More and more data is generated by an increasing number of agents (applications and devices) that are connected to the Internet, all contributing to data that is available on the Web. When this data is analyzed, combined, and shared powerful, new techniques can be designed and deployed, such as artificial intelligence applied by personal assistants (e.g., Apple's Siri and Amazon Alexa), improved search engines (e.g., Google Search and Microsoft's Bing), and decentralized data storages (as opposed to a large, central storage used by big companies).
Due to this increase in data, methods originally used to model data have become insufficient: they isolate the data, which resides in different data sources, and make it hard for agents to exchange data on the Web. Data cannot be easily exchanged between agents, because every agent uses a different data model to describe the concepts and relationships of the data they generate and use.
The Semantic Web offers a solution to this problem: knowledge graphs, which are directed graph that provides semantic descriptions of entities and their relationships. A common way to generate these knowledge graphs is by using rules. During my PhD I investigated how we can improve the creation of these rules, the refinement of these rules, and the execution of these rules.