Using hamming similarity to map ontology learning: a new data mining system
Proceedings of the 2013 Research in Adaptive and Convergent Systems
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With this paper we describe an ontology-driven system that performs relation extraction over textual data. The system exploits expert knowledge of the domain, including lexical resources, in the form of an ontology to drive the extraction of patterns using manually annotated texts. Such patterns are then applied in order to identify candidates for relation extraction. Paired with basic, reliable named-entity-level text annotation, this results in the discovery of relations among entities in Italian newspaper articles. In the paper, we describe the system and measure its performance.