SciELO - Scientific Electronic Library Online

 
vol.8 issue4Document Indexing with a Concept Hierarchy author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.8 n.4 México Apr./Jun. 2005

 

Artículos

 

CRIB: A Method for Integrity Constraint Checking on Knowledge Bases

 

CRIB: Método para la Comprobación de Restricciones de Integridad en Bases de Conocimiento

 

Julia Clemente1, Angélica de Antonio2 and Jaime Ramírez2

 

1 Universidad de Alcalá de Henares
Escuela Universitaria Politécnica
Departamento de Automática
Campus Universitario. Ctra. Madrid–Barcelona, Km. 33,600
28871 Alcalá de Henares, Madrid, Spain
e–mail: julia@aut.alcala.es

2 Facultad de Informática.
Universidad Politécnica de Madrid.
28660 Boadilla del Monte, Madrid, Spain.
e–mail: angelica@fi.upm.es, jramirez@fi.upm.es

 

Article received on october 15, 2003; accepted 01 february 28,2005

 

Abstract

The necessity of verification tools for Knowledge Based–Systems (KBSs), that help to guarantee a certain degree of quality and reliability of these systems will increase in the future when more critical systems are developed in areas such as industry, science, business, etc. One of the objectives of the KBSs verification is to assure the consistency and completeness of the Knowledge Base (KB). In this paper, a technique to detect possible inconsistencies or conflicting situations between the objects of the KB is described, and a tool called CRIB, that implements this technique, is presented. The generality of this technique, based on the checking of the Integrity Constraints (ICs) declared on the KB, will allow to apply it to different kinds of KBSs. In addition, the flexibility and the power of the ICs specification language will make it possible to detect a wide range of inconsistencies in a KB.

Keywords: Verification, Knowledge Based–System, Consistency.

 

Resumen

La necesidad de herramientas de verificación para Sistemas Basados en el Conocimiento (SBCs), que ayuden a garantizar un cierto grado de calidad y fiabilidad de estos sistemas aumentará en el futuro conforme más sistemas críticos sean desarrollados en áreas tales como la industria, la ciencia, los negocios, etc. Uno de los objetivos de la verificación de SBCs es asegurar la consistencia y la completitud de la Base de Conocimientos (BC). En este artículo se describe una técnica para detectar posibles inconsistencias o situaciones conflictivas entre los objetos de la BC, y se presenta una herramienta llamada CRIB, que implementa esta técnica. La generalidad de esta técnica, basada en la comprobación de las Restricciones de Integridad (Rls) declaradas en la BC, permite aplicarla a diferentes tipos de SBCs. Asimismo, la flexibilidad y la potencia del lenguaje de especificación de RIs hará posible detectar un amplio abanico de inconsistencias en una BC.

Palabras Clave: Verificación, Sistema Basado en el Conocimiento, Consistencia.

 

DESCARGAR ARTICULO EN FORMATO PDF

 

References

1. Plaza, E.: KBS Validation: From Tools to Methodology. IEEE Expert, vol. 8, No. 3. 1993, pp. 45–47.        [ Links ]

2. Hoppe, T., and Meseguer, P.: VVT Terminology: A proposal. IEEE Expert, Vol. 8, No. 3, 1993, pp. 48–55.        [ Links ]

3. Laurent, J.: Proposals for a valid terminology in KBS Validation. Proceedings ECAI 92, 1992, pp. 829–834.        [ Links ]

4. Adrion, W.: Validation, verification, and testing of computer software. ACM Computer Surveys, vol. 14, No. 2, 1982, pp. 159–192.        [ Links ]

5. ANSI/IEEE Standard 729, Standard Glossary of Software Engineering Terminology: 1983.        [ Links ]

6. Suen Ch. Y., Grogono P.D., and Shinghal. R.: Verifying, Validating, and Measuring the performance of Expert Systems. Expert Systems With Applications, Vol. 1, No. 2, 1990, pp. 93–102.        [ Links ]

