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dc.contributor.authorHassan, Bryar A
dc.contributor.authorRashid, Tarik A
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2021-07-07T01:02:17Z
dc.date.available2021-07-07T01:02:17Z
dc.date.issued2021
dc.identifier.issn2199-4536en_US
dc.identifier.doi10.1007/s40747-021-00422-wen_US
dc.identifier.urihttp://hdl.handle.net/10072/405748
dc.description.abstractIt is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using formal context analysis (FCA). However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is require to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising formal concept analysis (FCA); (2) A framework to decrease the formal context’s ambiguity of the first framework using an adaptive version of evolutionary clustering algorithm (ECA*). Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms (Fuzzy K-means, JBOS approach, AddIntent algorithm, and FastAddExtent) to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherSpringeren_US
dc.relation.ispartofjournalComplex & Intelligent Systemsen_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processingen_US
dc.subject.fieldofresearchcode0801en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsComputer Scienceen_US
dc.subject.keywordsConcept hierarchiesen_US
dc.titleFormal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm staren_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationHassan, BA; Rashid, TA; Mirjalili, S, Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star, Complex & Intelligent Systems, 2021en_US
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/en_US
dc.date.updated2021-07-06T22:37:39Z
dc.description.versionVersion of Record (VoR)en_US
gro.description.notepublicThis publication has been entered in Griffith Research Online as an advanced online version.en_US
gro.rights.copyright© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.en_US
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gro.griffith.authorMirjalili, Seyedali


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