Supervisor

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Abstract

RDF Knowledge Graphs (KGs) are widely used for representing complex, interconnected data in domains such as semantic web, bioinformatics, and enterprise data integration as they excel at storing, querying and reasoning about relationships between entities. However, while such systems provide rich semantic capabilities, they commonly lack native support for advanced graph analytics. Techniques such as shortest path computation are fundamental in graph theory and can reveal insights like entity proximity, semantic similarity, and relationship strength, which are important in various applications like recommendation systems.

The goal of this project is to bridge this gap by combining the semantic query power of RDF Knowledge Graphs with efficient graph analytics algorithms to deliver scalable, graph-theoretic capabilities to semantic systems. To meet the high-performance demands of these applications, the integration will be written in C++.

This project presents the opportunity to work with one of the Computer Science department’s spinout companies, and success story, Oxford Semantic Technologies. As well as help candidates build their CV strong candidates will have the opportunity of summer internships with Oxford Semantics.

 

Background reading

 

Aidan Hogan et al. Knowledge Graphs. Synthesis Lectures on Data, Semantics, and Knowledge, Morgan & Claypool Publishers 2021, ISBN 978-3-031-00790-3, pp. 1-257

 

Heiko Paulheim.  Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web 8(3): 489-508 (2017)

Angela Bonifati, M. Tamer Özsu, Yuanyuan Tian, Hannes Voigt, Wenyuan Yu, Wenjie Zhang:
A Roadmap to Graph Analytics. SIGMOD Rec. 53(4): 43-51 (2024)