http://www.swi.psy.uva.nl/usr/wielinga/morepub.html W.Jansweijer G. van Heijst A.Schreiberl Guidelines on domain layer building from reusable domain ontologies (version 1) http://www.swi.psy.uva.nl/usr/wielinga/morepub.html W.Jansweijer A.Schreiberl The KACTUS View on the 'O' word. 7th Dutch National Conference on Artificial Intelligence NAIC'95, EURIDIS 159 168 IJCAI Workshop on Basic Ontological Issues in K http://www.swi.psy.uva.nl/usr/wielinga/morepub.html A.Schreiber J.Breuker KBS Development through Knowledge Modelling. Enhancing the Knowledge Engineering Process - Contributions from ESPRIT 1992 15 51 http://www.swi.psy.uva.nl/usr/wielinga/morepub.html W.van de Welde A.Schreiber H.Akkermans The CommonKads Framework for knowledge Modelling. Proceedings of the 7th AAAI Knowledge Acquisition for Knowledge-Based Systems Workshop 1992 31.1 31.29 http://www.swi.psy.uva.nl/usr/wielinga/morepub.html W.van de Welde A.Schreiber H.Akkermans The KADS Knowledge Modelling Approach. Proceedings of the 2nd Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop 1992 23 42 http://www.swi.psy.uva.nl/usr/wielinga/morepub.html W.Jansweijer J.J.Elshout 1990 On the multiplicity of learning to solve problems. Learning and Instruction 2.1:127 2.1:144 http://www.swi.psy.uva.nl/usr/wielinga/morepub.html W.Jansweijer J.J.Elshout F.de Jong A. Demetriou A.Efklides Y.F. Barnard G. Erkens R.H. Kluwe 1990 Problem solving. Learning and instruction: European research in an international context, Vol. 2.1: Social and cognitive aspects of learning and instruction 2.1:127 2.1:245 http://www.swi.psy.uva.nl/usr/wielinga/morepub.html W.Jansweijer J.J.Elshout 1987 The expertise of novice problemsolvers. Advances in artificial intelligence 2.121 2,130 http://www.swi.psy.uva.nl/usr/wielinga/morepub.html J.Breuker J.J.Elshout M.W. van Someren 1986 Hardopdenken en protocolanalyse [Thinking aloud and protocol analysis]. Tijdschrift voor onderwijsresearch 11.241 11,254 http://www.swi.psy.uva.nl/usr/wielinga/morepub.html W.Jansweijer J.J.Elshout 1986 The expertise of novice problem solvers. Proceedings of the 7th European Conference on Artificial Intelligence 576 577 77th European Conference on Artificial Intelligence http://www.swi.psy.uva.nl/usr/wielinga/morepub.html J.J.Elshout W.Jansweijer 1985 Het leren van de beginnende probleemoplosser. Zelfstandig leren: Bijdragen aan de Onderwijs Research Dagen 1.102 1,109 http://www.swi.psy.uva.nl/usr/wielinga/morepub.html J.J.Elshout W.Jansweijer 1985 Het leren van de beginnende probleemoplosser. Zelfstandig leren: Bijdragen aan de Onderwijs Research Dagen 1.102 1,109 H.G.L.C. Lodewijks P.R.J. Simons http://www.swi.psy.uva.nl/usr/wielinga/morepub.html J.J.Elshout 1981 Simulation of learning to solve problems. Nederlands Tijdschrift voor de Psychologie 39.371 39,383 http://www.swi.psy.uva.nl/usr/wielinga/morepub.html J.J.Elshout 1979 A computational approach to the study of human skill acquisition. 6th International Joint Conference on Artificial Intelligence. Bob Wielinga Department of Social Science Informatics (S.W.I), Faculty of Psychology, University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam, The Netherlands (+31) 20 525-6796 (+31) 20 525-6896 wielinga@swi.psy.uva.nl http://www.cis.ohio-state.edu/~chandra/Chandra.JPG Balakrishnan Chandrasekaran chandra@cis.ohio-state.edu (614) 292-0923 Laboratory for Artificial Intelligence Research, Department of Computer and Information Sience, 395 Dreese Lab, 2015 Neil Avenue, Columbus, OH 43210-1277 USA (614) 292-1424 (614) 292-2911 http://www.cis.ohio-state.edu/lair/Projects/ARPA/arpa.html http://www.cis.ohio-state.edu/lair/Projects/MADE/RaDEO-project.html http://turing.paccs.binghamton.edu/jetai/ http://www.cis.ohio-state.edu/~chandra/diag-reason.html http://www.swi.psy.uva.nl/usr/wielinga/morepub.html W.Jansweijer G. van Heijst A.Schreiberl Guidelines on domain layer building from reusable domain ontologies (version 1) http://www.cis.ohio-state.edu/~chandra/pubs.html http://www.cis.ohio-state.edu/~chandra/ Susan G. Josephson http://www.cis.ohio-state.edu/~chandra/separability.ps Separability Hypothesis, http://www.cis.ohio-state.edu/~chandra/ http://www.cis.ohio-state.edu/~chandra/intelligent-control.pdf Intelligent Control at the Knowledge Level http://www.cis.ohio-state.edu/~chandra/ J.R.Josephson http://www.cis.ohio-state.edu/~chandra/ontology.PDF The Ontology of Tasks and Methods http://www.cis.ohio-state.edu/~chandra/ J.R.Josephson http://www.cis.ohio-state.edu/~chandra/workshop-version.pdf Representing Function as Effect: Assigning Functions to Objects in Context and out http://www.cis.ohio-state.edu/~chandra/function-paper.ps An Explication of Function http://www.cis.ohio-state.edu/~chandra/ H. Kaindl http://www.cis.ohio-state.edu/~chandra/requirements-workshop-version.pdf Representing Functional Requirements and User-System Interactions http://www.cis.ohio-state.edu/~chandra/ http://www.aifb.uni-karlsruhe.de/~mer/Pages/spatial-cognition-report.PDF Spatial Representation and Reasoning, 1997 http://www.aifb.uni-karlsruhe.de/WBS/dfe/foto/dfe.1.gif http://www.aifb.uni-karlsruhe.de/WBS/dfe/foto/dfe.2.gif Dieter Fensel dfe@aifb.uni-karlsruhe.de http://www.aifb.uni-karlsruhe.de/aifb.engl.html Broker for Software Reuse Knowledge Management Ressource Description Framework (RDF) Intelligent Web Agents Intelligent Information Integration The Slogan Level The Knowledge Acquisition and Representation Language (KARL) Specification languages for Knowledge-Based Systems Validation and Verification of Knowledge-Based Systems Reusable Problem-Solving Methods for Knowledge-Based Systems Knowledge-Level Modelling and Machine Learning http://www.cis.ohio-state.edu/~chandra http://www.aifb.uni-karlsruhe.de/Staff/sde.html http://www.cs.vu.nl/~joeri http://www.aifb.uni-karlsruhe.de/Staff/mer.eng.html http://www.ida.liu.se/~her http://smi-web.stanford.edu/people/gennari/ http://www.dia.fi.upm.es/home_pages/asun.html http://www.cs.rug.nl/~rix/ http://kmi.open.ac.uk/~enrico/ http://www.smi.Stanford.edu/people/musen/index.html http://www.iiia.csic.es/People/enric.html http://www.lri.fr/people/pierret.html http://www.informatik.uni-ulm.de/abt/pm/fg121/reif-homepage.html http://www.cs.rug.nl/~grl http://www.lri.fr/people/mcr.html http://i11www.ira.uka.de/~schoeneg http://www.swi.psy.uva.nl/usr/guus/home.html http://www.swi.psy.uva.nl/usr/remco/home.html http://www.aifb.uni-karlsruhe.de/Staff/studer.engl.html http://www.cs.vu.nl/~annette http://www.smi.Stanford.edu/people/tu/index.html http://www.cs.vu.nl/~patveck http://www.cs.vu.nl/~frankh http://www.cs.vu.nl/~yde http://www.swi.psy.uva.nl/usr/wielinga/home.html http://www.aifb.uni-karlsruhe.de/Staff/mwi.engl.html http://kmi.open.ac.uk/~zdenek http://www.uni-karlsruhe.de/ http://www.aifb.uni-karlsruhe.de/WBS/www-broker http://www.aifb.uni-karlsruhe.de/ekaw99 http://www.aifb.uni-karlsruhe.de/WBS/dfe/seke-cfp.html http://www.iiia.csic.es/~richard/KnowComp-track.html http://www.aifb.uni-karlsruhe.de/WBS/dfe/keml98/index.html http://www.cs.vu.nl/~frankh/KR98-VV.html http://www.cs.vu.nl/~patveck http://www.cs.vu.nl/~joeri http://www.aifb.uni-karlsruhe.de/WBS/dfe/index.html http://www.cs.vu.nl/~frankh http://www.cs.vu.nl/~yde Mark Willems Proceedings of the Workshop on (Trans)Actions and Change in Logic Programming and Deductive Databases (DYNAMICS'97). Post-Conference of International Logic Programming Symposium (ILPS-97), Port Jefferson, Long Island N.Y., USA, 1997 ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/dfe/paper/Dynamic.ps Specification of Dynamics for Knowledge-based Systems Building knowledge-based systems from reusable elements is a key factor in their economic development. However, one has to ensure that the assumptions and functionality of the reused building block fit to each other and the specific circumstances of the actual problem and knowledge. We use the Karlsruhe Interactive Verifier (KIV) for this purpose. We show how the verification of conceptual and formal specifications of knowledge-based systems can be done with it. KIV was originally developed for the verification of procedural programs but it fits well for verifying knowledge-based systems. Its specification language is based on algebraic specification means for the functional specification of components and dynamic logic for the algorithmic specification. It provides an interactive theorem prover integrated into a sophisticated tool environment supporting aspects like the automatic generation of proof obligations, generation of counter examples, proof management, proof reuse etc. Such a support is essential in making verification of complex specifications feasible. We provide some examples on how to specify and verify tasks, problem-solving methods, and their relationships. http://www.aifb.uni-karlsruhe.de/WBS/dfe/index.html http://www.ira.uka.de/~schoeneg Using KIV to Specify and Verify Architectures of Knowledge-Based Systems