Extracting focused knowledge from the semantic web

Ontologies are increasingly being recognized as a critical component in making networked knowledge accessible. Software architectures which can assemble knowledge from networked sources coherently according to the requirements of a particular task or perspective will be at a premium in the next generation of web services. We argue that the ability to generate task-relevant ontologies efficiently and relate them to web resources will be essential for creating a machine-inferencable “semantic web”. The Internet-based multi-agent problem solving (IMPS) architecture described here is designed to facilitate the retrieval, restructuring, integration and formalization of task-relevant ontological knowledge from the web. There are rich structured and semi-structured sources of knowledge available on the web that present implicit or explicit ontologies of domains. Knowledge-level models of tasks have an important role to play in extracting and structuring useful focused problem-solving knowledge from these web sources. IMPS uses a multi-agent architecture to combine these models with a selection of web knowledge extraction heuristics to provide clean syntactic integration of ontological knowledge from diverse sources and support a range of ontology merging operations at the semantic level. Whilst our specific aim is to enable on-line knowledge acquisition from web sources to support knowledge-based problem solving by a community of software agents encapsulating problem-sloving inferences, the techniques described here can be applied to more general task-based integration of knowledge from diverse web sources, and the provision of services such as the critical comparison, fusion, maintenance and update of both formal informal ontologies.

[1]  G J Williams,et al.  The Protein Data Bank: a computer-based archival file for macromolecular structures. , 1977, Journal of molecular biology.

[2]  Nigel Shadbolt,et al.  Exploiting the Ontological Qualities of Web Resources: Task-Driven Agents Structure Knowledge for Problem Solving , 2000, CIA.

[3]  A. T. Schreiber,et al.  Proceedings of the 8th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop , 1994 .

[4]  James A. Hendler,et al.  Ontology-based Web agents , 1997, AGENTS '97.

[5]  George A. Miller,et al.  Introduction to WordNet: An On-line Lexical Database , 1990 .

[6]  V. R. Benjamins,et al.  WonderTools? A comparative study of ontological engineering tools , 2000, Int. J. Hum. Comput. Stud..

[7]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[8]  Guus Schreiber,et al.  Knowledge Engineering and Management: The CommonKADS Methodology , 1999 .

[9]  Paul J. Feltovich,et al.  Categorization and Representation of Physics Problems by Experts and Novices , 1981, Cogn. Sci..

[10]  Hiroshi Maruyama,et al.  XML and Java: Developing Web Applications , 1999 .

[11]  Dieter Fensel,et al.  Brokering Problem-Solving Knowledge at the Internet. , 1999 .

[12]  David Fenyö,et al.  The Biopolymer Markup Language , 1999, Bioinform..

[13]  Crow Lr.  Software agents for Internet-based knowledge engineering. , 2000 .

[14]  Dieter Fensel,et al.  Practical Knowledge Representation for the Web , 1999, Intelligent Information Integration.

[15]  Ramanathan V. Guha,et al.  Cyc: toward programs with common sense , 1990, CACM.

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

[17]  Allen Newell,et al.  The Knowledge Level , 1989, Artif. Intell..

[18]  Yuval Shahar,et al.  Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making , 1999 .

[19]  Robert M. MacGregor,et al.  Practical Experiences in Trading Off Ontology Usability and Reusability , 1999 .

[20]  Trevor J. M. Bench-Capon,et al.  KRAFT: knowledge fusion from distributed databases and knowledge bases , 1997, Database and Expert Systems Applications. 8th International Conference, DEXA '97. Proceedings.

[21]  H. Simon,et al.  The mind's eye in chess. , 1973 .

[22]  Kathleen Dahlgren,et al.  A linguistic ontology , 1995, Int. J. Hum. Comput. Stud..

[23]  Marian H. Nodine,et al.  Experience with the InfoSleuth Agent Architecture , 1998 .

[24]  A. T. Schreiber,et al.  Ontologies as vehicles for reuse: a mini-experiment , 1996 .

[25]  A. Newell.  Unified Theories of Cognition , 1990 .

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

[27]  Nigel Shadbolt,et al.  IMPS- Internet Agents for Knowledge Engineering , 1998 .

[28]  Michael J. Prietula,et al.  Expertise and error in diagnostic reasoning , 1981 .

[29]  Mike Uschold,et al.  A Framework for Understanding and Classifying Ontology Applications , 1999 .

[30]  Brian R. Gaines,et al.  Comparing conceptual structures: consensus, conflict, correspondence and contrast , 1989 .

[31]  Dieter Fensel,et al.  Relating Ontology Languages and Web Standards. , 2000 .

[32]  Timothy W. Finin,et al.  KQML as an agent communication language , 1994, CIKM '94.

[33]  Dieter Fensel,et al.  Community is knowledge! in (KA)2 , 1998 .

[34]  Matthias Klusch,et al.  Intelligent Information Agents: Agent-Based Information Discovery and Management on the Internet , 1999 .

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

[36]  Nigel Shadbolt,et al.  Acquiring and Structuring Web Content with Knowledge Level Models , 1999, EKAW.

[37]  Ramana Rao,et al.  A focus+context technique based on hyperbolic geometry for visualizing large hierarchies , 1995, CHI '95.

[38]  Carole D. Hafner,et al.  The State of the Art in Ontology Design: A Survey and Comparative Review , 1997, AI Mag..

[39]  Dieter Fensel,et al.  UPML: A Framework for Knowledge System Reuse , 1999, IJCAI.

[40]  W. Chase,et al.  Visual information processing. , 1974 .

[41]  Craig A. Knoblock,et al.  New Directions: Agents for Information Gathering , 1997, IEEE Expert.

[42]  Herbert A. Simon,et al.  The Roles of Recognition Processes and Look-Ahead Search in Time-Constrained Expert Problem Solving: Evidence From Grand-Master-Level Chess , 1996 .

[43]  M. R. Genesereth,et al.  Knowledge Interchange Format Version 3.0 Reference Manual , 1992, LICS 1992.

[44]  Friedrich Steimann.  Modelle und Modellierungssprachen in Informatik und Wirtschaftsinformatik , 2000 .

[45]  Dieter Fensel,et al.  The Component Model of UPML in a Nutshell , 1999 .

[46]  P L Schuyler,et al.  The UMLS Metathesaurus: representing different views of biomedical concepts. , 1993, Bulletin of the Medical Library Association.

[47]  Daniel E. O'Leary.  Impediments in the use of explicit ontologies for KBS development , 1997, Int. J. Hum. Comput. Stud..

[48]  Deborah L. McGuinness,et al.  An Environment for Merging and Testing Large Ontologies , 2000, KR.

[49]  Douglas B. Lenat,et al.  CYC: a large-scale investment in knowledge infrastructure , 1995, CACM.

[50]  Nigel Shadbolt,et al.  Representational Redescription within Knowledge Intensive Data-Mining , 1994 .

[51]  David Stuart Robertson,et al.  Use of Formal Ontologies to Support Error Checking in Specifications , 1999, EKAW.

[52]  Nicola Guarino,et al.  Understanding and building, using ontologies , 1997, Int. J. Hum. Comput. Stud..

[53]  Aldo Gangemi,et al.  A Medical Ontology Library That Integrates the UMLS MetathesaurusTM , 1999, AIMDM.

[54]  Michael Uschold,et al.  The Enterprise Ontology , 1998, The Knowledge Engineering Review.

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

[56]  J. Jastrow.  The Mind's Eye. , 1899 .

[57]  Michael R. Genesereth,et al.  Software agents , 1994, CACM.