Learning ontology from text is a challenge in knowledge engineering research and practice. Learning relations between concepts is even more difficult work. However, when considering only a particular domain in which the concept hierarchy and relations can be modeled manually within an acceptable period of time, the learning process may be simplified. We focus on learning composite concepts and building up a knowledge base from existing documents. Our approach tries to make the machine understand the documents sentence by sentence and finally fit the knowledge conveyed by the document in our pre-defined ontology. Basic semantic units are defined for reasoning with higher-level concepts, including classes and instances. An agricultural case study on learning instances from plant disease descriptions is presented with a web-based ontology learning tool.
展开▼