Proceedings of the 12th IEEE International Conference on Automated Software Engineering (ASEC-97), Incline Village, Nevada 1997 ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/dfe/paper/kiv.ps.Z During the last years, a number of formal specification languages for knowledge-based systems have been developed. Characteristic for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects: they have to provide means to specify a complex and large amount of knowledge and they have to provide means to specify the dynamic reasoning behaviour of a knowledge-based system. This paper will focus on the second aspect, which is an issue considered to be unsolved. For this purpose, we have surveyed existing approaches in related areas of research. We have taken approaches for the specification of information systems (i.e., Language for Conceptual Modelling and Troll), approaches for the specification of database updates and the dynamics of logic programs (Transaction Logic and Dynamic Database Logic), and the approach of Evolving Algebras. This paper, which is a short version of a longer report, concentrates on the methodology of our comparison and on the conclusions we have drawn. The actual comparison between the languages has been removed from this version because of space limitations. KA Through ML The acquiring of knowledge by identifying concepts and relationships in data and by generalising from examples or identifying patterns in data. Induction Artificial Neural Networks Clustering, Discovery Instance Based Learning Bayesian Learning Explanation Based Learning Re-inforcement Learning Analogy and Case Base Learning Evolutionary Systems Inductive Logic Programming Simulated Annealing Symbolic Genetic Algorithms Hybrid University Of York Machine Learning Group Oxford University Computing Laboratory Machine Learning Group The Machine Learning Group - University of Louisville The Machine Learning Research Group - University of Texas at Austin Department of Computer Science - University of Turin GMD Artificial Intelligence Research Division Artificial Intelligence Laboratory of the J. Stefan Institute, Ljubljana Machine Learning Group at the University of California, Irvine Machine Learning research group at the Department of Computer Science of the Katholieke Universiteit Leuven Machine Learning Group at the Basser Department of Computer Science, University of Sydney Machine Learning at Keio University, Japan Porto Inductive Logic Programming Group, Portugal The Learning Lab, CMU University of Wisconsin, Madison IGOR, Georgia Tech Machine Learning Laboratory, University of Massachusetts Navy Center for Applied Research in Artificial Intelligence (ML), Naval Research Laboratory Center for Biomedical Modeling Research,University of Nevada Machine Learning Group, Basser Department of Computer Science, University of Sydney Australia Austrian Research Institute for Artificial Intelligence (ML), Austria Katholieke Universiteit Leuven, Belgium ML Group, University of Waikato, New Zealand KA and ML Research Group, University of Ottawa University of Kaiserslautern University of Aberdeen University of Aberystwyth University of Portsmouth University of Salford University College, Dublin Monash University Inductive Inference Group Vrije Universiteit Brussel, Artificial Intelligence Lab Univ. of Helsinki, Pattern Matching and Data Mining Projec Laboratoire de Recherche en Informatique (LRI) Karlsruhe Research Center - Environment and Technologies, Institute for Applied Computer Science TH Leipzig, Forschungsgruppe Algorithmisches Lernen TU Berlin, Fachgebiet Methoden der Kuenstl.Intelligenz TU Chemnitz, Professur Kuenstliche Intelligenz Maschinenbau und Verfahrenstechnik Univ. Dortmund, Artificial Intelligence Unit Univ. Frankfurt/Main, Laboratory for Data Mining (LDM) Univ. Kaiserslautern, Zentrum fuer lernende Systeme and Anwendungen Univ. Karlsruhe Inst. AIFB University of Bristol, machine learning research group Universita di Torino LACAM, Universite degli studi di Bari Artificial Intelligence Lab at the University of Coimbra University of Porto, LIACC Machine Learning Group Stockholm University Knowledge Engineering and Communication Lab ARIAI (AT),Austrian Institute for Artificial Intelligence,3 Shottengasse, A-1010 Vienna, Austria Department of Informatica,University of Bari, Via Amendola 173, 70126 Bari, Italy Bulgarian Academy of Science, Institute of Information Technologies, Acad G Bonchev St Block 29A, 1113 Sofia, Bulgaria Forth, Institute of Computing Science, Daedalou 20, PO Box 1385, 71110 Heraklion, Greece Helsinki University, Department of Computing Science, PO Box 26, Fin-00014 Helsinki, Finland LIRMM-INRIA, 161 Rue Ada, F-34392 Montpelier Cedex 5, France Milano Polytechnique, Dept di Electronica e Informazione, Politecnio di Milano, Pizza Leonardo da LAFORIA-IBP, University of Paris-6, BP 169, 75252 Paris Cedex 5, France Department of Information, Prague University of Economics, W Churchill Square 4, CZ-130-647 Prague 3, Czech Republic School of Information and Software Engineering,University of Ulster, Northern Ireland, County Londonderry George Mason University, Washington, USA Center for Automated Learning and Discovery (CMU) Institute for the Study of Learning and Expertise Oxford Computing Laboratory Machine Learning Group University of Texas Machine Learning Research Group University College Irvine University of Wisconsin Agnar Aamodt David Aha Kevin Ashley Karl Branting Ivan Bratko Peter Clark Padraig Cunningham Peter Edwards William Frawley Mark Keane Yves Kodratoff Janet Kolodner Ryszard Michalski Donald Michie Marvin Minsky Fraser Mitchell Tom Mitchell Stephen Muggleton Gregory Piatetsky-Shapiro Ross Quinlan Jude Shavlik LorenzaSaitta Derek Sleeman Stefan Wrobel Jan Zytkow Pavel Brazdil Wray Buntine Jaime Carbonell Gerry DeJong Tom Dietterich Ken Forbus Geoff Hinton Pat Langley Steve Minton Ray Mooney Mike Pazzani Bruce Porter Claude Sammut David Wilkins Exploratory Data Analysis Intelligent Data Analysis Agents Knowledge Base Refinement Theory Revision Knowledge Discovery in Datasets Data Mining Co-operative Knowledge Acquisition Learning Apprentice Systems Case Base Reasoning Reinforcment Learning Abduction Knowledge Acquisition Workshop European Knowledge Acquisition Workshop European Conference on Machine Learning International Conference on Machine Learning Principles of Knowledge Discovery in Databases UK Case Based Reasoning European Workshop on CBR International Conference on CBR Inductive Logic Programming (conference) Journees Francaises d’Apprentissage Machine Learning Journal Journal of Data Mining and Knowledge Discovery Knowledge Engineering Review Expert Systems with Applications ILP Newsletter Esprit II Machine Learning Toolkit (MLT) CADET CADSYN COMPOSER Deja Vu, Nirmani Esprit III BR Project Inductive Logic Programming (ILP) Esprit III StatLog Esprit IV Inductive Logic Programming (ILP2) EPSRC KrustWorks Knowledge Acquisition Expert Systems / KBS development Data analysis Design Planning Diagnosis Decision Support Speech recognition CN2 C4.5 Clementine ID3 Machine Learning Toolbox MineSet AQ11 ReCall CBR Express REMIND Alice Data Mariner MOBAL http://ai.iit.nrc.ca/bibliographies/ http://www.cs.bham.ac.uk/~anp/bibtex/kdd.bib.html http://www.ai-cbr.org/ Machine Learning List Knowledge Discovery Nuggets ai-cbr AI Statistics Mailing List datamine-l (Data Mining/Knowledge Discovery/Data Warehousing European Mailing List on Learning Robots (Volker Klingspor) Hybrid Models - Learning and Architectures Inductive Logic Programming Network Knowledge Acquisition (Kaw) Uncertainty in AI AI-SGES http://www.gmd.de/ml-archive/ http://www.aic.nrl.navy.mil/~aha/research/machine-learning.html http://www.ics.uci.edu/AI/ML/Machine-Learning.html http://www.cs.bham.ac.uk/~anp/TheDataMine.html http://www.kdnuggets.com/ http://mlis.www.wkap.nl/ http://www.csd.abdn.ac.uk/research/machine_learning.html http://www.ai-cbr.org/ http://nathan.gmd.de/projects/ml/ http://www.cs.orst.edu/~tgd/mlj/ http://www.turing.gla.ac.uk/r_and_d/ml.htm http://www.aic.nrl.navy.mil/~aha/people.html http://www.sis.port.ac.uk/~bramerma/sges/sges.htm ESPRIT EPSRC, DARPA Fraser Mitchell Derek Sleeman M Winter Robin Boswell Susan Craw 5/1/1999 Reuse 'Reuse may be about products (i.e., models or software) or about process (i.e., activities). Clearly both aspects interweave. This part of the ontology is sketchy because the main body of work on reuse in knowledge engineering is described by PSMs and ontologies. Actually one could say that the work on PSMs and ontologies is the work on reuse in knowledge engineering. This concept is rather an umbrella and points to some related activities in software engineering (SE).' Ontologies, Problem-solving methods (PSMs), Software Reuse propagated from Ontologies and PSMs Robert Allen David Garlan Paul Clements Jun-Jang Jeng Betty Cheng Ali Mili Rym Mili Roland Mittermeir Fatma Mili Charles