Knowledge Discovery from Text Learning for Ontology Modeling
This paper presents a methodology of knowledge discovery from text learning for ontology modeling. Knowledge written in text is always hard to be extracted by automated process, and most existing ontologies are defined manually. Those ontologies are not comprehensive enough to express most human kno...
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| creator | Lim, E.H.Y. Liu, J.N.K. Lee, R.S.T. |
| description | This paper presents a methodology of knowledge discovery from text learning for ontology modeling. Knowledge written in text is always hard to be extracted by automated process, and most existing ontologies are defined manually. Those ontologies are not comprehensive enough to express most human knowledge in the real world. Therefore, the most efficient way to identify knowledge is discovering it from rich text. In this paper, we proposed a statistical based method to measure the relation of appearing frequency of word in text. The method identifies and discovers knowledge by automated process. We also defined ontology model - ontology graph, to express knowledge, the graph facilitates machine and human processing. The extracted knowledge in the graph format can aid user to revise and define ontology knowledge more effectively and accurately. |
| doi_str_mv | 10.1109/FSKD.2009.669 |
| format | Conference Proceeding |
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Knowledge written in text is always hard to be extracted by automated process, and most existing ontologies are defined manually. Those ontologies are not comprehensive enough to express most human knowledge in the real world. Therefore, the most efficient way to identify knowledge is discovering it from rich text. In this paper, we proposed a statistical based method to measure the relation of appearing frequency of word in text. The method identifies and discovers knowledge by automated process. We also defined ontology model - ontology graph, to express knowledge, the graph facilitates machine and human processing. The extracted knowledge in the graph format can aid user to revise and define ontology knowledge more effectively and accurately.</description><identifier>ISBN: 9780769537351</identifier><identifier>ISBN: 0769537359</identifier><identifier>DOI: 10.1109/FSKD.2009.669</identifier><identifier>LCCN: 2009906464</identifier><language>eng</language><publisher>IEEE</publisher><subject>Content management ; Frequency measurement ; Fuzzy systems ; Humans ; Intelligent systems ; knowledge discovery ; Knowledge representation ; Learning systems ; Machine learning ; Natural languages ; Ontologies ; ontology ; text learning</subject><ispartof>2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009, Vol.7, p.227-231</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5359987$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5359987$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lim, E.H.Y.</creatorcontrib><creatorcontrib>Liu, J.N.K.</creatorcontrib><creatorcontrib>Lee, R.S.T.</creatorcontrib><title>Knowledge Discovery from Text Learning for Ontology Modeling</title><title>2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery</title><addtitle>FSKD</addtitle><description>This paper presents a methodology of knowledge discovery from text learning for ontology modeling. Knowledge written in text is always hard to be extracted by automated process, and most existing ontologies are defined manually. Those ontologies are not comprehensive enough to express most human knowledge in the real world. Therefore, the most efficient way to identify knowledge is discovering it from rich text. In this paper, we proposed a statistical based method to measure the relation of appearing frequency of word in text. The method identifies and discovers knowledge by automated process. We also defined ontology model - ontology graph, to express knowledge, the graph facilitates machine and human processing. The extracted knowledge in the graph format can aid user to revise and define ontology knowledge more effectively and accurately.</description><subject>Content management</subject><subject>Frequency measurement</subject><subject>Fuzzy systems</subject><subject>Humans</subject><subject>Intelligent systems</subject><subject>knowledge discovery</subject><subject>Knowledge representation</subject><subject>Learning systems</subject><subject>Machine learning</subject><subject>Natural languages</subject><subject>Ontologies</subject><subject>ontology</subject><subject>text learning</subject><isbn>9780769537351</isbn><isbn>0769537359</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj01LxDAURQMyoI5dunKTP9CaryZ94EZmnFGmMgu7H9LmpVQ6jaRF7b-3ondz4Vw4cAm55SzjnMH97u2wzQRjkGkNFyQBUzCjIZdG5nxFrn8nYFppdUmScXxnSyQoUMUVeTgM4atH1yLddmMTPjHO1MdwphV-T7REG4duaKkPkR6HKfShnelrcNgv9IasvO1HTP57TardU7V5Tsvj_mXzWKYdsCl1Hg3DWim0njdCOV870EJoZpmzwoFtELk1UgohuC2K2poGl2NSO8PRyTW5-9N2iHj6iN3ZxvmUyxygMPIHsMBI_g</recordid><startdate>200908</startdate><enddate>200908</enddate><creator>Lim, E.H.Y.</creator><creator>Liu, J.N.K.</creator><creator>Lee, R.S.T.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200908</creationdate><title>Knowledge Discovery from Text Learning for Ontology Modeling</title><author>Lim, E.H.Y. ; Liu, J.N.K. ; Lee, R.S.T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-dfe70eb44eaf1c24dfbd962260a0da2d9acee1a7332221a88ba7ce10936d71ed3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Content management</topic><topic>Frequency measurement</topic><topic>Fuzzy systems</topic><topic>Humans</topic><topic>Intelligent systems</topic><topic>knowledge discovery</topic><topic>Knowledge representation</topic><topic>Learning systems</topic><topic>Machine learning</topic><topic>Natural languages</topic><topic>Ontologies</topic><topic>ontology</topic><topic>text learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Lim, E.H.Y.</creatorcontrib><creatorcontrib>Liu, J.N.K.</creatorcontrib><creatorcontrib>Lee, R.S.T.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lim, E.H.Y.</au><au>Liu, J.N.K.</au><au>Lee, R.S.T.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Knowledge Discovery from Text Learning for Ontology Modeling</atitle><btitle>2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery</btitle><stitle>FSKD</stitle><date>2009-08</date><risdate>2009</risdate><volume>7</volume><spage>227</spage><epage>231</epage><pages>227-231</pages><isbn>9780769537351</isbn><isbn>0769537359</isbn><abstract>This paper presents a methodology of knowledge discovery from text learning for ontology modeling. Knowledge written in text is always hard to be extracted by automated process, and most existing ontologies are defined manually. Those ontologies are not comprehensive enough to express most human knowledge in the real world. Therefore, the most efficient way to identify knowledge is discovering it from rich text. In this paper, we proposed a statistical based method to measure the relation of appearing frequency of word in text. The method identifies and discovers knowledge by automated process. We also defined ontology model - ontology graph, to express knowledge, the graph facilitates machine and human processing. The extracted knowledge in the graph format can aid user to revise and define ontology knowledge more effectively and accurately.</abstract><pub>IEEE</pub><doi>10.1109/FSKD.2009.669</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009, Vol.7, p.227-231 |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Content management Frequency measurement Fuzzy systems Humans Intelligent systems knowledge discovery Knowledge representation Learning systems Machine learning Natural languages Ontologies ontology text learning |
| title | Knowledge Discovery from Text Learning for Ontology Modeling |
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