Learning time-sensitive domain ontology from scientific papers with a hybrid learning method

Large numbers of available scientific papers makes the research of ontology construction an attractive application area. However, there are two shortcomings for most current ontology construction approaches. First, implicit time properties of domain concepts are rarely taken into account in current approaches. Second, current automatic concept relation extraction methods mainly rely on the local context information that surrounds current considered concepts. These two problems prevent most current ontology construction methods from being employed to their full potential. To tackle these problems, we propose a hybrid learning method to integrate concepts' global information and human experts' knowledge together into ontology construction, among which concepts' temporal attributes are taken into account. Our method first divides each concept into four time periods according to their attribution distribution on a time axis. Then global time-related attributions are collected for each concept. Finally, concept relations are extracted with a hybrid learning method. We evaluated our method by testing it on Chinese academic papers. It outperformed a baseline system based on only hierarchical concept relations, showing the effectiveness of our approach.