Krueger, E. Gamma M. Shaw A.G. Sutcliff N.A.M. Maiden Will Tracz Douglas Smith Michael Lowry John Penis Peter Alexander Gregor Snelting Amy Moormann Zaremski Jeannette Wing Ontologies PSMs, Software Reuse Software Architectures Software Components Ontologies PSMs Software Reuse Yearly workshop on Software Reuse Workshop on Compositional Software Architectures, Jan 1998 http://www.obs.com/workshop/ws9801 Yearly International Conference on Software Reuse Yearly International Conference on Automated Software Engineering propagated from Ontologies and PSMs http://www.sema.es/projects/SER SPECWARE AMPHION NATURE http://www.swi.psy.uva.nl/projects/IBROW3/home.html propagated from Ontologies and PSMs KIDS SPECWARE AMPHION CORBA E. Gamma, R. Helm, R. Johnson, and J. Vlissides: Design Patterns, Addison-Wesley Pub., 1995 C. W. Krueger (1992). Software Reuse, ACM Computing Surveys, 24(2):131-184 H. Mili, F. Mili, and A. Mili: Reusing Software: Issues and Research Directions, IEEE Transactions on Software Engineering, 21(6):528-562, 1995 M. Shaw and D. Garlan: Software Architecture: Perspectives on an Emerging Discipline, Prentice Hall, 1996 R. Studer, V. R. Benjamins und D. Fensel: Knowledge Engineering: Principles and Methods, to appear in Data and Knowledge Engineering (DKE), 1998 A.G. Sutcliff and N.A.M. Maiden (1994): Domain Modeling for Reuse. In Proceedings of the 3rd International Conference on Software Reuse, Rio de Janeiro, 1994 reuse@wunet.wustl.edu http://pebbles.cs.utk.edu/ http://www.inria.fr/orion/KBUP-home/kbup-home.html http://www.inria.fr/orion/KBUP-home/kbup-home.html#wwwlinks http://www-ksl-svc-lia.dia.fi.upm.es:5915/ ESPRIT EPSRC, DARPA Dieter Fensel Richard Benjamins March 13, 1998 PSMs A PSM might be defined as an abstract, domain-independent specification of the reasoning process of a knowledge-based system. Research in this area is concerned with developing (a) new PSMs, (b) theories and methodologies for PSM development and reuse, (c) libraries of reusable PSMs, (d) tools to support PSM development and reuse, (e) languages for representing PSMs. A recent development (as shown by the IBROW KADS CommonKADS Components of Expertise VITAL Generic Task Role Limiting Method-to-Task Generalized Directive Models Assumption-Based Search-Based Stanford SMI Stanford KSL University of Amsterdam SWI University of Karlsruhe AIFB University of Southern California ISI Open University KMi University of Nottingham AIG Free University of Brussels Unilever Research Manfred Aben Hans Akkermans Juergen Angele A Anjewierden Richard Benjamins Joost Breuker B Chandrasekaran Jose Cuena Stefan Decker Dieter Fensel John Gennari Yolanda Gil Frank van Harmelen Gertjan van Heijst Masahiro Hori G Klinker J McDermott S Marcus Martin Molina Enrico Motta Mark Musen Kieron O’Hara Klas Orsvarn Christine Pierret-Golbreich A Puerta Frank Puppe Remco Straatman Guus Schreiber Nigel Shadbolt Luc Steels Rudi Studer Bill Swartout Annette ten Teije S Tu Andre Valente Walter van de Velde Bob Wielinga Zdenek Zdrahal Ontologies software engineering knowledge representation PSM libraries PSM notations automated PSM generation Web mediated PSM selection PSM evaluation Sisyphus I-III experiments Banff KA for KBS Workshops (KAW) KAW’98: http://ksi.cpsc.ucal gary.ca/KAW/KAW98/KAW98Proc.html KEML workshops European Knowledge Acquisition Workshop - EKAW’99 Workshop on PSMs - IJCAI’97 Workshop on Applications of Ontologies PSMs - ECAI’98 International Journal of Human-Computer Studies Knowledge Engineering Review IEEE Intelligent Systems Expert Systems with Applications Knowledge Acquisition AI Magazine International Journal of Expert Systems Artificial Intelligence Data and Knowledge Engineering KADS-I KADS-II GAMES VITAL IBROW3 (KA)2 Protege HPKB knowledge acquisition knowledge modelling KBS implementation knowledge management software reuse CommonKADS Workbench WebOnto KREST PC-Pack CommonKADS library (Breuker, J. A. and Van de Velde, W., eds. (1994). The CommonKADS Library for Expertise Modelling. Amsterdam: IOS Press. Stefik, M. (1995). Introduction to Knowledge Systems. San Francisco: Morgan Kaufmann. Karbach, W., Linster, M. and Voss, A. (1990). Models, methods, roles and tasks: many labels - one idea?. Knowledge Acquisition, 2(4), 279-300. Studer , R. et al. (1998). Knowledge Engineering: Principles and methods. Data and Knowledge Engineering, 25(1-2), 161-197. (????) http://www.isi.edu/isd/HPKB-PSMS/PSMS-biblio.html http://arti.vub.ac.be/~walter/papers/issues/doc/bibliography3_10.html http://ksi.cpsc.ucalgary.ca/KAW98S/menzies1/ http://www.swi.psy.uva.nl/mailing-lists/kaw-psm/home.html http://www.swi.psy.uva.nl/mailing-lists/kaw/home.html http://smi-web.stanford.edu/projects/protege/ ftp://swi.psy.uva.nl/pub/keml/keml.html http://kmi.open.ac.uk/~john/vital/vital.html http://swi.psy.uva.nl/projects/CommonKADS/home.html http://swi.psy.uva.nl/projects/IBROW3/home.html http://smi-web.stanford.edu/projects/protege/Hpkb-web/ http://www.cs.utexas.edu/users/mfkb/related.html http://www.cis.ohio-state.edu/lair/Projects/GTToolset/toolset.html http://www.psyc.nott.ac.uk/aigr/research/ka/SisIII/ ESPRIT EPSRC, DARPA Enrico Motta Arthur Stutt Mar 26, 1998 PSMs Concerned with developing reusable and sharable knowledge, originally static domain knowledge formal ontologies, conceptual ontologies" OR "implemented ontologies KSI-Stanford University SWI-University of Amsterdam AIFB-University of Karlsruhe LIA-Technical University of Madrid ISI-University of Southern California National Research Council-Italy Boeing LRI-University of Paris-Sud IRIT-University Paul Sabatier KMi-Open University SMI-Stanford Osaka University AIG-University of Nottingham Unilever University of Murcia IRST Peter Clark, Boeing (USA) Natalya Fridman Noy, Northeastern University (USA) Carole D. Hafner, Northeastern University (USA) Mike Uschold, Boeing (USA) Asun Gomez-Perez, Technical University Madrid (Spain) Mariano Fernandez (Technical University Madrid, Spain) Adam Farquhar, Stanford University (USA) Nicola Guarino, National Research Council (Italy) Richard Benjamins, University of Amsterdam (the Netherlands) Ashok K. Goel, Georgia Tech (USA) Bill Swartout, University of Southern California/ISI (USA) Bob Wielinga, University of Amsterdam (the Netherlands) Chantal Reynaud, Lab of Research in Informatics (France) Chris Welty (Vassar College) Cristiano Castelfranchi (Italy) Derek Sleeman, University of Aberdeen (UK) Dieter Fensel, University of Karlsruhe (Germany) Rudi Studer, University of Karlsruhe (Germany) Ed Hovy, University of Southern California (USA) Enric Plaza, IIIA, Spain Enrico Motta, Open University (UK) Frank Puppe, University of Wuerzburg (Germany) Gertjan van Heijst, Knowledge Centre CIBIT (the Netherlands) Guus Schreiber, University of Amsterdam (the Netherlands) Hans Akkermans, Free University Amsterdam (Netherlands) John Bateman, Steling University (United Kingdom) John Gennari, UC-Irvine (USA) Joost Breuker, University of Amsterdam (the Netherlands) Kris Van Marcke, Bolesian (Belgium) Mark A. Musen, Stanford (USA) Masahiro Hori, IBM, (Japan) Michael Gruninger, University of Toronto (Canada) Nathalie Aussenac, University of Toulouse (France) Nigel Shadbolt, University of Nottingham (UK) Rodrigo Martinez Bejar, University of Murcia (Spain) ose Palma, University of Murcia (Spain) Riichiro Mizoguchi, Osaka University (Japan) Vipul Kashyap, Applied Research at Bellcore, (USA) Pim Borst, Unilever (the Netherlands) http://www.aifb.uni-karlsruhe.de/WBS/broker/rt.html#PSM http://www.aifb.uni-karlsruhe.de/WBS/ECAI98OM/ http://www.tzi.de/grp/i3 http://dl.kr.org/ Theoretical foundations Methodologies Software applications Links to many events can be found at: http://www.cs.utexas.edu/users/mfkb/related.html http://delicias.dia.fi.upm.es/WORKSHOP/ECAI98/index.html http://ksi.cpsc.ucalgary.ca/KAW/KAW98/KAW98Proc.html Formal Ontologies in Information Systems, FOIS-98, Italy Ontological Engineering. Spring Symposium Series. Stanford, 1997 http://wwwis.cs.utwente.nl:8080/kbs/EcaiWorkshop/homepage.html ECAI-96 Practical Aspects Of Ontology Development, AAAI-96 Basic Ontological Issues In Knowledge Sharing, IJCAI-95 Implemented Ontologies, ECAI-94 Knowledge Sharing And Information Interchange IJCAI-93 Chambery Formal Ontology, Padova Italy (March 1993) International Journal of Human Computer Stdies (special issue: 1995, 43(5/6):623-965 thematic issue: 1997, 46(2/3)) Knowledge Engineering Review (special issue: 1998, 13(1)) IEEE-Intelligent Systems (special issue: 1999, february) Data and Knowledge Engineering Almost all project are referred to at http://www.cs.utexas.edu/users/mfkb/related.html Ontolingua Plinius Kactus Methontology Generalized Upper Model Sensus Ontology HPKB Wordnet http://www.aifb.uni-karlsruhe.de/WBS/broker/KA2.html http://www.arttic.com/GRASP/ http://research.swisslife.ch/pakm98.html "knowledge representation natural language understanding the Ontology Server