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| Adaptive Computing Method for the Determination of Scientific and Technological Innovation Indicators Based on a Knowledge Graph |
| Liu Zhihui, Wei Juanxia, Zhang Junsheng |
| Institute of Scientific and Technical Information of China, Beijing 100038 |
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Abstract Based on practices in decision-making, diverse schools of thought about innovation provide multiple theoretical perspectives, while big data allows the process to be data-driven. Both approaches have advanced the field of evidence-based policy making (EBPM) oriented data analytics. A knowledge graph for science and technology (S&T) decision-making is proposed based on knowledge organization that interrelates three elements for S&T decision-making. These elements include theories in the form of indicators, data as the input, and visualization as the output. The schema of the knowledge graph proposed in this research defines the classes and their attributes in three different layers (indicator, data, and visualization), under which the interrelationship of different elements is formed and inferred to provide the function of adaptive computation for decision-making. The application of the schema in the GII (global innovation index) and MSTI (main science and technology indicators) description demonstrates the validity of the proposed method in the scenario of innovation mapping and evaluation.
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Received: 14 March 2019
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