High-performance Query Processing over Knowledge Graphs

The label “Knowledge Graph” (KG) has been used in the literature for over four decades, typically to refer to a collection of information about real-world entities and their inter-relationships. The proliferation of KGs in recent times opens up exciting opportunities for a broad range of semantic applications such as recommendations. However, unlocking the full potential of KGs in response to the growing deployment requires data platforms to efficiently store and process the content to support various applications. What began with extensions of relational database systems to store the content of KGs led to the design and development of a number of new specialized data management systems. Although progress has been made around building efficient KG data management systems, developing high-performance systems continues to pose research challenges. In this research, we studied the efficiency of existing systems for storing and processing KG content. Our results pointed to performance inconsistencies in representative systems across diverse query types. We address this by introducing a polyglot model of KG query processing to analyze each query and match it to the best-performing available systems. Experimental evaluation highlighted that our proposed approach provides consistently high performance. Finally, we investigated leveraging emerging hardware and its benefits to RDF data management and performance. To this end, we introduced a novel index structure, RDFix, that utilizes Persistent Memory (PM) to outperform existing read-optimized indexes as shown experimentally