http://www-ksl-svc.stanford.edu:5915/ http://WWW-KSL-SVC-LIA.dia.fi.upm.es:5915/ http://ONTOLINGUA.NICI.KUN.NL:5915/ http://swi.psy.uva.nl/projects/Kactus/toolkit/about.html http://www.kr.org/top/bibliography.html mailto:ontology@cs.umbc.edu mailto:kaw@swi.psy.uva.nl http://www.cs.utexas.edu/users/mfkb/related.html http://krr.irst.itc.it:1024/ontology.html http://www.medg.lcs.mit.edu/doyle/top/ EC- http://www.cordis.lu/esprit/home.html DARPA (USA), NWO Richard Benjamins November 1998 SpecificationLanguages languages for formalising and/or operationalising knowledge models for KBS operational vs declarative, general purpose languages from Software Engineering vs. special purpose languages for Knowledge Engineering SWI,University of Amsterdam (CML, (ML)^2). VU Amsterdam (DESIRE) Free University of Brussels (KREST) GMD, Bonn (MoMo) University of Karlsruhe (KARL) University of Nottingham (QIL) LRI Paris (TFL/TASK) Open University UK (VITAL-CML) SICS (GSLA) University of Compiegne, AIDE IIIA Barcelona (MILORD, Noos) Guus Schreiber Frank van Harmelen Jan Treur Frances Brazier Sabine Geldof Hans Voss Rudi Studer Dieter Fensel Nigel Shadbolt Enrico Motta Per Kreuger Gilles Kassel Enric Plaza Thomas Wetter Christine Pierret knowledge engineering software engineering verification executable specification languages support tools for formal methods specification of control knowledge automated code-generation from specification specification methodology KEML’97,’96,’95 ftp://swi.psy.uva.nl/pub/keml/keml.html KEML’98 http://www.aifb.uni-karlsruhe.de/WBS/dfe/keml98/index.html KAW’98,96 http://ksi.cpsc.ucalgary.ca/KAW/KAW.html http://www.acia.org/ekaw/ EKAW’99 http://www.aifb.uni-karlsruhe.de/ekaw99/ EKAW’96 Journal of Human Computer Interaction International Journal of Intelligent Systems Software Engineering and Knowledge Engineering IEEE Transactions on Knowledge and Data Engineering Data and Knowledge Engineering Journal of Automated Software Engineering KADS MIKE VITAL DESIRE if only.. support tools for various languages ftp://swi.psy.uva.nl/pub/keml/biblio.txt http://www.cs.vu.nl/~frankh/abstracts/KER95.html http://www.aifb.uni-karlsruhe.de/WBS/dfe/publications95.html keml@swi.psy.uva.nl kaw@swi.psy.uva.nl ftp://swi.psy.uva.nl/pub/keml ESPRIT Annette ten Teije Frank van Harmelen 16 June 1998 SpecificationLanguages Validation and Verification comprise a set of techniques used in software engineering (and therefore in knowledge engineering) to evaluate the quality of software systems (including KBS). Verification is the process of checking whether the software system meets the specified requirements of the users, while validation is the process of checking whether the software system meets the actual requirements of the users. [Preece, Proceedings of Banff’98] finding anomalies in rule-bases generating adequate test-cases analysis of conceptual models formal methods refinement and machine learning VU Amsterdam KU Leuven LRI Paris Universite de Savoie University of Liverpool University of New South Wales University of Karlsruhe University of Souther California University of Tsukuba University of Washington Central Connecticut State University University of Aberdeen Griffith University Robert Gordon University Aberdeen Tufts University Det Norske Veritas University of Tulsa University of Miami Siemens Austria Concordia University Montreal Catholijn Jonker Jan Treur Frank van Harmelen Annette ten Teije Jan van Thienen Geert Wets Marie-Christine Rousset Marc Ayel Frans Coenen Tim Menzies Dieter Fensel Dan O’Leary Takao Terano Alon Levy Neli Zlatareva Alun Preece Gregoris Antoniou Susan Craw Jim Schmolze Anca Vermesan Rose Gamble Robert Plant Hermann Kaindl T. Radhakrishnan R. Shinghal Software Engineering Knowledge Engineering Software Engineering Machine Learning VV of multi-agent systems anomaly detection anomaly repair and knowledge revision methodology formalisms testing applications EUROVAV’95 EUROVAV’97 http://www.econ.kuleuven.ac.be/congres/eurovav/eurovav97.htm EUROVAV’99 ECAI’96 workshop http://www.csd.abdn.ac.uk/~apreece/ECAI96/workshop.html KR’98 workshop http://www.cs.vu.nl/~frankh/VVKR98/index.html AAAI’97 workshop http://www.aaai.org/Workshops/1997/validation-ws97.html AAAI’98 workshop DEX’98 workshop http://dee.csc.liv.ac.uk/~frans/dexaWorkshop Knowledge Engineering Review Decision Support Systems International Journal of Intelligent Systems Expert Systems with Applications IEEE Intelligent Systems The International Journal of Expert Systems ViVa http://www.cri.dk/ViVa/ http://www.dnv.no/research/safekbs/safekbs.htm Power management control centers clinical protocols medical diagnostic KBS product formulation KBS World Wide Web VLSI manufacturing COVER VERITE MetaCHECK SACCO COCTO SYCOJET http://www.csd.abdn.ac.uk/~apreece/Research/vvbiblio.html ftp://swi.psy.uva.nl/pub/keml/VV-bib.html vavtalk-admin@csd.abdn.ac.uk see bibliographies, projects, workshops ESPRIT Frank van Harmelen 16 June 1998 SpecificationLanguages Development of methods and tools for managing knowledge resources in a company, in order to facilitate access and reuse of this knowledge Document-based Corporate Memory (CM) Knowledge-based CM Case-based CM Distributed CM Knowledge Sharing Knowledge Integration Knowledge Servers Enterprise Modeling Information Retrieval Intelligent Agents CSCW Linguistics Analysis... AIAI AIFB CEA DFKI INRIA Stanford Univ. Swiss Life Toronto Univ UTC Compiegne.. Andreas Abecker Jean-Francois Ballay Jean-Paul Barthos Olivier Corby Stefan Decker Rose Dieng Jean-Louis Ermine Jerome Euzenat Fox Fraser Alain Giboin Knut Hinkelmann Kuhn Ann Macintosh Nada Matta Frank Maurer Jean-Pierre Poitou Ulrich Reimer Myriam Ribiere Gaele Simon Rudi Studer Mike Uschold Rob van der Spek Gertjan van Heijst Enterprise modelling Web Agents Knowledge servers Ontology servers Data Mining Information retrieval Text understanding Case-Based reasoning Datawarehousing Web Search Engines KAW EKAW AAAI’97 Spring Symposium on Artificial Intelligence in Knowledge Management AAAI’97 Workshop on AI and Knowledge Management Int. Symposium on the Management of Industrial and Corporate Knowledge (ISMICK) Knowledge-Based Systems for Knowledge Management in Enterprises International Conference on Practical Aspects of Knowledge Management (PAKM) ECAI’98 Interdisciplinary Workshop on Building, Maintaining, and Using Organizational Memories IJHCS Management Science IEEE-Intelligent Systems Journal of Universal Computer Science ICARE MNEMOS knowledge management APECKS CYGMA DOLMEN HYTROPES MKSM ONTOLINGUA REX SAGACE WebCOKACE "Engineering and Managing Knowledge, Schreiber, Akkermans, Anjewierden, de Hoog, Shadbolt, van de Velde, Wielinga, (1999) MIT Press kaw@swi.psy.uva.nl http://ksi.cpsc.ucalgary.ca/AIKM97/ http://ksi.cpsc.ucalgary.ca/KAW/KAW96/KAW96Proc.html http://ksi.cpsc.ucalgary.ca/KAW/KAW98/KAW98Proc.html http://www.hds.utc.fr/~iiia/ISMICK97.html http://www.iicm.edu/jucs_3_8/ http://www.brint.com/km/ http://www.km-forum, http//www.apqc.org/Subscribe.HTM http://www.sveiby.com.au/ http://www-ksl.stanford.edu/knowledge-sharing/papers/README.html http://www.inria.fr/Equipes/ACACIA-eng.html EC... DARPA... Rose Dieng April 8, 1998 Knowledge Acquisition Methodologies Knowledge Acquisition methodologies provide methods and tools for systematically extract and model knowledge used for building knowledge-based systems or solving other knowledge management problems CommonKADS PROTEGE MIKE Stanford SMI Stanford KSL University of Amsterdam SWI University of Karlsruhe AIFB University of Southern California ISI Open University KM University of Nottingham AIG Free University of Brussels Unilever Research Andreas Abecker Manfred Aben Hans Akkermans Juergen Angele Anjo Anjewierden Nathalie Aussenac-Gilles Richard Benjamins Pascal Beys Francies Brazier Joost Breuker B Chandrasekaran Paul Compton Jose Cuena Susan Craw Stefan Decker Dieter Fensel Brain Gaines John Gennari Yolanda Gil Ashok K. Goel Asuncion Gomez-Perez Frank van Harmelen Gertjan van Heijst Achim Hoffmann Masahiro Hori Catalijn Jonker G Klinker S Marcus J McDermott Tim Menzis Martin Molina Enrico Motta Mark Musen Udo Hahn Kieron O’Hara Klas Orsvarn Christine Pierret-Golbreich Alun Preece A Puerta Frank Puppe Debbie Richards Remco Straatman Mildred Shaw Guus Schreiber Nigel Shadbolt Yuval Shahar Maarten van Someren Luc Steels Rudi Studer Bill Swartout Annette ten Teije Jan Treur S Tu Andre Valente Walter van de Velde Thomas Wetter Bob Wielinga Zdenek Zdrahal Software Engineering Software Reuse Requirement Engineering Ontologies Problem-Solving Methods knowledge representation Intelligent Systems http://ksi.cpsc.ucalgary.ca/KAW/ http://ksi.cpsc.ucalgary.ca/KAW/KAW98/KAW98Proc.html ftp://swi.psy.uva.nl/pub/keml/keml.html#events http://www.aifb.uni-karlsruhe.de/ekaw99/ http://www.aifb.uni-karlsruhe.de/WBS/dfe/PSM/proceedings.html http://delicias.dia.fi.upm.es/WORKSHOP/ECAI98/index.html International Journal