Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-09T09:04:29.078Z Has data issue: false hasContentIssue false
  • English
  • Français

Integration of Machine Learning and Knowledge Acquisition

Published online by Cambridge University Press:  07 July 2009

Claire Nédellec
Claire Nédellec
Affiliation:
Laboratorie de Recherche Informatique, Groupe Inférence et Apprentissage, Université Paris Sud, bût 490, F-91405 Orsay, France
Get access Rights & Permissions [Opens in a new window]

Extract

“Integration of Machine Learning and Knowledge Acquisition” may be a surprising title for an ECAI-94 workshop, since most machine learning (ML) systems are intended for knowledge acquisition (KA). So what seems problematic about integrating ML and KA? The answer lies in the difference between the approaches developed by what is referred to as ML and KA research. Apart from sonic major exceptions, such as learning apprentice tools (Mitchell et al., 1989), or libraries like the Machine Learning Toolbox (MLT Consortium, 1993), most ML algorithms have been described without any characterization in terms of real application needs, in terms of what they could be effectively useful for. Although ML methods have been applied to “real world” problems few general and reusable conclusions have been drawn from these knowledge acquisition experiments. As ML techniques become more and more sophisticated and able to produce various forms of knowledge, the number of possible applications grows. ML methods tend then to be more precisely specified in terms of the domain knowledge initially required, the control knowledge to be set and the nature of the system output (MLT Consortium, 1993; Kodratoff et al., 1994).

Type
Research Article
Information
The Knowledge Engineering Review , Volume 10 , Issue 1 , March 1995 , pp. 77 - 81
Copyright
Copyright © Cambridge University Press 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adé, H, De, Raedt L and Bruynooghe, M, 1995. “Declarative bias for specific-to-general ILP systems” Machine Learning (to appear).CrossRefGoogle Scholar
Anjewierden, A, Shadbolt, N and Wielinga, BJ, 1992. “Supporting knowledge acquisition: The acknowledge project” In: Enhancing the Knowledge Engineering Process—Contributions from ESPRIT, pp 143–172, Elsevier.Google Scholar
Bisson, G, 1994. “From inductive learning to knowledge acquisition through explanations” In: Proc. EJAI-94 Workshop on Inegration of ML and KA, pp 1–11.Google Scholar
Börner, K and Janetzko, D, 1994. “Case-based learning for knowledge-based design support” In: Proc. ECAI-94 Workshop on Integration of ML and KA, pp 12–18.Google Scholar
Chandrasekaran, B, 1989. “Task-structures, knowledge acquisition and learning” Machine Learning 4 251–346. Kluwer Academic Publishers.Google Scholar
Consortium MLT, 1993. “Final Public Report.” Esprit II Project 2154.Google Scholar
Dompseler, HJH and Van, Someren M, 1994. “Using models of problem solving as bias in automated knowledge acquisition.” In: Cohn, AG (ed.), Proc. EcAI-94, pp 499–503.Google Scholar
Feldman, R, 1994. “FRST– an interactive revision system for forward chaining rule bases” In: Proc. ECA 1–94 Workshop on Integration of ML and KA, pp 19–28.Google Scholar
Feldman, R and Nédellec, C, 1994. “A framework for specifying explicit bias for revision of approximate knowledge bases.” In: Gaines, B and Musen, M (eds.), Proc. Knowledge Acquisition Workshop (KAW-94), pp 15.1–15.19.Google Scholar
Fensel, D, Ganascia, JG, Nédellec, C, Plaza, E, Rouveirol, C, Van, de Velde W and Van, Someren M (eds.), 1995. Knowledge Level Modelling and Machine Learning, Heraklion, Greece.Google Scholar
Ganascia, JG, Thomas, J and Laublet, P, 1993. “Integrating models of knowledge and machine learning.” In: Brazdil, PB (ed). Proc. ECML 93, pp 396–401, Vienna.Google Scholar
Grosof, B and Russell, S, 1990. “Shift of bias as non-monotonic reasoning” In: Brazdil, PB and Konolige, K (eds.), Machine Learning, Meta-Reasoning and Logics. Kluwer Academic.Google Scholar
Kodratoff, Y, Moustakis, V and Graner, G, 1994. “Can machine learning solve my problem?” Applied Artificial Intelligence Journal 8 (1) 1–31. Taylor & Francis Publishers.CrossRefGoogle Scholar
Mladenic, D, Bratko, I, Paul, RJ and Grobelnik, M, 1994. “Knowledge acquisition for discrete event systems using machine learning” In: Proc. ECA1–94 Workshop on integration of ML and KA, pp 29–36.Google Scholar
Mitchell, TM, 1991. “The need for biases in learning generalizations” In: Shavlik, JW and Dietterich, TG (eds.), Readings in Machine Learning, pp 184–191. Morgan Kaufmann.Google Scholar
Mitchell, TM, Mahadevan, S and Steinberg, LI, 1989. LEAP: A learning apprentice for VLSI design. In: Machine Learning III: An Artificial Intelligence Approach, pp 271–289, Morgan Kaufmann.Google Scholar
Nédellec, C and Causse, K, 1992. “Knowledge refinement using knowledge acquisition and machine learning methods.” In: Wetter, T et al. (eds.), Proc. 6th European Knowledge Acquisition Workshop (EKAW-92), pp.171–190, Springer-Verlag.Google Scholar
Nédellec, C, Ferreira, JL, Correia, J and Costa, E, 1995. “Machine learning goes to the bank.” Applied Artificial Intelligence Journal (Special Issue on Machine Learning), Kodratoff, Y. (ed). Taylor & Francis Publishers.Google Scholar
Nédellec, C and Rouveirol, C, 1994. “Specification of the HAIKU system.” Report 928, University of Paris- South.Google Scholar
Park, YT and Sung-Hee, K, 1994. “Knowledge base refinement by failure driven approach.” In: Proc. ECAI-94 Workshop on integration of ML and KA, pp 37–43.Google Scholar
Rouveirol, C and Albert, P, 1994. “Knowledge level model of a configurable learning system.” In: Steels, L. et al. (eds.), Proc. 8th European Knowledge Acquisition Workshop (EKAW-94), pp 374–393, Springer- Verlag.Google Scholar
Tausend, B, 1994. “Modelling inductive learning for knowledge acquisition tasks.” In: Proc. ECAI-94 Workshop on integration of ML and KA, pp 45–49.Google Scholar
Tecuci, G, Kedar, S and Kodratoff, Y, 1993. Proc. IJCAI-93 Workshop on Machine Learning and Knowledge Acquisition: Common issues, Contrasting Methods, and Integrated Approaches.Google Scholar
Thomas, J, 1994. “Learning within a problem solving method: An empirical evaluation.” In: Proc. ECA1–94 Workshop on Integration of ML and KA, pp 51–57.Google Scholar
Utgoff, PE, 1986. “Shift of bias for inductive concept-learning” In: Michalski, RS, Carbonell, JG, and Mitchell, TM (eds.), Machine Learning: An artificial intelligence approach, pp 107–148, Morgan Kaufmann.Google Scholar
Van, de Velde W and Aamodt, A, 1992. “Machine learning issues in CommonKADS, ESPRIT Project KADS-II”.Google Scholar
Van, de Velde W, Fensel, D, Plaza, E and Van, Someren M (eds.), 1994. Learning and Knowledge Level, MLnet Workshop, Catania, Italy.Google Scholar