Novel rules for extracting the entities of entity relationship models
Other Title(s)
قوانين جديدة لاستخراج كينونات مخطط الكينونة العلاقة
Joint Authors
Umar, Musa Ahmad Muhammad
al-Shaykhi, Abd al-Rahman Abd Allah Miftah
Fayiz, Balha Hasan Nasr
Source
Journal of Pure and Applied Sciences
Issue
Vol. 20, Issue 2 (30 Jun. 2021), pp.29-35, 7 p.
Publisher
Publication Date
2021-06-30
Country of Publication
Libya
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
Extracting entities from natural language text to design conceptual models of the entity relationships is not trivial and novice designers and students can find it especially difficult.
Researchers have suggested linguistic rules/guidelines for extracting entities from natural language text.
Unfortunately, while these guidelines are often correct they can, also, be invalid.
There is no rule that is true at all times.
This paper suggests novel rules based on the machine learning classifiers, the RIPPER, the PART and the decision trees.
Performance comparison was made between the linguistic and the machine learning rules.
The results shows that there was a dramatic improvement when machine learning rules were used.
American Psychological Association (APA)
Umar, Musa Ahmad Muhammad& al-Shaykhi, Abd al-Rahman Abd Allah Miftah& Fayiz, Balha Hasan Nasr. 2021. Novel rules for extracting the entities of entity relationship models. Journal of Pure and Applied Sciences،Vol. 20, no. 2, pp.29-35.
https://search.emarefa.net/detail/BIM-1419261
Modern Language Association (MLA)
Umar, Musa Ahmad Muhammad…[et al.]. Novel rules for extracting the entities of entity relationship models. Journal of Pure and Applied Sciences Vol. 20, no. 2 (Jun. 2021), pp.29-35.
https://search.emarefa.net/detail/BIM-1419261
American Medical Association (AMA)
Umar, Musa Ahmad Muhammad& al-Shaykhi, Abd al-Rahman Abd Allah Miftah& Fayiz, Balha Hasan Nasr. Novel rules for extracting the entities of entity relationship models. Journal of Pure and Applied Sciences. 2021. Vol. 20, no. 2, pp.29-35.
https://search.emarefa.net/detail/BIM-1419261
Data Type
Journal Articles
Language
English
Notes
Record ID
BIM-1419261