PhD Thesis

Storing and Querying Evolving Knowledge Graphs on the Web

In Storing and Querying Evolving Knowledge Graphs on the Web (2020)

The Web has become our most valuable tool for sharing information. Currently, this Web is mainly targeted at humans, whereas machines typically have a hard time understanding information on the Web. Using knowledge graphs, this information can be linked in a structured way, so that intelligent agents can act upon this data autonomously. Current knowledge graphs remain however rather static. As there is a lot of value in acting upon evolving knowledge, there is a need for evolving knowledge graphs, and ways to manage them. As such, the goal of this PhD is to allow such evolving knowledge graphs to be stored and queried, taking into account the decentralized nature of the Web where anyone should be able to say anything about anything. Concretely, four challenges related to this goal are investigated: (1) generation of evolving data, (2) storage of evolving data, (3) querying over heterogeneous datasets, and (4) querying evolving data. For each of these challenges, techniques and algorithms have been developed, which prove to be valuable for storing and querying evolving knowledge graphs on the Web. This work therefore brings us closer towards a Web in which both human and machine can act upon evolving knowledge.