KWTR: Ontology Learning
Main Contributors: Johanna Völker. See the list of contributors
- What is the state of the art of Semantic Web in your research field?
- A. Gomez-Perez and D. Manzano-Macho. A survey of ontology learning methods and techniques. OntoWeb Deliverable 1.5, May, 2003.
- P. Cimiano, J. Völker and R. Studer. Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text. Information, Wissenschaft und Praxis 57 (6-7): 315-320. October 2006.
- Provide references and short abstracts of three papers you consider as significant in your research field.
- Please provide one or more examples (either business, or research, or both) in which semantic web has been used (if you can, add some references).
- Are there existing tools or demos? Please indicate some of them.
- OntoLT: http://olp.dfki.de/OntoLT/OntoLT.htm
- JATKE: http://jatke.opendfki.de
- ASIUM: http://www-ai.ijs.si/%7eilpnet2/systems/asium.html
- OLE: http://nlp.fi.muni.cz/projects/ole/
- TextToOnto: http://sourceforge.net/projects/texttoonto/
- Text2Onto: http://ontoware.org/projects/text2onto/
- Mo'K Workbench
- What are the open problems in your Semantic Web research field? Why?
- Integration of ontology learning and evaluation
- Methodologies for semi-automatic ontology engineering
- Provide references and links of the most relevant Semantic Web research projects in your field.
- What challenges try these projects to overcome?
- NeOn: Context for learning networked ontologies
- SEKT: Data-driven change discovery by incremental ontology learning
- Dot.Kom: Integration of information extraction and ontology learning for knowledge management
- X-Media: Large-scale information extraction, ontology population and learning
- What are their foreseen benefits (both in market and scientific community)?
- NeOn: The NeOn reference architecture and the NeOn ontology engineering toolkit will provide a solid basis for the development of future semantic web applications.
- When, in your opinion, will projects’ results be ready for industry?
- Some of the results could be ready for industry in less than five year. However, ontology learning approaches developed in these projects will probably require much more time to be adopted by industrial applications.
- Do you think that it is important to invest (money and time) in these topics? Why?
- Approaches to the automatic acquisition of ontologies can help to reduce the amount of money and human resources required for building knowledge-intensive applications.
- What are, in your opinion, the most relevant Semantic Web challenges that will be solved in the long term (10 years)? Why?