of Human-Computer Studies Knowledge Engineering Review IEEE Intelligent Systems Expert Systems with Applications Knowledge Acquisition AI Magazine International Journal of Expert Systems Artificial Intelligence Data and Knowledge Engineering CommonKADS PROTEGE MIKE KADS Components of Expertise VITAL Generic Task Role Limiting Method-to-Task Generalized Directive Models KACTUS (KA)2 IBROW3 CommonKADS Workbench WebOnto KREST PC-Pack CommonKADS library ftp://swi.psy.uva.nl/pub/keml/keml.html Benjamins and D. Fensel: Knowledge Engineering: Principles and Methods Data and Knowledge Engineering (DKE), 25(1-2):161-197, 1998 Engineering and Managing Knowledge, Schreiber, Akkermans, Anjewierden, de Hoog, Shadbolt, van de Velde, Wielinga, (1999) MIT Press mailto:kaw@swi.psy.uva.nl mailto:keml@swi.psy.uva.nl http://smi-web.stanford.edu/projects/protege/ ftp://swi.psy.uva.nl/pub/keml/keml.html http://kmi.open.ac.uk/~john/vital/vital.html http://swi.psy.uva.nl/projects/CommonKADS/home.html http://www.commonkads.uva.nl/ http://swi.psy.uva.nl/projects/IBROW3/home.html http://smi-web.stanford.edu/projects/protege/Hpkb-web/ http://www.cs.utexas.edu/users/mfkb/related.html http://www.cis.ohio-state.edu/lair/Projects/GTToolset/toolset.html http://www.psyc.nott.ac.uk/aigr/research/ka/SisIII/ ESPRIT NSF DARPA DFG BMFB Dieter Fensel Enrico Motta Arthur Stutt November 10, 1998 Evaluation of Knowledge Acquisition The research topic is meta science. It deals with the evaluation of a scientific enterprise, the work and results of the knowledge acquisition community Sisyphus-I Sisyphus-II Sisyphus-III Sisyphus-IV (KA)2 Nigel Shadbolt Guus Schreiber Frank van Harmelen Tim Menzis Richard Benjamins Dieter Fensel Mark Linster Francis Brazier Joost Breuker Masahiro Hori Paul Compton Alun Preece Ashok Goel Eleni Stroulia Richard Benjamins Dieter Fensel Asun Gomez-Perez Stefan Decker Michael Erdmann Enrico Motta Mark Musen Brian Gaines Mildred Shaw Frank Puppe Guus Schreiber Bob Wielinga Debbie Richards Jeffrey Bradshaw (KA)2 workshop in 1999 KAW-98 Track on Evaluation on knowledge-acquisition methodologies KAW-98 Track on Sisyphus-III and Sisyphus-IV Sisyphus-I Sisyphus-II VT Experiment Sisyphus-III Sisyphus-IV (KA)2 http://www.cse.unsw.edu.au/~timm/pub/eval/ International Journal of Human-Computer Studies (IJHCS), 44(3-4), 1996 http://www.aifb.uni-karlsruhe.de/WBS/broker/rt.html http://www.cse.unsw.edu.au/~timm/pub/eval/ http://www.cse.unsw.edu.au/~timm/pub/eval/ http://www.aifb.uni-karlsruhe.de/WBS/broker/KA2.html DARPA DFG BMFB Dieter Fensel 10, 1998 Knowledge Elicitation A set of techniques and methods to elicit an expert’s knowledge through some form of direct interaction with that expert (un)structured interview protocol analysis think-aloud protocols card-sort laddering grids AIG-University of Nottingham University of Murcia SWI-University of Amsterdam Nigel Shadbolt Maarten van Someren Jacobijn Sandberg Rodrigo Martinez Bejar Cognitive Science Knowledge Acquisition Natural Language Understanding International Journal of Human-Computer Studies expert systems knowledge management PC-PACK ODE VOID Webgrid Varieties of knowledge elicitation techniques, N.J. Cooke, IJHCS (1994), 41, 801-849 The efficacy of knowledge elicitation techniques: a comparison across domains and levels of expertise, Burton, Shadbolt, Rugg, Hedgecock, Knowledge Acquisition (1990), 2, 167-178 Engineering and Managing Knowledge, Schreiber, Akkermans, Anjewierden, de Hoog, Shadbolt, van de Velde, Wielinga, (1999) MIT Press mailto:kaw@swi.psy.uva.nl Richard Benjamins November 1998 Motta http://kmi.open.ac.uk/ksg.html http://kmi.open.ac.uk/~enrico/design-papers.html http://kmi.open.ac.uk/~enrico/ke-papers.html http://kmi.open.ac.uk/~enrico/vital-kr-papers.html http://kmi.open.ac.uk/~enrico/kn-mgt-papers.html mailto:E.Motta@open.ac.uk +44 1908 653506 +44 1908 653169 http://kmi.open.ac.uk/~enrico/ Z.Zdrahal http://kmi.open.ac.uk/~enrico/papers/ijhcs_psm.ps.gz http://kmi.open.ac.uk/~enrico/ http://www.aifb.uni-karlsruhe.de/WBS/dfe/dieter.html http://www.aifb.uni-karlsruhe.de/Staff/sde.html Z.Zdrhal http://kmi.open.ac.uk/~enrico/papers/ekaw97.ps.gz The use of Ontologies for Specifying Tasks and Problem Solving Methods: A Case Study 10th European Workshop on Knowledge Acquisition, Modeling, and Management http://kmi.open.ac.uk/~enrico/ 1997 Trends in Knowledge Modelling: Report on the 7th KEML Workshop The Knowledge Engineering Review, Vol. 12(2) http://kmi.open.ac.uk/~enrico/ Z.Zdrahal http://kmi.open.ac.uk/kmi-abstracts/kmi-tr-26-abstract.html Parametric Design Problem Solving 10th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop http://kmi.open.ac.uk/~enrico/ Z.Zdrahal http://kmi.open.ac.uk/kmi-abstracts/kmi-tr-27-abstract.html Improving Competence by Integrating Case-Based Reasoning and Heuristic Search 10th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop gennari@smi.stanford.edu http://smi-web.stanford.edu/people/gennari/meontandem.jpg http://smi-web.stanford.edu/projects/protege/ http://smi-web.stanford.edu/projects/prot-nt/ http://smi-web.stanford.edu/projects/intermed-web/ http://smi-web.stanford.edu/projects/eon/ http://smi-web.stanford.edu/projects/protege/Rapit/ http://smi-web.stanford.edu/projects/protege/Hpkb-web/ http://smi-web.stanford.edu/ http://www.eerie.fr/LGI2P/lgi2p.htm Mourad Chabane Oussalah Parc Scientifique Georges Besse 30000 NIMES, FRANCE (33) 66 38 70 27 (33) 66 38 70 21 (33) 66 38 70 74 Oussalah@eerie.fr http://www.eerie.fr/LGI2P/rech.htm http://www.eerie.fr/EERIE/Welcome.html http://www.ensm-ales.fr/ http://www.masson.fr/cgi-bin/bookf.pl?1:1:is=2729606424 http://www.eerie.fr/LGI2P/lgi2p.htm Karima Messaadia mailto:messaadi@eerie.fr http://www.eerie.fr/~messaadi/page.html http://www.ensm-ales.fr/ http://www.eerie.fr/EERIE/Welcome.html Knut Hinkelmann DFKI GmbH, Postfach 2080, D-67608 Kaiserslautern, Germany ++49 631 205 3467 ++49 631 205 3210 mailto:hinkelma@dfki.uni-kl.de knowledge management knowledge representation and reasoning deductive databases and logic programming business process management information extraction German Research Center for Artificial Intelligence Mark Musen http://www.smi.stanford.edu/affiliates/images/musen.gif mailto:musen@smi.stanford.edu http://smi-web.stanford.edu/projects/prot-nt/ http://smi-web.stanford.edu/projects/eon/index.html http://smi-web.stanford.edu/projects/kmg/ http://amia2.amia.org/ http://ksi.cpsc.ucalgary.ca/KAW/KAW.html http://www.mieur.nl/mihandbook/r_1/handbook/home.htm http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~studer http://www.aifb.uni-karlsruhe.de/~mer/itknows98.pdf http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~mer/Sisy_FCA/ http://www.aifb.uni-karlsruhe.de/~mer/ ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/mer/dwh_km.ps http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~studer/ http://www.aifb.uni-karlsruhe.de/~dfe ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/mer/www.ps http://www.aifb.uni-karlsruhe.de/~sde/ http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~studer/ M.Daniel ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/sde/ekawfinal.ps http://www.aifb.uni-karlsruhe.de/~sde/ http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~studer/ M.Daniel ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/kaw96des.ps.Z http://ksi.cpsc.ucalgary.ca:80/KAW/KAW96/KAW96.html http://www.aifb.uni-karlsruhe.de/~sde/ http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~studer/ M.Daniel ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/kaw96des.ps.Z http://ksi.cpsc.ucalgary.ca:80/KAW/KAW96/KAW96.html http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~studer/ http://www.aifb.uni-karlsruhe.de/~dfe ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/mer/www.ps http://www.aifb.uni-karlsruhe.de/~sde/ http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~studer/ M.Daniel ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/sde/ekawfinal.ps http://www.aifb.uni-karlsruhe.de/~mer/ ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/mer/dwh_km.ps http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~mer/Sisy_FCA/ http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~studer http://www.aifb.uni-karlsruhe.de/~mer/itknows98.pdf http://www.aifb.uni-karlsruhe.de/WBS/broker/ Universitt Karlsruhe (TH), Karlsruhe, Germany Wissensmanagement AIFB/KBS Institut fr Angewandte Informatik und Formale Beschreibungsverfahren (AIFB) http://www.aifb.uni-karlsruhe.de/Staff/schmeck.html http://www.aifb.uni-karlsruhe.de/Staff/seese.html http://www.aifb.uni-karlsruhe.de/Staff/stucky.html http://www.aifb.uni-karlsruhe.de/Staff/studer.html ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/xps97.ps.Z http://www.fh-wolfenbuettel.de/fb/i/organisation/personal/angele/ http://www.aifb.uni-karlsruhe.de/~studer/ G-KARL, A graphical, formal, and executable specification language 4. Deutschen Tagung 'Wissensbasierte