Knowledge Graph Construction and Complementation for Research Projects

Tracking and analyzing data from research projects is critical for understanding research trends and supporting the development of science and technology strategies. However, the data from these projects is often complex and inadequate, making it challenging for researchers to conduct indepth data mining to improve policies or management. To address this problem, this paper adopts a top-down approach to construct a knowledge graph (KG) for research projects. Firstly, we construct an integrated ontology by referring to the metamodel of various architectures, which is called the meta-model integration conceptual reference model. Subsequently, we use the dependency parsing method to extract knowledge from unstructured textual data and use the entity alignment method based on weakly supervised learning to classify the extracted entities, completing the construction of the KG for the research projects. In addition, a knowledge inference model based on representation learning is employed to achieve knowledge completion and improve the KG. Finally, experiments are conducted on the KG for research projects and the results demonstrate the effectiveness of the proposed method in enriching incomplete data within the KG.