Graphs provide a natural way of representing complex data. In rrecent years, graph anlytics has proliferated rapidly, and it has useful applications across a wide range of fields, such as social science, computer science, biology and archaeology. However, graph analytics is often computationally expensive. Regardless of implementation details that different data management systems may have, the need to capture semantics remains. Can the efficiency of graph analytics be improved by leveraging their semantics such as in the form of knowledge graphs?
The project aims to investigate: (1) how to effectively represent and manage the semantics of graph-structured data in the form of knowledge graphs, and (2) how to support efficient querying over graph-structured data using knowledge graphs.
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