Systeme', XPS'97, Bad Honnef 1979 In this paper we present the language G-KARL. G-KARL allows to describe the static, the functional and the dynamic view to a knowledge based system (kbs) graphically. This graphical representation makes the communication between the expert and the knowledge engineer easier. The underlying conceptual model for G-KARL is derived from the KADS model of expertise. Every primitive of G-KARL may be mapped to a language primitive of the language KARL (Knowledge Acquisition and Representation Language). KARL is a formal language, so every primitive of G-KARL has a defined formal semantics. KARL is an executable language, so G-KARL is also executable which supports the validation of the model of expertise by testing and thus allows the model of expertise to be built by prototyping. G-KARL allows to visualize the execution of this model which additionally supports the validation process. While G-KARL contains well established graphical means for the different views like the OMT-notation for the static view, data flow diagrams for the functional view and program flow diagrams for the dynamic view, G-KARL also offers new graphical primitives for specifying elementary inference actions within data flow diagrams and for specifying sufficient and necessary conditions within the static view. In KARL these model elements are described by L-KARL, a logical language enriched by additional modeling primitives http://www.aifb.uni-karlsruhe.de/~mwi Empirical Evaluation of an Iterative Rule Discovery System Using a Bidirectional Search Strategy University of Karlsruhe 362 1997 An empirical evaluation of the ILP system JoJo-FOL is presented. JoJo-FOL uses an iterative method to discover rules for a single concept within the normal inductive logic programming framework. The search process starts with a very restricted hypothesis space to efficiently derive most general and correct clauses first. This strong language bias is weakened by iteratively increasing the number of variables allowed to occur in a clause such that JoJo-FOL is able to induce additional correct but more specific clauses. JoJo-FOL conducts its search through the hypothesis space in a bidirectional manner controlled by two criteria of success derived from the completeness and consistency requirements. In an empirical evaluation JoJo-FOL was tested on two ILP benchmark data sets. The results presented show that the iterative rule discovery method combined with the bidirectional search strategy leads to more general rules and a higher accuracy compared to well-known ILP systems on the tested domains. Th. Pirlein http://www.aifb.uni-karlsruhe.de/Staff/studer.html EIntegrating the Reuse of Commonsense Ontologies and Problem Solving Methods. University of Karlsruhe 354 1997 Software Reuse is a topic which is discussed in various computer science disciplines. Our work deals with the construction and reuse of knowledge base components from a knowledge engineering point of view. In order to reuse domain knowledge and problem solving knowledge experience has shown that powerful and integrated methods and tools are absolutely necessary. Therefore we describe KARO (Knowledge Acquisition Environment with Reusable Ontologies) providing different means for retrieving and adapting concept definitions of a commonsense ontology as part of a domain model construction process. In our framework commonsense ontologies are defined as highly domain-independent knowledge bases containing conceptual definitions which formalise theories about, e.g., time, objects, space. We introduce the formal, linguistic and graphical methods, the architecture and other properties of KARO. By reusing these ontologies in a well-defined way conceptual domain models can be developed easier and made more robust. In addition, we show how KARO and MIKE (Model-based and Incremental Knowledge Engineering Environment) are integrated in order to support the two main notions of reuse being investigated within knowledge engineering: the reuse of problem solving methods and the reuse of ontologies. Special emphasis is put on handling the interaction between problem solving methods and ontologies. ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/dfe/paper/seke.ps http://www.aifb.uni-karslruhe.de/~dfe R. Groenboom Specifying Knowledge-Based Systems with Reusable Components. 9th International Conference on Software Engineering and Knowledge Engineering (SEKE'97), Madrid, Spain 1979 The paper introduces an approach for the specification and verification of knowledge-based systems that combines conceptual and formal techniques. We identify four elements of the specification of a knowledge-based system: a task definition, a problem-solving method, a domain model, and an adapter that relates the other elements. We present abstract data types and a variant of dynamic logic as formal means to specify and verify these different elements. As a consequence of our conceptual model we can decompose the overall verification task of the knowledge-based systems into different proof obligations. Each proof obligation deals with a different aspect of the entire system. The use of the conceptual model in specification and verification improves understandability and reduces the effort for both activities. The modularization enables reuse of specifications and proofs. A knowledge-based system can be build by combing and adapting different components ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/dfe/paper/harmonise.ps http://www.iiia.csic.es/~richard/ http://www.aifb.uni-karslruhe.de/~dfe Chr. Pierret E. Motta http://www.aifb.uni-karlsruhe.de/Staff/studer.html B. Wielinga Making Knowledge Engineering Technology Work 9th International Conference on Software Engineering and Knowledge Engineering (SEKE'97), Madrid, Spain 1997 The rapid-prototyping approach of the early 1980's failed to deliver high-quality knowledge-based systems. As a reaction, in the early 90's, there has been a large activity in the knowledge engineering community to define methodologies for principled knowledge-based system development. These methodologies (e.g., CommonKADS, MIKE, VITAL, TASK) succeeded in organising the development process as a set of intermediate models of the functionality of the system to be built, to end in the final implementation. Feedback from industries that use these methodologies in practice reveals, however, that building a high-quality implemented system remains a difficult and error-prone process. The main reason for this is that, although the structure of the development process is rather well understood, the transition process is less clear. In this paper, we outline an approach for assuring the transition of a conceptual model into a final implementation which satisfies particular quality criteria. The approach is based on identifying adequate transition paths and activities on models in order to achieve particular quality criteria. An essential part of the approach is formed by methodological guidelines and tool support such as change-management systems, reusable libraries, translators, decision support tools, and support for validation and verification. ftp://ftp.aifb.uni-karlsruhe.de/pub/ren/pkdd97.ps.Z R. Wirth C. Shearer U. Grimmer Th. Reinartz J. Schlsser Chr. Breitner http://www.aifb.uni-karlsruhe.de/~ren/ G. Lindner Towards Process-Oriented Tool Support for KDD Proceedings of the 1st European Symposium on Principles of Data Mining and Knowledge Discovery, Trondheim, Norway 1997 Knowledge Discovery in Databases (KDD) is currently a hot topic in industry and academia. Unfortunately, the focus of research behind most emerging products is on underlying algorithms and modelling techniques. The main bottleneck for KDD applications is not the lack of techniques. The challenge is to exploit and combine existing algorithms effectively. In this paper, we describe the project Citrus which addresses these practically relevant issues. Starting from a commercially available system, we develop a scalable, extensible tool inherently based on the view of KDD as an interactive and iterative process. In this paper we sketch the main components of this system, namely an information manager for effective retrieval of data and results, an execution server for efficient execution, and a process support interface for guiding the user through the process http://www.fh-wolfenbuettel.de/fb/i/organisation/personal/angele/ http://www.aifb.uni-karlsruhe.de/~rpe http://www.aifb.uni-karlsruhe.de/~studer A. Oberweis http://www.aifb.uni-karlsruhe.de/Staff/gzi.html B. Dellen F. Maurer G. Pews W. Stein Abschlubericht der GI-Arbeitsgruppe 'Vergleichende Analyse von Problemstellungen und Lsungsanstzen in den Fachgebieten Information Systems Engineering, Software Engineering und Knowledge Engineering EMISA-Forum 1997 11 58 Mit Fragen der methodischen Untersttzung des Entwicklungsprozesses von Softwaresystemen beschftigen sich