7. Green, C., and Keyes, M.: Verification and Validation of expert systems. Proceedings Western Conference on Expert Systems, 1987, pp. 38–43.        [ Links ]

8. Rousset, M.: On the Consistency of Knowledge Bases: The COVADIS System. Proceedings ECAI 88, 1988, pp. 79–84.        [ Links ]

9. Ginsberg, A.: Knowledge–Base Reduction: A New Approach to checking Knowledge Bases for Inconsistency and Redundancy. Proceedings AAAI–88, 1988, pp. 585–589.        [ Links ]

10. Beauvieux, A., and Dague, P.: A General Consistency (Checking and Restoring) Engine for Knowledge Bases. Proceedings ECAI 90, 1990, pp. 77–82.        [ Links ]

11. Loiseau, S.: Refinement of Knowledge Bases Based on Consisten y Proceedings ECAI 92, 1992, pp. 845–849.        [ Links ]

12. Meseguer, P.: lncremental Verification of Rule–Based Expert Systems. Proceedings ECAI 92, 1992, pp. 840–844.        [ Links ]

13. Van Melle, W., Shortliffe, H., and Buchanan: G. , EMYCIN: A Knowledge Engineer's Tool for Constructing Rule–Based Systems. Rule–Based Expert Systems, Addison–Wesley. 1984, pp. 301–313.        [ Links ]

14. Suwa, M., Scott, A.C., and Shortliffe, E.H.: An approach to verifying completeness and consistency in a rule based expert system. Al Magazine, fall 82, 1982, pp. 16–21.        [ Links ]

15. Steinmetz R. and Theissen S.: Integration of Petri Nets into a Tool for Consistency Checking of Expert Systems with Rule–Based Knowledge Representation. Proceedings 6th. Workshop on Petri nets, 1985, pp. 35–52.        [ Links ]

16. Murata T., and Matsuyama K.: Inconsistency Check of a Set of Clauses Using Petri Net Reductions: EECS #86–12 Dept. Universityof Illinois at Chicago, Technical Report 1986.        [ Links ]

17. Meseguer, P.: A New Method to checking Rule Bases for Inconsistency: A Petri Net Approach. Proceedings ECAI 90,1990, pp. 437–442.        [ Links ]

18. He X., Chu W. C., Yang H., and Yang S. J. H.: A New Approach to Verify Rule–Based Systems using Petri Nets. Proceedings of the Twenty–Third Annual Intemational Computer Software and Applications Conference (Cat. No.99CB37032). IEEE Comput. Soc, Los Alamitos, CA, USA; xx+478: 1999, pp. 462–467.        [ Links ]

19. Nguyen, T.A, Perkins, W.A., and Pecora, D.: Knowledge Base Verification. Al Magazine, vol. 8, No. 2, 1987, pp. 69–75.        [ Links ]

20. Nguyen T.A.: Verifying Consistency of Production Systems. Proceedings of the 3rd. conference on Artificial Intelligence Aplications, 1987, pp. 4–8.        [ Links ]

21. Nazareth D.L.: An Analysis of Techniques for Verification of Lodical Correctness in Rule Based Systems, Ph D. diss., Departament of Managerial Studies, Case Westem Reserve University. Cleveland, Ohio, 1988.        [ Links ]

22. Nazareth D.L., Kennedy M.H.: Static and dynamic verification, Validation and testing: The Evolution of a Discipline. Procedings of the AAA'90 on Knowledge–Based Systems Validation, Verification and Testing, Boston, 1990.        [ Links ]

23. Barr V.B.: Incorporating Uncertainty in a DAG–Based Approach to Static and Dynamic Verification of Rule–Based Systems. Proceedings of the AAAI'93 Workshop on Knowledge–Based Systems Validation, Verification and testing, 1993, pp. 129–130.        [ Links ]

24. Ramaswamy M., Sarkar S., and Chen Ye Sho: Using directed hypergraphs to verify rule–based expert systems. IEEE Transactions on Knowledgeand Data Engineering, vol. 9, No. 2, 1997, pp. 221–237.        [ Links ]

