Ontology Learning
| Authors | |
|---|---|
| Year of publication | 2006 |
| Type | Monograph |
| MU Faculty or unit | |
| Citation | |
| Description | Ontology learning is one of the essential topics in the scope of an important ar ea of current computer science and artificial intelligence -- the upcoming Semantic Web. As the Semanti c Web idea comprises semantically annotated descendant of the current world wide web a nd related tools and resources, the need of vast and reliable knowledge repositories is obvious. Onto logies present well defined, straightforward and standardised form of these repositories. There are many poss ible utilisations of ontologies -- from automatic annotation of web resources to domain representa tion and reasoning tasks. However, the ontology creation process is very expensive, time-consuming and uno bjective when performed manually. So a framework for automatic acquisition of ontologies would be very a dvantageous. In this work we present such a framework called OLE (an acronym for Ontology LEarning) a nd current results of its application. The main relevant topics, state of the art methods and techniques r elated to ontology acquisition are discussed as a part of theoretical background for the presentati on of the OLE framework and respective results. Moreover, we describe also preliminary results of progre ssive research in the area of uncertain fuzzy ontology representation that will provide us with natura l and reasonable instruments for dealing with inconsistencies in empiric data as well as for reas oning. Main future milestones of the ongoing research are debated as well. |
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