ONTOLOGY FOR PERFORMANCE MEASUREMENT INDICATORS’ COMPARISON

Indicators are widely used by organizations as a way of evaluating, measuring and classifying organizational performance. As part of performance evaluation systems, indicators are often shared or compared across internal sectors or with other organizations. However, indicators can be vague and imprecise, and also can lack semantics, making comparisons with other indicators difficult. Thus, this paper presents a knowledge model based on an ontology that may be used to represent indicators semantically and generically, dealing with the imprecision and vagueness, and thus facilitating better comparison. Semantic technologies are shown to be suitable for this solution, so that it could be able to represent complex data involved in indicators comparison.

[1]  D. Otley.  Business performance measurement Theory and practice , 2003 .

[2]  Fernando Bobillo,et al.  A semantic fuzzy expert system for a fuzzy balanced scorecard , 2009, Expert Syst. Appl..

[3]  John K. Debenham.  Knowledge Engineering , 1998, Encyclopedia of Social Network Analysis and Mining.

[4]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[5]  Geert Wets,et al.  Modeling qualitative data in data envelopment analysis for composite indicators , 2011, Int. J. Syst. Assur. Eng. Manag..

[6]  Heng Li,et al.  Practical Framework for Measuring Performance of International Construction Firms , 2013 .

[7]  B. A. Gobin.  Reusing OWL-S to model knowledge intensive tasks performed by Knowledge Based Systems , 2012, 2011 Ninth International Conference on ICT and Knowledge Engineering.

[8]  Hacene Belhadef,et al.  CHARACTER RECOGNITION APPROACH BASED ON ONTOLOGY , 2011 .

[9]  W. N. Borst,et al.  Construction of Engineering Ontologies for Knowledge Sharing and Reuse , 1997 .

[10]  Adelina Tang.  TOWARDS THE USE OF A COMPUTING ONTOLOGY:A CURRICULA MANAGEMENT SYSTEM PROPOSAL , 2011 .

[11]  R. Kaplan,et al.  The balanced scorecard--measures that drive performance. , 2015, Harvard business review.

[12]  Tharam S. Dillon,et al.  Semantic Web Support for Open-Source Software Development , 2008, 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems.

[13]  Rade Stanković,et al.  Role and importance of key performance indicators measurement , 2011 .

[14]  Fernando Bobillo,et al.  Tractability of the Crisp Representations of Tractable Fuzzy Description Logics , 2010, URSW.

[15]  Alfredo Nazareno Pereira Boente,et al.  METODOLOGIA DE AVALIAÇÃO DE DESEMPENHO BASEADA EM LÓGICA FUZZY: AVALIAÇÃO DE DESEMPENHO DE UMA INSTITUIÇÃO ESTADUAL DE ENSINO SUPERIOR EM DUQUE DE CAXIAS , 2016 .

[16]  George Chryssolouris,et al.  Defining Manufacturing Performance Indicators Using Semantic Ontology Representation , 2012 .

[17]  Andy Neely,et al.  Performance measurement system design , 1995 .

[18]  John Debenham,et al.  Knowledge Engineering , 1998, Artificial Intelligence.

[19]  Nicola Guarino,et al.  Formal Ontology and Information Systems , 1998 .

[20]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[21]  A. Abramovich.  Human experience representation via upper level ontologies , 2005, International Conference on Integration of Knowledge Intensive Multi-Agent Systems, 2005..

[22]  Asunción Gómez-Pérez,et al.  Ontological Engineering: A state of the Art , 1999 .

[23]  Madjid Tavana,et al.  A fuzzy-QFD approach to balanced scorecard using an analytic network process , 2013, Int. J. Inf. Decis. Sci..