Hailong Su
ABSTRACT
Aiming at the practical problems such as imperfect semantic analysis function of recommendation service and low sharing degree among scientific data in the existing science and technology resources sharing, the center of knowledge association algorithm with intelligence is built through the mutual reflection evidence among different data subjects such as talents, enterprises, platforms, industries and scientific payoffs to realize multi-angle, multi-dimensional and multi-association data portrait. Through business understanding, data extraction, data processing, feature extraction, model construction, model deduction, model application, model evaluation and other mining steps, the data mining algorithm center with intelligence is established. Based on knowledge graph, an intelligent recommendation model for scientific data is proposed, and we construct vector model bank through machine learning, and realize intelligent recommendation and accurate pushing of scientific resources based on massive data of scientific research such as papers, patents, projects, and scientific reports. The problems of data centralization and unification, data systematization mapping, data application convenience, and data multiform and multi-scene application are effectively solved in the actual research work. By summarizing the theories, technologies and methods related to data sharing and application of S&T resources, this study aims to provide useful references for the wisdom upgrading of S&T resource sharing services and scientific and technical information service model innovation under the big data environment.
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Index Terms
- Research on Intelligent Recommendation of Science and Technology Resource Data Based on Semantic Intelligence Analysis
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