Open AccessOntology Reengineering: A Case Study from the Automotive Industry
Author(s) -
Rychtyckyj Nestor,
Raman Venkatesh,
Sankaranarayanan Baskaran,
Kumar P. Sreenivasa,
Khemani Deepak
Publication year - 2017
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v38i1.2712
Subject(s) - ontology , computer science , semantic web , automotive industry , rdf , usable , owl s , software engineering , process (computing) , scope (computer science) , world wide web , engineering , semantic web stack , philosophy , epistemology , programming language , aerospace engineering , operating system
For more than 25 years Ford Motor Company has been utilizing an AI‐based system to manage process planning for vehicle assembly at its assembly plants around the world. The scope of the AI system, known originally as the Direct Labor Management System and now as the Global Study Process Allocation System (GSPAS), has increased over the years to include additional functionality on ergonomics and powertrain assembly (engines and transmission plants). The knowledge about Ford's manufacturing processes is contained in an ontology originally developed using the KL‐ONE representation language and methodology. To preserve the viability of the GSPAS ontology and to make it easily usable for other applications within Ford, we needed to reengineer and convert the KL‐ONE ontology into a semantic web OWL/RDF format. In this article, we will discuss the process by which we reengineered the existing GSPAS KL‐ONE ontology and deployed semantic web technology in our application.