Abstract
The paper describes several classes of tasks namely solving word problems, and classifying datasets using machine learning techniques, where the tasks may not be solvable because the information provided is incomplete. We explore the situation where one has a central concept and the missing information can either be a further descriptor / field of that concept or a (distantly) related concept. We describe how an ontology search engine has assisted in solving such problems, by summarizing the frequency of occurrence of descriptors found in a group of relevant ontologies, and by reporting which concepts are related to the central concept. The search engine used in this work has been ONTOSEARCH2. We further speculate about how such a “concept web” might be used to support the analysis and generation of natural language texts as well as spoken language.
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References
Sharma, S., Sleeman, D.: Refiner: A Case-Based Differential Diagnosis Aide for Knowledge Acquisition and Knowledge Refinement, EWSL 1988, 201–210.
Mitchell, T. Machine Learning. McGraw Hill, 1997.
Aiken, A., Sleeman, D.: Refiner++: A Knowledge Acquisition and Refinement Tool D Sleeman, Y Gil (ed), Capturing knowledge from domain experts: Progress & Prospects. KCAP 2003 Proceedings (Sanibel Island, Florida)
Thomas, E. Pan, J.Z., Sleeman, D.: ONTOSEARCH2: Searching Ontologies Semantically. In The 2007 International Workshop on OWL: Experiences and Directions (OWLED 2007), 2007.
Ding, L. et al.: Swoogle: A Search and Metadata Engine for the Semantic Web, In the Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management.
Patel, C. et al.: Ontokhoj: A semantic web portal for ontology searching, ranking, and classification. In Proc. 5th ACM Int. Workshop on Web Information and Data Management, New Orleans, Louisiana, USA, pp. 58–61.
Prud’hommeaux, B., Seaborne, A.: SPARQL query language for RDF. Technical report, World Wide Web Consortium. http://www.w3.org/TR/rdf-sparql-query/.
Tsang, E.: Foundations of Constraint Satisfaction. Academic Press, London and San Diego, 1993.
Villemont, M.: A Constraint Satisfaction Problem Description enhancement Web Tool. Aberdeen University MSc Thesis, 2006.
Sleeman, D., Chalmers, S.: Assisting domain experts to formulate and solve constraint satisfaction problems. Managing Knowledge in a World of Networks: 15th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2006)
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© 2009 Springer-Verlag London Limited
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Sleeman, D., Thomas, E., Aiken, A. (2009). Using Ontology Search Engines to support Users and Intelligent Systems solving a range of tasks. In: Allen, T., Ellis, R., Petridis, M. (eds) Applications and Innovations in Intelligent Systems XVI. SGAI 2008. Springer, London. https://doi.org/10.1007/978-1-84882-215-3_9
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DOI: https://doi.org/10.1007/978-1-84882-215-3_9
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