verschiedene Teildisziplinen der Informatik. Speziell sind hier das Software Engineering, das Information Systems Engineering und das Knowledge Engineering zu nennen. Whrend das Software Engineering insbesondere Beitrge zur Beschreibung des Entwicklungsprozesses durch Vorgehensmodelle und zur Beschreibung (nicht-)funktionaler Aspekte von Softwaresystemen geliefert hat, beschftigte man sich im Information Systems Engineering zunchst primr mit der Modellierung statischer Aspekte von Informationssystemen durch semantische Datenmodelle. In den zurckliegenden Jahren gewannen dynamische Aspekte jedoch immer mehr an Bedeutung. Im Knowledge Engineering wurden ursprnglich hauptschlich Fragen der methodischen Unterstzung der Wissenserhebung untersucht, in jngster Zeit bekamen jedoch Methoden zur Wissensmodellierung und -wiederverwendung ein immer strkeres Gewicht. Bei einer nheren Betrachtung dieser Fachgebiete zeigt es sich, daeinerseits eine Vielzahl gemeinsamer Fragestellungen und Lsungsanstze existiert, andererseits aber auch sehr unterschiedliche Problemstellungen untersucht werden. Um einen systematischen Vergleich der Methodiken zu ermglichen, erarbeitete die Arbeitsgruppe einen Kriterienkatalog, mit dem charakteristische Eigenschaften einer Methodik erfat werden knnen. Dieser Kriterienkatalog wird nachfolgend verwendet, um jede der vier Methodiken detailliert zu charakterisieren. [aus der Einleitung] ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/kdd97c.ps.Z http://www.aifb.uni-karlsuhe/~ren/ G. Lindner http://www.aifb.uni-karlsruhe.de/~studer/ A Guided Tour through the Data Mining Jungle 3rd International Conference on Knowledge Discovery in Databases (KDD'97). 1997 An important success factor for the field of KDD lies in the development and integration of methods for supporting the construction and execution of KDD processes. Crucial aspects in this context are the (incremental) development of a precise problem description, a decomposition of this top level problem description into manageable and compatible subtasks which can be reused, and a selection and combination of adequate algorithms for solving these subtasks. In this paper we describe an approach for supporting the systematic decomposition of a KDD process into subtasks and for selecting appropriate problem-solving methods and algorithms for solving these subtasks. Our approach has been partially integrated into the CLEMENTINE system and has been used to develop a real world application in the area of prediction. http://www.aifb.uni-karlsruhe.de/WBS/dfe/ijcai.html http://www.aifb.uni-karlsruhe.de/~dfe/ A. Schnegge Hunting for Assumptions as Developing Method for Problem-Solving Methods. Workshop Proceedings Problem-solving Methods for Knowledge-based Systems in Connection with the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI'97), Nagoya, Japan 1997 Problem-solving methods (PSMs) for knowledge-based systems need to make assumptions to provide effective and efficient problem solving: assumptions about the scope of the problem they should solve and assumptions about the domain knowledge they can use as a resource for their reasoning process. If these assumptions are made explicit they can improve the reusability of PSMs by guiding the selection and refinement process of problem-solving methods for a given application and by defining goals for the acquisition process of domain knowledge. However, making the underlying assumptions explicit is not an easy task. The goal of our paper is to contribute to solve this problem. The main idea is to construct mathematical proofs and analysis of their failure as a systematic means for forming assumptions. Tool support is provided by adapting the Karlsruhe Interactive Verifier (KIV) for our purpose. KIV is an interactive theorem prover that returns with open goals if a proof could not be completed. These open goals can be used to derive the assumptions we are looking for. ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/dfe/spool/IB.ps http://www.aifb.uni-karlsruhe.de/~dfe/ An Ontology-based Broker: Making Problem-Solving Method Reuse Work. Workshop Proceedings Problem-solving Methods for Knowledge-based Systems in Connection with the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI'97), Nagoya, Japan 1997 We present the architecture of an intelligent broker for enabling the use of problem-solving methods via the World Wide Web (WWW). The core component of such a broker is realised by an ontologist and an adapter. Ontologies mediate between domain-specific requirements and knowledge, task-specific problem descriptions and method-specific terms describing the competence and requirements of the reasoning components. The ontological reasoning for relating the different ontologies is supported by the ontologies. Adapters are necessary to provide domain knowledge and case data to problem-solving methods and to rephrase the output of problem-solving methods into domain-specific terms. Therefore, the ontologist mediates the selection and adaptation process of PSMs whereas the adapter mediates the execution of them. ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/dfe/paper/www.ps http://www.aifb.uni-karlsruhe.de/~dfe/ http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~studer/ Ontology Groups: Semantically Enriched Subnets of the WWW. Proceedings of the International Workshop Intelligent Information Integration during the 21st German Annual Conference on Artificial Intelligence (KI'97), Freiburg, Germany 1997 The World Wide Web (WWW) can be viewed as the largest knowledge-based system that has ever existed. However, its support in automated inference is very limited. We present ontologies as means to enrich web documents for representing semantic information for overcoming this bottleneck. Ontologies enable informed search for information as well as the derivation of additional knowledge that is not directly represented as facts in the WWW. Such an ontology can be used by a subgroup of web users that share a common point of interest like it is known from newsgroups in the internet. Therefore, we propose ontologies to be used to annotate intranets or certain subnets within the entire WWW. The paper presents such an ontology that can be used to annotate web documents of a research community. In fact, we have chosen the knowledge acquisition community. Further, an architecture of an ontology-based broker is sketched that can make use of these ontologies for the automatic derivation of knowledge. ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/sde/ekawfinal.ps http://www.aifb.uni-karlsruhe.de/~sde/ M. Daniel http://www.aifb.uni-karlsruhe.de/~mer/ http://www.aifb.uni-karlsruhe.de/~studer/ An Enterprise Reference Scheme for Integrating Model Based Knowledge Engineering and Enterprise Modelling Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling, and Management (EKAW'97) 1997 In recent years the demand on business process modelling (BPM) became apparent in many different communities. To provide a unifying framework for different needs on enterprise modelling we define an enterprise reference scheme and show how the development of knowledge based systems can be incorporated in such a framework. From this framework conclusions for tool support are drawn. ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/dfe/paper/lib.ps http://www.aifb.uni-karlsruhe.de/~dfe/ The Tower-of-Adapter Method for Developing and Reusing Problem-Solving Methods. Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling, and Management (EKAW'97) 1997 We present a structured approach for developing problem-solving methods for knowledge-based systems. We view this process as a stepwise combination process of methods and adapters. We start from very generic search strategies with very general data structures and add adapters that refine the states of the search process, that refine state transitions, and that add assumptions necessary to link the competence of a method with given problem definitions and domain knowledge. The paper provides three novel contributions to knowledge engineering. First, we provide a structured approach for the development and adaptation of problem-solving method. Second, we show how the usability-reusability trade-off of task-specific versus task-independent problem-solving methods can easily be overcome by the virtual existence of specific methods. Third, we provide the concept of an integrated library combining reusable problem definitions, problem-solving methods, and adapters ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/dfe/paper/to.ps http://www.aifb.uni-karlsruhe.de/~dfe/ E. Motta http://www.aifb.uni-karlsruhe.de/~sde/ Z. Zdrahal Using Ontologies For Defining Tasks, Problem-Solving Methods and Their Mapping. Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling, and Management (EKAW'97) 1997 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 ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/ekaw97per.ps http://www.aifb.uni-karlsruhe.de/~rpe/ Reuse of Problem-Solving Methods and Family Resemblances Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling, and Management (EKAW'97) 1997 In the last years a common notion of a Problem-Solving Method (PSM) emerged from different knowledge engineering frameworks. As a generic description of the dynamic behaviour of knowledge based systems PSMs are favored subjects of reuse. Up to now, most investigations on the reuse of PSMs focus on static features and methods as objects of reuse. By this, they ignore a lot of information of how the PSM was developed that is, in principle, entailed in the different parts of a conceptual model of a PSM. In this paper the information of the different parts of PSMs is reconsidered from a reuse process point of view. A framework for generalized problem-solving methods is presented that describes the structure of a category of methods based on family resemblances. These generalized methods can be used to structure libraries of PSMs and - in the process of reuse - as a means to derive an incarnation, i.e. a member of its family of PSMs. For illustrating the ideas, the approach is applied to the task rsp. problem type of parametric design. ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/dfe/paper/Dynamic.ps http://www.cs.vu.nl/~patveck http://www.cs.vu.nl/~joeri http://www.aifb.uni-karlsruhe.de/WBS/dfe/index.html http://www.cs.vu.nl/~frankh http://www.cs.vu.nl/~yde Mark Willems Specification of Dynamics for Knowledge-based Systems Proceedings of the Workshop on (Trans)Actions and Change in Logic Programming and Deductive Databases (DYNAMICS '97). Post-Conference of International Logic Programming Symposium (ILPS-97), Port Jefferson, Long Island N.Y., USA 1997 During the last years, a number of formal specification languages for knowledge-based systems have been developed. Characteristic for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects: they have to provide means to specify a complex and large amount of knowledge and they have to provide means to specify the dynamic reasoning behaviour of a knowledge-based system. This paper will focus on the second aspect, which is an issue considered to be unsolved. For this purpose, we have surveyed existing approaches in related areas of research. We have taken approaches for the specification of information systems (i.e., Language for Conceptual Modelling and Troll), approaches for the specification of database updates and the dynamics of logic programs (Transaction Logic and Dynamic Database Logic), and the approach of Evolving Algebras. This paper, which is a short version of a longer report, concentrates on the methodology of our comparison and on the conclusions we have drawn. The actual comparison between the languages has been removed from this version because of space limitations. ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/dfe/paper/kiv.ps.Z http://www.aifb.uni-karlsruhe.de/WBS/dfe/index.html http://www.ira.uka.de/~schoeneg Using KIV to Specify and Verify Architectures of Knowledge-Based Systems Proceedings of the 12th IEEE International Conference on Automated Software Engineering (ASEC-97), Incline Village, Nevada 1997 Building knowledge-based systems from reusable elements is a key factor in their economic development. However, one has to ensure that the assumptions and functionality of the reused building block fit to each other and the specific circumstances of the actual problem and knowledge. We use the Karlsruhe Interactive Verifier (KIV) for this purpose. We show how the verification of conceptual and formal specifications of knowledge-based systems can be done with it. KIV was originally developed for the verification of procedural programs but it fits well for verifying knowledge-based systems. Its specification language is based on algebraic specification means for the functional specification of components and dynamic logic for the algorithmic specification. It provides an interactive theorem prover integrated into a sophisticated tool environment supporting aspects like the automatic generation of proof obligations, generation of counter examples, proof management, proof reuse etc. Such a support is essential in making verification of complex specifications feasible. We provide some examples on how to specify and verify tasks, problem-solving methods, and their relationships. European Knowledge Acquisition Workshop Dagstuhl Castle, Germany May 26 - 29, 1999 http://www.aifb.uni-karlsruhe.de/WBS/dfe/ http://www.aifb.uni-karlsruhe.de/Staff/studer.html Nathalie Aussenac-Gilles http://www.irit.fr/ACTIVITES/EQ_SMI/IMAGE/nat.JPG mailto:aussenac@irit.fr 05 61 55 82 93 Institut f. Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Universität Karlsruhe (TH), Karlsruhe, Germany Rainer Perkuhn rpe@aifb.uni-karlsruhe.de ++49-(0)721-608-4754 ++49-(0)721-693717 Knowledge Engineering Knowledge Acquisition Formal Specifications Problem Solving Methods Reuse in Software Engineering and Knowledge Engineering http://www.aifb.uni-karlsruhe.de/~rpe Requirements Engineering in MIKE Proceedings of EMISA-Fachgruppentreffen 'Requirements Engineering fr Informationssysteme' 1995 ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/emisa95.ps.Z http://www.aifb.uni-karlsruhe.de/WBS/ren/ren.html Describing and Integrating Competence Theories for Problem Solving Components and Machine Learning Algorithms Position Paper Collection of the 9th European Knowledge Acquisition Workshop (EKAW '96) 1996 ftp://ftp.aifb.uni-karlsruhe.de/pub/ren/ekaw96.ps.Z http://www.aifb.uni-karlsruhe.de/~rpe http://www.aifb.uni-karlsruhe.de/Staff/sde.html http://www.aifb.uni-karlsruhe.de/Staff/studer.html http://www.fh-wolfenbuettel.de/fb/i/organisation/personal/angele/ Modeling Problem-Solving Methods in New KARL Proceedings of the 10th Knowledge Acquisition for Knowledge-Based Systems Workshop (KAW'96) 1996 1.1 1.18 ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/kaw96adp+.ps.Z http://www.aifb.uni-karlsruhe.de/~rpe Reuse of Problem Solving Methods and Family Resemblances Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling, and Management (EKAW '97) 1997 ftp://ftp.aifb.uni-karlsruhe.de/pub/mike/ekaw97per.ps.Z http://www.aifb.uni-karlsruhe.de/~rpe Eine abstrakte Syntax fr das Parsen von Z Workshop über Softwareentwurf und Spezifikation in Simmern am 1./2. Oktober 1991 1991 http://www.iiia.csic.es/~richard/pictures/richard.gif Artificial Intelligence Research Institute CSIC - Spanish Scientific Research Council Campus UAB 08193 Bellaterra, Barcelona, Spain Richardr Benjamins mailto:richard@iiia.csic.es Methodologies for Knowledge Engineering and Acquisition problem-solving methods ontologies assumptions Problem solving for diagnosis Planning problem solving Using the World-Wide Web for making knowledge-system technology more widely available Scaling-up reuse through the World-Wide Web Artificial Intelligence Research Institute (IIIA) Dept. of Social Science Informatics (SWI) http://www.swi.psy.uva.nl/projects/SION-project/home.html http://www2.cordis.lu/tmr/src/grants/fmbi/950550.htm http://www.iiia.csic.es/~richard/ecai-ws.html http://www.iiia.csic.es/~richard/aips.html http://www.iiia.csic.es/~richard/KnowComp-track.html http://www.aifb.uni-karlsruhe.de/WBS/broker/KA2.html IJHCS http://www.iiia.csic.es/~richard/index.html M. Aben Structure-Preserving KBS Development through Reusable Libraries: a Case-Study in Diagnosis IJHCS 1997 259 288 http://www.iiia.csic.es/~richard/postscripts/ijhcs.ps Enric Plaza http://www.iiia.csic.es/~richard/index.html LNAI 1319: Knowledge Acquisition, Modeling and Management. Proceedings of the 10th EKAW. Springer-Verlag 1997 University of Groningen Instituut voor Wiskunde en Informatica, room 128 Blauwborgje 3 P.O. Box 800 NL 9700 AV Groningen the Netherlands Rix Groenboom mailto:rix@cs.rug.nl +31 50 363.3957 +31 50 363.3800 Formal Methods Software Architectures Asynchronous architectures Distributed databases GPS-systems KADS development method Decision support for Anesthesia http://www.aifb.uni-karlsruhe.de/WBS/ Institute for Applied Computer Science and Formal Description Methods (AIFB), Universitt Karlsruhe (TH), Karlsruhe, Germany /Staff/studer.gif Rudi Studer studer@aifb.uni-karlsruhe.de ++49-(0)721-608-3923/4750 ++49-(0)721-693717 Knowledge Engineering Formal Specification Languages Knowledge Discovery in Databases Knowledge Management http://www.aifb.uni-karlsruhe.de/WBS/ http://www.aifb.uni-karlsruhe.de/WBS/publications/index.html Institut fr Angewandte Informatik und Formale Beschreibungsverfahren Universitt Karlsruhe (TH) D-76128 Karlsruhe Gisela Schillinger gsc@aifb.uni-karlsruhe.de ++49-(0)721-608-4750 ++49-(0)721-693717 mailto:Stefan.Decker@aifb.uni-karlsruhe.de http://www.uni-karlsruhe.de/ http://www.aifb.uni-karlsruhe.de/WBS/broker/KA2.htm http://www.aifb.uni-karlsruhe.de/Works/index.engl.html