Ontology-based project level environmental impact assessment database design research and practice in China

For the rapid economic and social development, in China, there are tens of thousands of construction projects launched each year. During construction and operation periods, projects will be more or less destroy or impact environment of projects site and surrounding area. In China, project level environmental impact assessment (PEIA) has been executed more than ten years in terms of a state law in order to prevent or mitigate environmental impact of projects. PEIA is a data-driven or data-intensive research work. On one hand, it needs large amounts of dataset support. On the other hand, PEIA will produce abundant data and documents. Therefore, it is urgent to build PEIA database to standardize, integrate, and preserve these data in the long term, and thus to promote their wide sharing and usage. For the successful PEIA database building, database design is a crucial issue that decides what data elements and their attributes should be included and thus influences the range of application of the database. For traditional database design measures, it is hard to figure out all domain entities or objects and represent complex semantic relationships behind entities or objects. In this paper, we propose an ontology-based design method and mapping ontology to the Entity-Relationship (ER) model method for PEIA database. Based on ontology, all concepts of PEIA and their attributes as well as semantic relationships can be clearly and completely designed and transformed to the ER model. By this method, we have built National PEIA of China (NPEIA) that has integrated amounts of basic supporting datasets, such as geo-spatial data, environmental sensitive areas data and so on and more than 100,000 PEIA records that covers 13 different industries projects. With the development of the big data mining and linked open data, in the future we will focus on enriching and opening PEIA database and linking it with SEA (strategic environmental assessment) and PEA (planning environmental assessment). Data deep mining and analysis based on NPEIA and other related datasets are also our research emphasis to support national industrial structural adjustment and total amount control of pollution.