Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.eswa.2011.03.090
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dc.titleGRAONTO: A graph-based approach for automatic construction of domain ontology
dc.contributor.authorHou, X.
dc.contributor.authorOng, S.K.
dc.contributor.authorNee, A.Y.C.
dc.contributor.authorZhang, X.T.
dc.contributor.authorLiu, W.J.
dc.date.accessioned2014-06-17T06:22:56Z
dc.date.available2014-06-17T06:22:56Z
dc.date.issued2011-09
dc.identifier.citationHou, X., Ong, S.K., Nee, A.Y.C., Zhang, X.T., Liu, W.J. (2011-09). GRAONTO: A graph-based approach for automatic construction of domain ontology. Expert Systems with Applications 38 (9) : 11958-11975. ScholarBank@NUS Repository. https://doi.org/10.1016/j.eswa.2011.03.090
dc.identifier.issn09574174
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/60415
dc.description.abstractExtracting domain knowledge and taking its full advantage has been an important way to reducing costs and accelerating processes in domain-related applications. Domain ontology, providing a common and unambiguous understanding of a domain for both the users and the system to communicate with each other via a set of representational primitives, has been proposed as an important and natural approach to represent domain knowledge. Most domain knowledge about domain entities with their properties and relationships is embodied in document collections. Thus, extracting ontologies from these documents is an important means of ontology construction. In this paper, a graph-based approach for automatic construction of domain ontology from domain corpus, named GRAONTO, has been proposed. First, each document in the collection is represented by a graph. After the generation of document graphs, random walk term weighting is employed to estimate the relevance of the information of a term to the corpus from both local and global perspectives. Next, the MCL (Markov Clustering) algorithm is used to disambiguate terms with different meanings and group similar terms to produce concepts. Next, an improved gSpan algorithm constrained by both vertices and informativeness is exploited to find arbitrary latent relations among these concepts. Finally, the domain ontology is output in the OWL format. For ontology evaluation purposes, a method for adaptive adjustment of concepts and relations with respect to its practical effectiveness is conceived. Evaluation experiments show that GRAONTO is a promising approach for domain ontology construction. © 2011 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.eswa.2011.03.090
dc.sourceScopus
dc.subjectAutomatic construction of domain ontology
dc.subjectDocument graph
dc.subjectFrequent subgraph mining
dc.subjectGraph clustering
dc.subjectInformative subgraph
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1016/j.eswa.2011.03.090
dc.description.sourcetitleExpert Systems with Applications
dc.description.volume38
dc.description.issue9
dc.description.page11958-11975
dc.description.codenESAPE
dc.identifier.isiut000291118500141
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