong, H., P., Gauch, S., & Wang, Q. In 2009 International Conference on Knowledge and Systems Engineering, pages 106-112, 2009. Ieee.
Ontology Learning Through Focused Crawling and Information Extraction [link]Website  abstract   bibtex   
Ontology learning aims to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, there are few studies that attempt to automate the entire ontology learning process from the collection of domain-specific literature, to text mining to build new ontologies or enrich existing ones. In this paper, we present a complete framework for ontology learning that enables us to retrieve documents from the Web using focused crawling, and then use a SVM (support vector machine) classifier to identify domain-specific documents and perform text mining in order to extract useful information for the ontology enrichment process. We have carried out several experiments on components of this framework in a biological domain, amphibian morphology. This paper reports on the overall system architecture and our initial experiments on information extraction using text mining techniques to enrich the domain ontology.
@inProceedings{
 title = {Ontology Learning Through Focused Crawling and Information Extraction},
 type = {inProceedings},
 year = {2009},
 identifiers = {[object Object]},
 keywords = {focused crawling,information extreaction,ontology learning,svm,text mining},
 pages = {106-112},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5361721},
 publisher = {Ieee},
 id = {81194751-2dc6-3306-b131-6fde4ee25e75},
 created = {2011-02-27T18:33:21.000Z},
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 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
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 last_modified = {2017-03-14T14:36:19.698Z},
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 citation_key = {Luong2009},
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 abstract = {Ontology learning aims to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, there are few studies that attempt to automate the entire ontology learning process from the collection of domain-specific literature, to text mining to build new ontologies or enrich existing ones. In this paper, we present a complete framework for ontology learning that enables us to retrieve documents from the Web using focused crawling, and then use a SVM (support vector machine) classifier to identify domain-specific documents and perform text mining in order to extract useful information for the ontology enrichment process. We have carried out several experiments on components of this framework in a biological domain, amphibian morphology. This paper reports on the overall system architecture and our initial experiments on information extraction using text mining techniques to enrich the domain ontology.},
 bibtype = {inProceedings},
 author = {Luong, Hiep Phuc and Gauch, Susan and Wang, Qiang},
 booktitle = {2009 International Conference on Knowledge and Systems Engineering}
}

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