An Ontology-Based Service Platform for Scientist Knowledge

In the present paper we describe an ontology-based service platform to efficiently manage and maintain the explosive growth scientist knowledge. Due to their powerful knowledge representation formalism and associated inference mechanisms, ontology-based systems are emerging as a natural choice for the next generation of knowledge management systems [1]. Scientist service platform is designed to support a convenient and rapid way to get accurate and comprehensive information online. For the characteristics of the scientist knowledge and the large scale of the dataset, the proposed platform relies on scientist ontology, and takes Allegro Graph as database and then we explain the process of modeling the ontology-based platform with the dataset of scientist knowledge of Chinese Academy of Sciences. The platform can achieve a better performance than the keyword based information retrieval.