Ontology-based Scientific Data Integration And Sharing Methods

With the development of digital information science, the role of scientific data is gradually enhanced in research activities. It is not only an important strategic resource for economic development, national security, but also an important basis for government policy and scientific decision-making. As the most active, the most basic and most influential scientific and technological resources at information age, scientific data is fundamental to promote the development of scientific and technological progress and innovation. In the process of dissemination and circulation of scientific data, data sharing is achieved by data integration. That is"Turn pearls into necklaces"for the whole community to take advantage, fully reflecting the value and effectiveness of data.Although China has accumulated a large amount of data resources, but because of management, lacking of effective integration instruments, the degree of data sharing is relatively low, while there are a lot of data loss problems and sharing barriers. Domain knowledge is difficult to accurately describe, it is difficult to classify the large amounts of data and save it. The precision and recall rate of related information also need to be improved. How to discover new information by knowledge reasoning data in the process of integration and sharing of a large number of disorder data is the challenge during the research on data integration and sharing.In the process of coal mine safety warning, there are the problems on low utilization of data, knowledge fragmented, difficult to get information and so on. In the case of fragmented knowledge, the above problems led to data cannot rely on useful and reliable knowledge to derive new information. It is difficult to achieve precise gas disaster warning. As a new data integration method, Ontology can describe the conceptual knowledge of specific areas and associations. This article studies the ontology method and data integration sharing technology for the gas warning problems. The collected scientific gas early warning data was integrated, using ontology methods and OWL to describe the properties of relations between the conceptual data; to describe the inference rules between concepts by SWRL language, establishing gas data integration and share warning models based on ontology and achieving ontology-based knowledge information query and data sharing.Gas data ontology model can not only integrate and reason the previous gas data, but also deriving a large number of new data by knowledge reasoning. With this model, experts in the field of coal can draw more reliable conclusions.