Open AccessAutomated Identification of Semantic Similarity between Concepts of Textual Business Rules
Author(s) -
Abdellatif Haj,
Youssef Balouki,
Taoufiq Gadi
Publication year - 2021
Publication title -
international journal of intelligent engineering and systems
Language(s) - English
Resource type - Journals
eISSN - 2185-310X
pISSN - 1882-708X
DOI - 10.22266/ijies2021.0228.15
Subject(s) - computer science , synonym (taxonomy) , semantic similarity , heuristics , identification (biology) , natural language processing , reusability , similarity (geometry) , artificial intelligence , semantics of business vocabulary and business rules , natural language , information retrieval , data mining , business rule , business process , programming language , software , botany , geochemistry , compatibility (geochemistry) , geology , image (mathematics) , biology , genus , operating system
Business Rules (BR) are usually written by different stakeholders, which makes them vulnerable to contain different designations for a same concept. Such problem can be the source of a not well orchestrated behaviors. Whereas identification of synonyms is manual or totally neglected in most approaches dealing with natural language Business Rules. In this paper, we present an automated approach to identify semantic similarity between terms in textual BR using Natural Language Processing and knowledge-based algorithm refined using heuristics. Our method is unique in that it also identifies abbreviations/expansions (as a special case of synonym) which is not possible using a dictionary. Then, results are saved in a standard format (SBVR) for reusability purposes. Our approach was applied on more than 160 BR statements divided on three cases with an accuracy between 69% and 87% which suggests it to be an indispensable enhancement for other methods dealing with textual BR.