In the context of the Semantic Web, ontologies based on Description Logics are gaining more and more importance for knowledge representation on a large scale. While the need arises for high quality
ontologies with large background knowledge to enable powerful machine reasoning, the acquisition of such knowledge is only advancing slowly, because of the lack of appropriate tools. Concept learning
algorithms have made a great leap forward and can help to speed up knowledge acquisition in the form of induced concept descriptions. This work investigated whether concept learning algorithms have
reached a level on which they can produce results that can be used in an ontology engineering process. Two learning algorithms (YinYang and DL-Learner) are investigated in detail and tested with
benchmarks. A method that enables concept learning on large knowledge bases on a SPARQL endpoint is presented and the quality of learned concepts is evaluated in a real use case. A proposal is made
to increase the complexity of learned concept descriptions by circumventing the Open World Assumption of Description Logics.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:16616 |
| Date | 26 October 2017 |
| Creators | Hellmann, Sebastian |
| Contributors | Fähnrich, Klaus-Peter, Lehmann, Jens, Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English, German |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | urn:nbn:de:bsz:15-qucosa2-163403, qucosa:16340 |
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