document: Hierarchical , perceptron-like learning for ontology-based information extraction Recent work on ontology-based Information Extraction ( IE ) has tried to make use of knowledge from the target ontology in order to improve semantic annotation results . However , very few approaches exploit the ontology structure itself , and those that do so , have some limitations . This paper introduces a hierarchical learning approach for IE , which uses the target ontology as an essential part of the extraction process , by taking into account the relations between concepts . The approach is evaluated on the largest available semantically annotated corpus . The results demonstrate clearly the benefits of using knowledge from the ontology as input to the information extraction process . 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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