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Refereed Papers

Track: Semantic Web

Paper Title:
Hierarchical, Perceptron-like Learning for Ontology-Based Information Extraction


Recent work on ontology-based Information Extraction (IE) has tried to make an increased use of the knowledge from the target ontology in order to improve the semantic annotation results. However, only very few approaches are able to benefit from the ontology structure and one of them is not a learning system, thus is not easy to adapt to new domains, whereas the other one does not perform semantic annotation of documents, but only ontology population.

This paper introduces a hierarchical learning approach for IE, which uses the target ontology as an essential part of the extraction process. Hierarchical classification takes into account the relations between concepts, thus benefiting directly from the ontology.

We also carry out evaluation experiments on the largest available semantically annotated corpus of 146 classes. The results demonstrate clearly the benefits of using knowledge from the ontology for ontology-based IE. We also demonstrate the advantages of our approach over other state-of-the-art learning systems on a commonly used benchmark dataset.

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