Knowledge graphs (KGs) are widely regarded as a key enabler for explainable machine learning with over 4B distinct users through Google alone. They are also used by a number of Fortune500 companies to provide key user-facing and backend functionality (e.g. chatbots, product descriptions, recommendations, etc.). However, deploying and using KGs at the core of small and medium-sized businesses or even for personal purpose is still challenging for most of the entities.
The goal of the innovative training network KnowGraphs was to address some of the key challenges related to the representation, extraction, operation and exploitation of KGs. To achieve this goal, the project developed time-efficient and effective representation, extraction, storage, verification and exploitation algorithms for KGs that can be easily employed by large and small companies as well as individuals. The legal implications of these developments as well as real ways to exploit these solutions were also considered. The societal ramifications of the results of this ITN are directly linked with current developments at the interface between data, algorithms and humans both at EU and worldwide level. By making KGs easier to use in practice, the project’s outcomes support the democratization and broadening of their use. Furthermore, by studying the legal consequences of the use of KGs in real-life applications, KnowGraphs’ results support the AI and Data Protection agendas of the EU, especially with respect to explainability, consent and explicit information pertaining to the use of artificial intelligence.