The fundamental failure of current approaches to ontology learning is to view it as single pipeline with one or more specific inputs and a single static output. In this paper, we present a novel approach to ontology learning which takes an iterative view of knowledge acquisition for ontologies. Our approach is founded on three open-ended resources: a set of texts, a set of learning patterns and a set of ontological triples, and the system seeks to maintain these in equilibrium. As events occur which disturb this equilibrium, actions are triggered to re-establish a balance between the resources. We present a gold standard based evaluation of the final output of the system, the intermediate output showing the iterative process and a comparison of performance using different seed input. The results are comparable to existing performance in the literature.
|Publication status||Published - 2007|
|Event||6th International Conference on Recent Advances in Natural Language Processing - Borovets, Bulgaria|
Duration: 27 Sep 2007 → 29 Sep 2007
|Conference||6th International Conference on Recent Advances in Natural Language Processing|
|Period||27/09/07 → 29/09/07|
- ontology learning
- knowledge acquisition