An ontology-based intelligent data query system in manufacturing networks

Abstract This paper investigates the development of an intelligent data query framework through the use of semantic web technologies for manufacturing purposes. The primary objectives of the ontology-based data query were to develop an efficient and scalable data interoperability and retrieval system; in order to find the most relevant query results with minimum message cost, most hits per query and least response time. This document explains the idea of ontology and the application of the same in the manufacturing domain. A computer simulation software was developed based on a real case study of distributed networks of manufacturing workshops. In this research, a semantic query algorithm was developed where query results are returned by investigating the semantic richness of each workshop. Results were compared with a semantic-free search mechanism based on key performance indicators. The results show the validity of the proposed model for efficient data query when compared to random search.

[1]  Lanfen Lin,et al.  Developing manufacturing ontologies for knowledge reuse in distributed manufacturing environment , 2011 .

[2]  Robert I. M. Young,et al.  Towards a formal manufacturing reference ontology , 2013 .

[3]  Miltiadis D. Lytras,et al.  Metadata and Semantics , 2008 .

[4]  Shamkant B. Navathe,et al.  OSQR: A framework for ontology-based semantic query routing in unstructured P2P networks , 2012, 2012 19th International Conference on High Performance Computing.

[5]  Chantal Reynaud,et al.  Alignment-Based Partitioning of Large-Scale Ontologies , 2010 .

[6]  Dragan Gasevic,et al.  Model Driven Engineering and Ontology Development , 2009 .

[7]  S. Kotha Mass Customization: The New Frontier in Business Competition , 1992 .

[8]  Gregory Zacharewicz,et al.  An ontology-driven framework towards building enterprise semantic information layer , 2013, Adv. Eng. Informatics.

[9]  A. Siadat,et al.  MASON: A Proposal For An Ontology Of Manufacturing Domain , 2006, IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS'06).

[10]  Farid Cerbah Learning Ontologies with Deep Class Hierarchies by Mining the Content of Relational Databases , 2009, EGC.

[11]  Khedija Arour,et al.  LRS: A Novel Learning Routing Scheme for Query Routing on Unstructured P2P Systems , 2013, Trans. Large Scale Data Knowl. Centered Syst..

[12]  Khedija Arour,et al.  Learning model for efficient query routing in P2P information retrieval systems , 2015, Peer-to-Peer Netw. Appl..

[13]  Thomas H. Davenport,et al.  Analytics at Work: Smarter Decisions, Better Results , 2010 .

[14]  King Lun Choy,et al.  Application of intelligent data management in resource allocation for effective operation of manufacturing systems , 2014 .

[15]  Lida Xu,et al.  Internet of Things for Enterprise Systems of Modern Manufacturing , 2014, IEEE Transactions on Industrial Informatics.

[16]  Stefan Decker,et al.  Creating Semantic Web Contents with Protégé-2000 , 2001, IEEE Intell. Syst..

[17]  Stefano Borgo,et al.  What are features? An ontology-based review of the literature , 2016, Comput. Aided Des..

[18]  Steffen Staab,et al.  What Is an Ontology? , 2009, Handbook on Ontologies.

[19]  Peter Denno,et al.  An analysis and approach to using existing ontological systems for applications in manufacturing , 2000, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[20]  Sun Wei,et al.  An Ontology-Based Manufacturing Design System , 2009 .

[21]  Sandeep Saxena,et al.  Survey of Various Search Mechanisms in Unstructured Peer-to-Peer Networks , 2013 .

[22]  Henrik Eriksson,et al.  The evolution of Protégé: an environment for knowledge-based systems development , 2003, Int. J. Hum. Comput. Stud..

[23]  Birgit Vogel-Heuser,et al.  From Selling Products to Providing User Oriented Product-Service Systems - Exploring Service Orientation in the German Machine and Plant Manufacturing Industry , 2015, PLM.

[24]  Jérôme Euzenat,et al.  Semantic Precision and Recall for Ontology Alignment Evaluation , 2007, IJCAI.

[25]  M. Anusha,et al.  Big Data-Survey , 2016 .

[26]  Mitchell M. Tseng,et al.  Design for mass customization , 1996 .

[27]  Thierry Pun,et al.  Performance evaluation in content-based image retrieval: overview and proposals , 2001, Pattern Recognit. Lett..

[28]  Khedija Arour,et al.  A QUERY LEARNING ROUTING APPROACH BASED ON SEMANTIC CLUSTERS , 2011 .

[29]  Jayashankar M. Swaminathan,et al.  MASS CUSTOMIZATION , 2010 .

[30]  Amar Gupta,et al.  Creating Knowledge for Business Decision Making , 2011, Encyclopedia of Knowledge Management.

[31]  A. Gunasekaran,et al.  Sustainable supply management: An empirical study , 2012, ECIS 2012.

[32]  Archana Shirke,et al.  Generating OWL ontologies from a relational databases for the semantic web , 2009, ICAC3 '09.

[33]  Mozafar Saadat,et al.  Holonic Ontology and Interaction Protocol for Manufacturing Network Organization , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[34]  Christian Esposito,et al.  A knowledge-based platform for Big Data analytics based on publish/subscribe services and stream processing , 2015, Knowl. Based Syst..

[35]  Jenny A. Harding,et al.  A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration , 2007, Comput. Ind..

[36]  Irina Astrova Rules for Mapping SQL Relational Databases to OWL Ontologies , 2007, MTSR.

[37]  Vijay V. Raghavan,et al.  A critical investigation of recall and precision as measures of retrieval system performance , 1989, TOIS.

[38]  Timos K. Sellis,et al.  Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art , 2016, EDBT/ICDT Workshops.

[39]  Mozafar Saadat,et al.  Ontological Extension of PROSA for Manufacturing Network Formation , 2013, HoloMAS.

[40]  K. Zhang,et al.  ManuHub: A Semantic Web System for Ontology-Based Service Management in Distributed Manufacturing Environments , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.