Simple ontologies for practical information extraction and advanced information extraction for practical ontologies

Information extraction can be regarded as a pragmatic approach to semantic understanding of natural language texts. Ontology is very important for modeling and specifying knowledge,e. g. relations between entities and concepts. Therefore,ontology is often used for definition of the information extraction tasks. The advanced information extraction technologies such as complex relation extraction can be utilized for learning language patterns,which can recognize ontological relations from free texts and extract relation instances. This paper describes an ontological model for information extraction tasks and presents a general machine learning framework DARE for learning relation extraction patterns and extracting relation instances. The DARE system has been intensively used for the English language. It is applied to the Chinese newspaper texts to detect Chinese relation extraction rules and relation instances.