H2020Doctoral network2019–2024

KnowGraphs · Knowledge Graphs at Scale

Horizon 2020 — Marie Skłodowska-Curie Actions

Duration: 2019-10-01 → 2024-03-31
EU contribution: €3,873,641
Participants: 8
Scheme: MSCA-ITN

Lines connect the coordinator with its partners.

Results in brief

Knowledge Graphs at Scale

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.

Data: CORDIS, © European Union

Project objective

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.

Original text from CORDIS.

Participants

UNIVERSITAET PADERBORN · PaderbornCoordinatorGermany
BABELSCAPE SRL · RomaItaly
GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER · HannoverGermany
IDRYMA TECHNOLOGIAS KAI EREVNAS · IRAKLEIOGreece
RIJKSUNIVERSITEIT GRONINGEN · GroningenNetherlands
UNIVERSITEIT MAASTRICHT · MaastrichtNetherlands
UNIVERSITY OF STUTTGART · StuttgartGermany
WIRTSCHAFTSUNIVERSITAT WIEN · WienAustria

KnowGraphs · Knowledge Graphs at Scale

Results in brief

Knowledge Graphs at Scale

Project objective

Participants

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