Using Ontologies for Defining Tasks, Problem-Solving Methods and their Mappings

In recent years two main technologies for knowledge sharing and reuse have emerged: ontologies and problem solving methods (PSMs). Ontologies specify reusable conceptualizations which can be shared by multiple reasoning components communicating during a problem solving process. PSMs describe in a domain-independent way the generic reasoning steps and knowledge types needed to perform a task. Typically PSMs are specified in a task-specific fashion, using modelling frameworks which describe their control and inference structures as well as their knowledge requirements and competence. In this paper we discuss a novel approach to PSM specification, which is based on the use of formal ontologies. In particular our specifications abstract from control, data flow and other dynamic aspects of PSMs to focus on the logical theory associated with a PSM (method ontology). This approach concentrates on the competence and knowledge requirements of a PSM, rather than internal control details, thus enabling black-box-style reuse. In the paper we also look at the nature of PSM specifications and we show that these can be characterised in a task-independent style as generic search strategies. The resulting ‘modelling gap’ between method-independent task specifications and task-independent method ontologies can be bridged by constructing the relevant adapter ontology, which reformulates the method ontology in task-specific terms. An important aspect of the ontology-centred approach described here is that, in contrast with other characterisations of task-independent PSMs, it does away with the simple, binary distinction between weak and strong methods. We argue that any method can be defined in either task-independent or task-dependent style and therefore such distinction is of limited utility in PSM reuse. The differences between PSMs which affect reuse concern the ontological commitments which they make with respect to domain knowledge and goal specifications.

[1]  Chantal Reynaud,et al.  Using explicit ontologies to create problem solving methods , 1997, Int. J. Hum. Comput. Stud..

[2]  Jan Top,et al.  Tasks and ontologies in engineering modelling , 1994, Int. J. Hum. Comput. Stud..

[3]  Douglas B. Lenat,et al.  On the thresholds of knowledge , 1987, Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications.

[4]  Dieter Fensel,et al.  Specifying and Verifying Knowledge-Based Systems with KIV , 1997, EUROVAV.

[5]  Dieter Fensel,et al.  Domain and Task Modeling in MIKE , 1996 .

[6]  B. Chandrasekaran,et al.  Generic Tasks for Knowledge-Based Reasoning: The "Right" Level of Abstraction for Knowledge Acquisition , 1987, Int. J. Man Mach. Stud..

[7]  John P. McDermott,et al.  SALT: A Knowledge Acquisition Language for Propose-and-Revise Systems , 1993, Artif. Intell..

[8]  Yuval Shahar,et al.  Task Modeling with Reusable Problem-Solving Methods , 1995, Artif. Intell..

[9]  Dieter Fensel,et al.  Specifying Knowledge-Based Systems with Reusable Components , 1997 .

[10]  A. T. Schreiber,et al.  A formal analysis of parametric design problem solving , 1995 .

[11]  John McDermott,et al.  Usable and reusable programming constructs , 1991 .

[12]  Richard Fikes,et al.  The Ontolingua Server: a tool for collaborative ontology construction , 1997, Int. J. Hum. Comput. Stud..

[13]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[14]  Nigel Shadbolt,et al.  KA process support through generalised directive models , 1993 .

[15]  V. R. Benjamins,et al.  Problem-Solving Methods for Diagnosis and their Role in Knowledge Acquisition , 1996 .

[16]  A.C.M. ten Teije-Koppen Automated configuration of problem solving methods in diagnosis , 1997 .

[17]  B. Chandrasekaran,et al.  Design Problem Solving: A Task Analysis , 1990, AI Mag..

[18]  Brian R. Gaines,et al.  Knowledge acquisition for knowledge-based systems , 1991, IEEE Expert.

[19]  Frank Puppe,et al.  Systematic introduction to expert systems - knowledge representations and problem-solving methods , 2011 .

[20]  Samson W. Tu,et al.  Mapping domains to methods in support of reuse , 1994, Int. J. Hum. Comput. Stud..

[21]  Richard Benjamins,et al.  Remedying the Reusability-Usability Tradeoff for Problem-Solving Methods , 1996 .

[22]  Todd R. Johnson,et al.  Task-structure analysis for knowledge modeling , 1992, CACM.

[23]  Walter Van de Velde Inference Structure as a Basis for Problem Solving , 1988, ECAI.

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

[25]  Luc Steels,et al.  Components of Expertise , 1990, AI Mag..

[26]  John P. McDermott,et al.  VT: An Expert Elevator Designer That Uses Knowledge-Based Backtracking , 1988, AI Mag..

[27]  Michael Kifer,et al.  Logical foundations of object-oriented and frame-based languages , 1995, JACM.

[28]  John McDermott,et al.  Preliminary steps toward a taxonomy of problem-solving methods , 1993 .

[29]  Dieter Fensel,et al.  Formal methods in knowledge engineering , 1995, The Knowledge Engineering Review.

[30]  Dieter Fensel The Tower-of-Adapter Method for Developing and Reusing Problem-Solving Methods , 1997, EKAW.

[31]  Bob J. Wielinga,et al.  Using explicit ontologies in KBS development , 1997, Int. J. Hum. Comput. Stud..

[32]  Dieter Fensel An Ontology-based Broker: Making Problem-Solving Method Reuse Work , 1998 .

[33]  Bob J. Wielinga,et al.  Steps in Constructing Problem Solving Methods , 1993, EKAW.

[34]  Bob J. Wielinga,et al.  CommonKADS: a comprehensive methodology for KBS development , 1994, IEEE Expert.

[35]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[36]  Gregg R. Yost,et al.  Configuring elevator systems , 1996, Int. J. Hum. Comput. Stud..