Abstract
With the popularity of Linked Open Data, a large amount of RDF data have been published and developed in the form of knowledge graphs, which can be publicly accessible via SPARQL endpoints. The efficiency of SPARQL querying on large-scale knowledge graphs has attracted increasing research efforts. In this paper, we propose a template-based query approach, which involves temporal, spatial, and domain-specific constraints to focus on certain resources of interest. Furthermore, query results which include a set of RDF triples are visualized in graph format to display entities and relationships in a user-friendly manner. We also analyze the visualized graph with ranking, partitioning, filtering, and statistics. Various template-based queries are designed and evaluated on the knowledge graph of DBpedia. It can be observed that template-based queries with temporal-spatial and domain-specific constraints can effectively facilitate users to obtain target answers by filtering out irrelevant information.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61572353) and the Natural Science Foundation of Tianjin (17JCYBJC15400).
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Wang, X., Xin, Y., Xu, Q. (2018). Template-Based SPARQL Query and Visualization on Knowledge Graphs. In: Liu, C., Zou, L., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10829. Springer, Cham. https://doi.org/10.1007/978-3-319-91455-8_17
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DOI: https://doi.org/10.1007/978-3-319-91455-8_17
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