KnowGraphs (Knowledge Graphs at Scale is a Maria Sklodowska-Curie Initial Training Network funded by the European Commission’s Horizon 2020: programme, call “Fostering new skills by means of excellent initial training of researchers” (H2020-MSCA-ITN-2019), project code 860801.
Project website: https://knowgraphs.eu/
Contact: Vladimir Alexiev
A knowledge graph (KG) is a programmatic way of modelling a knowledge domain with the help of subject-matter experts, data interlinking, and machine learning algorithms. 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, Web search; and artificial intelligence across numerous 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 as a key component of their data products (incl. Microsoft, Google, IBM, Facebook, Amazon, Samsung, Ebay, Uber, Pinterest, etc), 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.
The project aims to scale knowledge graphs, making them 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.
KnowGraphs focuses on addressing four of the facets of knowledge graph management: representation, construction & maintenance, operation, and exploitation. The project addresses these pillars by researching and developing novel methods, models and frameworks using a cross-disciplinary mix of methods from web science, data science, knowledge representation, knowledge engineering, Big Data, data law, and business innovation.
Having significant experience with commercial applications of KGs, Ontotext was invited as an Associate Partner in the project. We bring to bear our expertise with creating harmonized semantic models, semantic data integration, large-scale data integration pipelines, knowledge graph embeddings, semantic similarity, etc. in a range of industries including economic and commodity information, companies, startups and transactions;,scientific information, industrial data, etc.
This is the second ITN in which Ontotext collaborates after Cleopatra. We provide similar services: provide commercial use cases and relevant datasets (as appropriate); host research stays and internships for Ph.D. students; participate in bilateral research visits; contribute to training activities and events, including access to our product training (GraphDB and Ontotext Platform). We also promote the project in our local, national and international academic networks.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860801.