Saturday , March 2 2024

Semantics and Knowledge-based System for
Occupational Health Safety

Alexandra GALATESCU1, Adriana ALEXANDRU1, Corneliu ZAHARIA2,

1 I C I Bucharest
(National Institute for R & D in Informatics)

8-10 Averescu Blvd.
011455 Bucharest 1, Romania,
2 Stefan Nicolau Institute of Virology
285 Mihai Bravu Avenue, 030304, Bucharest, Romania,

Abstract: The paper presents the conceptual framework, the basic inference and some implementation results in a system (under development) for the online training on the potential risks and events that can occur during the execution of certain activities. This system is an application of ontologies in the occupational health domain. The training (in a requested context and complying with domain-specific rules) results from the automatic and semantics-based discovery of the prevention documents, actions, methods etc that fit the user’s request.

Keywords: Occupational health; risk prevention; formal ontologies; ontology-based modeling; e-Training.

>>Full text
Alexandra GALATESCU, Adriana ALEXANDRU, Corneliu ZAHARIA, Andrei POPOVICI, Semantics and Knowledge-based System for Occupational Health Safety, Studies in Informatics and Control, ISSN 1220-1766, vol. 20 (2), pp. 107-120, 2011.

1. Introduction

According to the Joint ILO/WHO committee in Occupational Health (1950), the goal of the occupational health is the promotion and maintenance of the highest degree of physical, mental and social well-being of the workers in all domains, their protection from risks caused by their working conditions, negligence, lack of training etc [1]. Hence, the occupational risks are a special type of risks that appear in the work environments with a high probability of harming people or machines. Despite its importance in the risk management (RM) inside and across organizations, the occupational risk prevention is still a manual process, specific to domains like health, construction, transportation, industry, biology, etc. A proactive approach in RM relies on the risk early recognition and prevention.

Nowadays, the occupational risk prevention and management comply with the principles and methodology of the risk management process, a key process within both the private and public organizations [2].

The training in occupational risk prevention should advise the operator/ worker on the health, safety, security and environmental issues related to his work. He can ask for training before or during the execution of an activity or before the use of a certain machine.

The system described in this paper relies on the standard terminology proposed with ISO CD31000 [2], [3], combined with the terminology common to several upper-level ontologies and process models.

There are several risk-related standards published by ISO and other standards bodies, as well as many proposals and principles that refer to risk management. In 2005, ISO has initiated a working group to develop a guidance standard on RM, ISO CD31000. In conjunction with this standard, the group has updated the ISO/IEC Guide 73-Risk Management – Vocabulary [4], that gives a basic vocabulary and the definitions of the RM generic terms. It encourages a mutual and consistent understanding and a coherent approach to the description of the RM activities.

In Europe, the risk prevention is subject of two directives Seveso I and Seveso II [5] that establish the domain terminology, the obligations and normative documents regarding the large scale industrial hazards.

In practice, there are products for the risk control in industrial environments and domain-specific standards and software tools for RM in health, environment, insurance, finances, construction, transportation, etc. Risk prevention is automated for the security of computers, Web, networks. Ontologies are also used mainly for the security management (of assets, networks, information systems, databases, etc). Some examples are in [6]-[10]. There is no system based on knowledge and semantics for risk prevention and for training and dynamic discovery of prevention information, documents and actions. However, [11] proposes the risk evaluation and analysis along the life cycle of the construction projects, based on ontologies and on a conceptual model.

They rely on a simpler reference ontology and model and have a different inference goal. Also, [12] gives an example of an ontology in OWL (Web Ontology Language) [13] for occupational health. And, [14] confirms the idea that a model of occupational risks is needed to describe relevant data in the context of event occurring and this data can be transformed into knowledge navigated using an intelligent search engine (similarly to the goal of the system presented in this paper).

Section 2 describes the ontology-based model proposed for risk prevention. Section 3 presents the basic inference that will be implemented in the system.


  1. COPPÉE, G., Occupational Health Services and Practice, in J. Mager Stellman (Ed.), Encyclopaedia of Occupational Health and Safety, Geneva, PA: International Labour Office, 1998.
  2. METHODWARE, ISO 31000: Risk Management Standard, 2009, [visited, May 14, 2010].
  3. ISO, ISO 31000: 2009 Risk management – principles and implementation of risk management, [visited, May 10, 2010].
  4. ISO, ISO/ IEC Guide 73:2002 “Risk management – Vocabulary Guidelines for use in Standards”, 2009, [visited, May 2010].
  5. MAHB (Major Accident Hazards Bureau), Safety Management Systems – Seveso II, Official Publications of European Communities, Luxembourg, 1998, [visited, May, 2010].
  6. LEE, S.-W., R. GANDHI, D. MUTHURAJAN, D. YAVAGAL, G.-J. AHN, Building Problem Domain Ontology from Security Requirements in Regulatory Documents, Proc. Intl. WS on Software Engineering for Secure Systems. ACM Press, 2006.
  7. TSOUMAS, B., D. GRITZALISI, Towards an Ontology-based Security Management, Proc. Intl. Conf. on Advanced Information Networking and Applications (AINA), Vienna, 2006.
  8. KLEMEN, M., E. WEIPPL, A. EKELHART, S. FENZ, Security Ontology: Simulating Threats to Corporate Assets, Proc. 2nd Intl. Conf. on Information Systems Security (ICISS), Springer, 2006.
  9. SIMMONDS, A., P. SANDILANDS, L. VAN EKERT, An Ontology for Network Security Attacks, Proc. Asian Applied Computing Conference (AACC), LNCS, vol. 3285, Springer, 2004.
  10. MITRA P., C. PAN, P. LIU, V. ATLURI, Privacy-preserving Semantic Interoperation and Access Control of Heterogeneous Databases, Proc. Symposium on Information, Computer and Communications Security, ACM Press, 2006.
  11. FORCADA, N., M. CASALS, A. FUERTES, The Basis of a Decision Making Tool for Risks’ Evaluation Based on Ontologies, Proc. Intl. Conf. on Inf. and Knowledge Management – Helping the Practitioner in Planning and Building, Stuttgart, 2007, 2008031001259 [visited, April 2010].
  12. KOLA, J., B. WHEELDIN, A. RECTOR, Lessons in Building OWL Ontology driven applications: OCHWIZ – an Occupational Health Application, National e-Science Centre, 2007, [visited, April 12, 2010].
  13. W3C, OWL 2 Web Ontology Language Structural Specification and Functional-Style Syntax, Oct. 2009, [May, 2010].
  14. SWUSTE, P., Qualitative Methods for Occupational Risk Prevention Strategies in Safety or Control Banding safety, Safety Science Monitor, Issue 3, vol. 11, 2007.
  15. GANAYE, P., An Expression Evaluator written in VB.NET, evaluator.aspx.
  16. ABILOV, V., WYSIWYG Rule Editor: Create and Test Rules for any .NET Type,
  17. ALEXANDRU, A., F. FILIP, A. GĂLĂTESCU, E. JITARU, Using Ontologies in eHealth and Biomedicine, in book A. Shukla and R. Tiwari (Eds) “Intelligent Medical Technologies and Biomedical Engineering: Tools and Applications”, IGP Global, May, 2010.
  18. GĂLĂTESCU, A., A. ALEXANDRU, C. ZAHARIA, S. KOVACS, Ontology-based Modeling and Inference for Risk Prevention, Proc. Intl. Conf. on Advances in Semantic Processing, SEMAPRO, Florence, Italy, October, 2010.
  19. OBOUT. obout Suite for ASP.Net, [visited, November 17, 2010].