An Algebra Supporting Semantic Query for Graph Data Model
Xueming Tang, Nan Wu, Ying Pan · 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE) · 2022
Following the massive emergence of graph data such as the RDF graph and the knowledge graph, semantic query of graph data has become a research hotspot. However, most of the existing studies suffer from lower query efficiency of complex semantics and higher query rewriting cost, and so on. Moreover, these studies mainly improve the query efficiency of graph data from the application perspective, with less research on the related query theory. Query theory is a robust basis for realizing queries and their applications. In this paper, we present an algebra supporting semantic query for graph data model, firstly defining a generic graph data model on the basis of common graph models. Then proposing basic query operations and keyword query methods. Finally, proposing association query methods and query optimization strategies. The graph query algebra study in this paper has some reference value for the research on semantic query of graph data.