A Latent-Dirichlet-Allocation Based Extension for Domain Ontology of Enterprise’s Technological Innovation

This paper proposed a method for building enterprise's technological innovation domain ontology automatically from plain text corpus based on Latent Dirichlet Allocation (LDA). The proposed method consisted of four modules: 1) introducing the seed ontology for domain of enterprise's techno...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International Journal of Computers Communications & Control 2019-02, Vol.14 (1), p.107-123
Hauptverfasser: Zhang, Qianqian, Liu, Shifeng, Gong, Daqing, Tu, Qun
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 123
container_issue 1
container_start_page 107
container_title International Journal of Computers Communications & Control
container_volume 14
creator Zhang, Qianqian
Liu, Shifeng
Gong, Daqing
Tu, Qun
description This paper proposed a method for building enterprise's technological innovation domain ontology automatically from plain text corpus based on Latent Dirichlet Allocation (LDA). The proposed method consisted of four modules: 1) introducing the seed ontology for domain of enterprise's technological innovation, 2) using Natural Language Processing (NLP) technique to preprocess the collected textual data, 3) mining domain specific terms from document collections based on LDA, 4) obtaining the relationship between the terms through the defined relevant rules. The experiments have been carried out to demonstrate the effectiveness of this method and the results indicated that many terms in domain of enterprise's technological innovation and the semantic relations between terms are discovered. The proposed method is a process of continuously cycles and iterations, that is the obtained objective ontology can be re-iterated as initial seed ontology. The constant knowledge acquisition in the domain of enterprise's technological innovation to update and perfect the initial seed ontology.
doi_str_mv 10.15837/ijccc.2019.1.3366
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2518342779</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2518342779</sourcerecordid><originalsourceid>FETCH-LOGICAL-c275t-2aeab8c1c3218be5fbdb9b951bb51b572bee69b548ddd3ae61dd42bc7453bb5d3</originalsourceid><addsrcrecordid>eNpNkL1OwzAQgC0EEhX0BZgsMSfEdpw4Y2kLVKrUpcyW_0JdpXaxXUQ3XoPX40lIWgaG093pPt3pPgDuUJEjykj9YLdKqRwXqMlRTkhVXYARYiXKGkaqy3_1NRjHaGVBMCloxeoR6CZwKZJxKZvZYNWmMymbdJ1XIlnv4KOIRsP5Z0_EoW99gDO_E9bBlUu-829H6Fs4d8mEfbDR_Hx9R7g2auOGoVWigwvn_Mdp3S24akUXzfgv34DXp_l6-pItV8-L6WSZKVzTlGFhhGQKKYIRk4a2UstGNhRJ2QetsTSmaiQtmdaaCFMhrUssVV1S0iOa3ID789598O8HExPf-kNw_UmOKWKkxHXd9BQ-Uyr4GINpef_BToQjRwU_ieUnsXwQyxEfxJJfvOdwVw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2518342779</pqid></control><display><type>article</type><title>A Latent-Dirichlet-Allocation Based Extension for Domain Ontology of Enterprise’s Technological Innovation</title><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Zhang, Qianqian ; Liu, Shifeng ; Gong, Daqing ; Tu, Qun</creator><creatorcontrib>Zhang, Qianqian ; Liu, Shifeng ; Gong, Daqing ; Tu, Qun</creatorcontrib><description>This paper proposed a method for building enterprise's technological innovation domain ontology automatically from plain text corpus based on Latent Dirichlet Allocation (LDA). The proposed method consisted of four modules: 1) introducing the seed ontology for domain of enterprise's technological innovation, 2) using Natural Language Processing (NLP) technique to preprocess the collected textual data, 3) mining domain specific terms from document collections based on LDA, 4) obtaining the relationship between the terms through the defined relevant rules. The experiments have been carried out to demonstrate the effectiveness of this method and the results indicated that many terms in domain of enterprise's technological innovation and the semantic relations between terms are discovered. The proposed method is a process of continuously cycles and iterations, that is the obtained objective ontology can be re-iterated as initial seed ontology. The constant knowledge acquisition in the domain of enterprise's technological innovation to update and perfect the initial seed ontology.</description><identifier>ISSN: 1841-9836</identifier><identifier>EISSN: 1841-9836</identifier><identifier>EISSN: 1841-9844</identifier><identifier>DOI: 10.15837/ijccc.2019.1.3366</identifier><language>eng</language><publisher>Oradea: Agora University of Oradea</publisher><subject>Dirichlet problem ; Innovations ; Knowledge acquisition ; Natural language processing ; Ontology ; Technological change</subject><ispartof>International Journal of Computers Communications &amp; Control, 2019-02, Vol.14 (1), p.107-123</ispartof><rights>2019. This work is published under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c275t-2aeab8c1c3218be5fbdb9b951bb51b572bee69b548ddd3ae61dd42bc7453bb5d3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,27901,27902</link.rule.ids></links><search><creatorcontrib>Zhang, Qianqian</creatorcontrib><creatorcontrib>Liu, Shifeng</creatorcontrib><creatorcontrib>Gong, Daqing</creatorcontrib><creatorcontrib>Tu, Qun</creatorcontrib><title>A Latent-Dirichlet-Allocation Based Extension for Domain Ontology of Enterprise’s Technological Innovation</title><title>International Journal of Computers Communications &amp; Control</title><description>This paper proposed a method for building enterprise's technological innovation domain ontology automatically from plain text corpus based on Latent Dirichlet Allocation (LDA). The proposed method consisted of four modules: 1) introducing the seed ontology for domain of enterprise's technological innovation, 2) using Natural Language Processing (NLP) technique to preprocess the collected textual data, 3) mining domain specific terms from document collections based on LDA, 4) obtaining the relationship between the terms through the defined relevant rules. The experiments have been carried out to demonstrate the effectiveness of this method and the results indicated that many terms in domain of enterprise's technological innovation and the semantic relations between terms are discovered. The proposed method is a process of continuously cycles and iterations, that is the obtained objective ontology can be re-iterated as initial seed ontology. The constant knowledge acquisition in the domain of enterprise's technological innovation to update and perfect the initial seed ontology.