Abstract:In order to provide more specific knowledge and technology of plant field, the main task of KG (knowledge graph) is to extract a wealth of concepts and relationships. Due to the relation extraction is the most difficult in KG construction, this paper makes use of ontology learning, and proposes a nontaxonomic relation learning method to obtain representative concepts and their relations from unstructured and semistructured texts of Baidu Encyclopedia entry content by using lexiconsyntactic patterns based on dependency grammar analysis. Moreover, the methods of adding constraint models and words filtering were adopted to build heavy weight ontology automatically based on a lightweight ontology and greatly improved the precision of the relation extraction. The approach established a concept structure from the plant domain corpus, ameliorated the discovery of the most representative non-taxonomic relation, and formalized them in the standardized OWL 2.0. A set of experiments was performed using the approach implemented in the plant domain. The results indicated that extraction by patterns should be performed directly after natural language processing, which has a comparatively high accuracy compared to the former algorithms, and this approach can extract non-taxonomic relations with high effectiveness, which lays the foundation for KG construction of plant field.