Abstract:

[Objective] Cross-endpoint association path retrieval is a crucial method for discovering scientific data association in large-scale distributed scenarios. However, the efficiency and accuracy of multi-endpoint and multi-hop queries pose significant technical challenges. The solutions and technologies addressing these challenges have broad and important application prospects. [Methods] To tackle these issues, this article presents a cross-endpoint association path retrieval technique driven by RDF class relationships. This technique constructs distributed endpoint RDF class association relationships, which map cross-endpoint data entity association discovery to RDF class association discovery. Utilizing RDF class association relationships enables dynamic encapsulation of SPARQL federated query statements and facilitates cross-endpoint discovery of association data. [Results] Through testing and verification, this technique has shown effectively the higher efficiency and quality of cross-RDF data endpoint association path retrieval, supporting dynamic queries with multiple data source endpoints, any association direction, and multiple hops.[Conclusions] The cross-endpoint association path retrieval technique driven by RDF class relationships offers an efficient and accurate solution for joint data querying in distributed environments, which is expected to play a significant role in complex network settings and big data applications.

Key words: RDF, linked data, association path retrieval, multi-hop query, cross-endpoint