</description><subject>Dirichlet problem</subject><subject>Innovations</subject><subject>Knowledge acquisition</subject><subject>Natural language processing</subject><subject>Ontology</subject><subject>Technological change</subject><issn>1841-9836</issn><issn>1841-9836</issn><issn>1841-9844</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpNkL1OwzAQgC0EEhX0BZgsMSfEdpw4Y2kLVKrUpcyW_0JdpXaxXUQ3XoPX40lIWgaG093pPt3pPgDuUJEjykj9YLdKqRwXqMlRTkhVXYARYiXKGkaqy3_1NRjHaGVBMCloxeoR6CZwKZJxKZvZYNWmMymbdJ1XIlnv4KOIRsP5Z0_EoW99gDO_E9bBlUu-829H6Fs4d8mEfbDR_Hx9R7g2auOGoVWigwvn_Mdp3S24akUXzfgv34DXp_l6-pItV8-L6WSZKVzTlGFhhGQKKYIRk4a2UstGNhRJ2QetsTSmaiQtmdaaCFMhrUssVV1S0iOa3ID789598O8HExPf-kNw_UmOKWKkxHXd9BQ-Uyr4GINpef_BToQjRwU_ieUnsXwQyxEfxJJfvOdwVw</recordid><startdate>20190201</startdate><enddate>20190201</enddate><creator>Zhang, Qianqian</creator><creator>Liu, Shifeng</creator><creator>Gong, Daqing</creator><creator>Tu, Qun</creator><general>Agora University of Oradea</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20190201</creationdate><title>A Latent-Dirichlet-Allocation Based Extension for Domain Ontology of Enterprise’s Technological Innovation</title><author>Zhang, Qianqian ; Liu, Shifeng ; Gong, Daqing ; Tu, Qun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c275t-2aeab8c1c3218be5fbdb9b951bb51b572bee69b548ddd3ae61dd42bc7453bb5d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Dirichlet problem</topic><topic>Innovations</topic><topic>Knowledge acquisition</topic><topic>Natural language processing</topic><topic>Ontology</topic><topic>Technological change</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Qianqian</creatorcontrib><creatorcontrib>Liu, Shifeng</creatorcontrib><creatorcontrib>Gong, Daqing</creatorcontrib><creatorcontrib>Tu, Qun</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>International Journal of Computers Communications &amp; Control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Qianqian</au><au>Liu, Shifeng</au><au>Gong, Daqing</au><au>Tu, Qun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Latent-Dirichlet-Allocation Based Extension for Domain Ontology of Enterprise’s Technological Innovation</atitle><jtitle>International Journal of Computers Communications &amp; Control</jtitle><date>2019-02-01</date><risdate>2019</risdate><volume>14</volume><issue>1</issue><spage>107</spage><epage>123</epage><pages>107-123</pages><issn>1841-9836</issn><eissn>1841-9836</eissn><eissn>1841-9844</eissn><abstract>This paper proposed a method for building enterprise's technological innovation domain ontology automatically from plain text corpus based on Latent Dirichlet Allocation (LDA). The proposed method consisted of four modules: 1) introducing the seed ontology for domain of enterprise's technological innovation, 2) using Natural Language Processing (NLP) technique to preprocess the collected textual data, 3) mining domain specific terms from document collections based on LDA, 4) obtaining the relationship between the terms through the defined relevant rules. The experiments have been carried out to demonstrate the effectiveness of this method and the results indicated that many terms in domain of enterprise's technological innovation and the semantic relations between terms are discovered. The proposed method is a process of continuously cycles and iterations, that is the obtained objective ontology can be re-iterated as initial seed ontology. The constant knowledge acquisition in the domain of enterprise's technological innovation to update and perfect the initial seed ontology.</abstract><cop>Oradea</cop><pub>Agora University of Oradea</pub><doi>10.15837/ijccc.2019.1.3366</doi><tpages>17</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1841-9836
ispartof International Journal of Computers Communications & Control, 2019-02, Vol.14 (1), p.107-123
issn 1841-9836
1841-9836
1841-9844
language eng
recordid cdi_proquest_journals_2518342779
source DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Dirichlet problem
Innovations
Knowledge acquisition
Natural language processing
Ontology
Technological change
title A Latent-Dirichlet-Allocation Based Extension for Domain Ontology of Enterprise’s Technological Innovation
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T09%3A27%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Latent-Dirichlet-Allocation%20Based%20Extension%20for%20Domain%20Ontology%20of%20Enterprise%E2%80%99s%20Technological%20Innovation&rft.jtitle=International%20Journal%20of%20Computers%20Communications%20&%20Control&rft.au=Zhang,%20Qianqian&rft.date=2019-02-01&rft.volume=14&rft.issue=1&rft.spage=107&rft.epage=123&rft.pages=107-123&rft.issn=1841-9836&rft.eissn=1841-9836&rft_id=info:doi/10.15837/ijccc.2019.1.3366&rft_dat=%3Cproquest_cross%3E2518342779%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2518342779&rft_id=info:pmid/&rfr_iscdi=true