Abstract
While Linked Data (LD) provides standards for publishing (RDF) and (SPARQL) querying Knowledge Graphs (KGs) on the Web, serving, accessing and processing such open, decentralized KGs is often practically impossible, as query timeouts on publicly available SPARQL endpoints show. Alternative solutions such as Triple Pattern Fragments (TPF) attempt to tackle the problem of availability by pushing query processing workload to the client side, but suffer from unnecessary transfer of irrelevant data on complex queries with large intermediate results. In this paper we present smart-KG, a novel approach to share the load between servers and clients, while significantly reducing data transfer volume, by combining TPF with shipping compressed KG partitions. Our evaluations show that smart-KG outperforms state-of-the-art client-side solutions and increases server-side availability towards more cost-effective and balanced hosting of open and decentralized KGs.
Highlights
Knowledge Graphs (KGs) have emerged as a promising data management foundation to provide scalable knowledge models that represent facts about entities as well as relations among these [13]
In this part of the study, we focus on the graph Waterloo SPARQL Diversity Benchmark (WatDiv)-100M as this is in line with the size of open KGs published in the LOD Cloud [6], with an average of 183M Resource Description Framework (RDF) triples
We introduced smart-KG, a hybrid approach to efficiently query Knowledge Graphs (KGs) on the Web, balancing the load between servers and clients
Summary
Knowledge Graphs (KGs) have emerged as a promising data management foundation to provide scalable knowledge models that represent facts about entities as well as relations among these [13]. Smart servers maintain compressed and queryable graph partitions, that is, KG “slices” that can be shipped, cached and be locally queried by smart clients. The smart clients implement query optimization and execution techniques to handle combinations of KG partitions and intermediate results of triple queries issued directly to the server, to evaluate SPARQL queries. A novel paradigm, smart-KG, to distribute the evaluation of SPARQL queries among clients and servers by leveraging the transfer of compressed KG partitions. Client-side query optimization and execution techniques that combine KG partition retrieval and intermediate results that ensure correct query evaluation. An empirical evaluation of smart-KG on synthetic and realworld KGs and queries, significantly outperforming state-ofthe art on server- and client-side SPARQL query processing.
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