Semantics representation in a sentence with concept relational model (CRM)
The current way of representing semantics or meaning in a sentence is by using the conceptual graphs. Conceptual graphs define concepts and conceptual relations loosely. This causes ambiguity because a word can be classified as a concept or relation. Ambiguity disrupts the process of recognizing gra...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Universiti Utara Malaysia
2008
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| Online Access: | http://psasir.upm.edu.my/id/eprint/59727/ http://psasir.upm.edu.my/id/eprint/59727/1/112-116-CR80.pdf |
| _version_ | 1848854002159058944 |
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| author | Abdullah, Rusli Abdul Hamid, Jamaliah Selamat, Mohd Hasan Ibrahim, Hamidah Ungku Chulan, Ungku Azmi Iskandar |
| author_facet | Abdullah, Rusli Abdul Hamid, Jamaliah Selamat, Mohd Hasan Ibrahim, Hamidah Ungku Chulan, Ungku Azmi Iskandar |
| author_sort | Abdullah, Rusli |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The current way of representing semantics or meaning in a sentence is by using the conceptual graphs. Conceptual graphs define concepts and conceptual relations loosely. This causes ambiguity because a word can be classified as a concept or relation. Ambiguity disrupts the process of recognizing graphs similarity, rendering difficulty to multiple graphs interaction. Relational flow is also altered in conceptual graphs when additional linguistic information is input. Inconsistency of relational flow is caused by the bipartite structure of conceptual graphs that only allows the representation of connection between concept and relations but never between relations per se. To overcome the problem of ambiguity, the concept relational model (CRM) described in this article strictly organizes word classes into three main categories; concept, relation and attribute. To do so, CRM begins by tagging the words in text and proceeds by classifying them according to a predefined mapping. In addition, CRM maintains the consistency of the relational flow by allowing connection between multiple relations as well. CRM then uses a set of canonical graphs to be worked on these newly classified components for the representation of semantics. The overall result is better accuracy in text engineering related task like relation extraction. |
| first_indexed | 2025-11-15T11:02:56Z |
| format | Conference or Workshop Item |
| id | upm-59727 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:02:56Z |
| publishDate | 2008 |
| publisher | Universiti Utara Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-597272018-03-21T03:33:23Z http://psasir.upm.edu.my/id/eprint/59727/ Semantics representation in a sentence with concept relational model (CRM) Abdullah, Rusli Abdul Hamid, Jamaliah Selamat, Mohd Hasan Ibrahim, Hamidah Ungku Chulan, Ungku Azmi Iskandar The current way of representing semantics or meaning in a sentence is by using the conceptual graphs. Conceptual graphs define concepts and conceptual relations loosely. This causes ambiguity because a word can be classified as a concept or relation. Ambiguity disrupts the process of recognizing graphs similarity, rendering difficulty to multiple graphs interaction. Relational flow is also altered in conceptual graphs when additional linguistic information is input. Inconsistency of relational flow is caused by the bipartite structure of conceptual graphs that only allows the representation of connection between concept and relations but never between relations per se. To overcome the problem of ambiguity, the concept relational model (CRM) described in this article strictly organizes word classes into three main categories; concept, relation and attribute. To do so, CRM begins by tagging the words in text and proceeds by classifying them according to a predefined mapping. In addition, CRM maintains the consistency of the relational flow by allowing connection between multiple relations as well. CRM then uses a set of canonical graphs to be worked on these newly classified components for the representation of semantics. The overall result is better accuracy in text engineering related task like relation extraction. Universiti Utara Malaysia 2008 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/59727/1/112-116-CR80.pdf Abdullah, Rusli and Abdul Hamid, Jamaliah and Selamat, Mohd Hasan and Ibrahim, Hamidah and Ungku Chulan, Ungku Azmi Iskandar (2008) Semantics representation in a sentence with concept relational model (CRM). In: Knowledge Management International Conference 2008 (KMICe 2008), 10-12 June 2008, Langkawi, Kedah. (pp. 112-116). |
| spellingShingle | Abdullah, Rusli Abdul Hamid, Jamaliah Selamat, Mohd Hasan Ibrahim, Hamidah Ungku Chulan, Ungku Azmi Iskandar Semantics representation in a sentence with concept relational model (CRM) |
| title | Semantics representation in a sentence with concept relational model (CRM) |
| title_full | Semantics representation in a sentence with concept relational model (CRM) |
| title_fullStr | Semantics representation in a sentence with concept relational model (CRM) |
| title_full_unstemmed | Semantics representation in a sentence with concept relational model (CRM) |
| title_short | Semantics representation in a sentence with concept relational model (CRM) |
| title_sort | semantics representation in a sentence with concept relational model (crm) |
| url | http://psasir.upm.edu.my/id/eprint/59727/ http://psasir.upm.edu.my/id/eprint/59727/1/112-116-CR80.pdf |