25. Gursaran G. S., Kanungo S., and Sinha A. K.: Rule–base content verification using a digraph–based modelling approach. Artificial Intelligence in Engineering, vol 13, No. 3, pp. 321–336.        [ Links ]

26. de Antonio, A: Sistema para la verificación estructural y detección de inconsistencias en Bases de Conocimientos, Trabajo Fin de Carrera, FIM, UPM, 1990.        [ Links ]

27. Dahl M., and Williamson K.: A verification Strategy for Long–term Maintenance of Large Rule–Based Systems. Workshop Notes of the AAAI'92 WorkShop on Verification and Validation of expert Systems, 1992.        [ Links ]

28. Ayel M.: Protocols for Consistency Checking in Expert System Knowledge Bases. Proceedings of the th. European Conference on Artificial Intelligence (ECAI'88) 1988, pp. 220–225.        [ Links ]

29. Ayel M., and Laurent J. P.: SACCO–SYCOJET: Two Different Ways of Verifying Knowledged–Based Systems. Validation, Verification and Test of Knowledge–Based Systems: John Wiley Publishers, 1991, pp. 63–76.        [ Links ]

30. de Kleer J.: An Assumption Based TMS. Artificial Intelligence vol. 28, No. 2, 1986, pp. 127–162.        [ Links ]

31. de Antonio, A: Una interpretación Algebraica de la Verificación de Sistemas basados en el Conocimiento, Ph.D. diss, Facultad de Informática, Universidad Politécnica de Madrid, 1994.        [ Links ]

32. Laita L. M., Roanes–Lozano E., Roanes–Macías E., and Díaz A.: From Computer Algebra to Al. Application to Verification and Automated Proving in Multi Valued Logics Knowledge Systems. Proceedings of the SEKE–97, 1997, pp. 295–301.        [ Links ]

33. Laita L. M., Roanes Lozano E., de Ledesma L, and Maojo V.: Computer Algebra based Verification and knowledge extraction in RBS application to Medical Fitness criteria. Proceedings of the EUROVAD'99, 1999.        [ Links ]

34. Antoniou, G.: Verification and Correctness Issues for Nonmonotonic Knowledge Bases. International Journal of Intelligent Systems,Vol. 12, No. 10, 1997, pp. 725–738.        [ Links ]

35. Wu C. H., and Lee S. J.: Knowledge Verification with an Enhanced High–Level Petri–Net Model. IEEE Expert, Sep/Oct, 1997, pp. 73–80.        [ Links ]

36. O'Leary D. E.: Verification of uncertain knowledge based systems: an empirical verification approach. Management Science. Vol. 42, No. 12, 1996, pp. 1663–1675.        [ Links ]

37. Lee S., and O'Keefe R. M.: Subsumption anomalies in hybrid knowledge based systems. Intemational Joumal of Expert Systems, Vol. 6, No. 3, 1993, pp. 299–320.        [ Links ]

38. Mukherjee R., Gamble R. F., and Parkinson J. A.: Classifying and detecting anomalies in hybrid knowledge–based systems. Decision–Support–Systems, Vol. 21, No. 4, 1997, pp. 231–251.        [ Links ]

39. Cardeñosa, J., and Juristo, N.: General Overview of the Valid Project. Proceedings European Symposium on the Validation and Verification of Knowledge Based Systems, EUROVAV'93, 1993, pp. 53–67.        [ Links ]

40. VALID team, Deliverable D4: VETA definition, in ESPRIT II 2148 VALID project, 1989.        [ Links ]

41. VALID team, Deliverable D10: Report on Valid Environment. ESPRIT II 2148 VALID project, 1990.        [ Links ]

42. Martínez, L: CCR2: Un Modelo Genérico de Representación del Conocimiento. Trabajo Fin de Carrera, FIM, UPM, 1993.        [ Links ]

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License