Knowledge graphs at scale
Knowledge graphs (KGs) are a flexible knowledge representation paradigm intended to allow knowledge to be consumed by humans and machines. Hence, they are regarded as a key enabler for a number of technologies including question answering, personal assistants and artificial intelligence across all sectors including Industry 4.0, personalized medicine, legislation, economics and more. While different implementations of the KG paradigm are now used by several large companies (incl. Microsoft, Google, Facebook, Amazon, Samsung, Ebay and IBM) as a key component of their data products, their use is currently unattainable for the majority of companies and private users. Custom formal representation mechanisms, organisation-specific storage solutions and query languages as well as large dedicated maintenance teams (often 100+ people per graph) are only some of the current challenges faced by organizations aiming to manage KGs at scale. Developing and maintaining a company-specific infrastructure to represent, construct and maintain KGs is only viable for large organisations able to afford the corresponding costs. In addition, a plethora of open questions pertaining to the transfer, applicability and integration of legal rights of knowledge graphs remain completely unsolved. The overall objective of KnowGraphs (summarized in Figure 1.1) is to scale knowledge graphs to be accessible to a wide audience of (1) companies of all sizes and (2) end users across their professional and private life by using a multi-disciplinary and multi-sectorial approach.