AKBC 2021 (3rd Conference on Automated Knowledge Base Construction)
Virtual (online event)The AKBC conference is a research forum for knowledge base construction and related work, in both academia and industry.
Conferences and other events about knowledge graphs, linked data and related topics
While every effort has been made to keep these listings current, in light of the COVID-19 pandemic always check the linked event site for the most up-to-date information concerning that event.
The AKBC conference is a research forum for knowledge base construction and related work, in both academia and industry.
The theme of DCMI Virtual 2021 is metadata innovation, with a program that includes virtual, invited and moderated sessions.
The Graph + AI Summit Fall 2021 conference is focused on accelerating analytics and artificial intelligence with graph.
The inaugural workshop on Argumentative Knowledge Graphs (ArgKG) will explore the automatic construction of knowledge graphs that encode argumentative knowledge and structures as well as the incorporation of argumentation into factual knowledge graphs, drawing together researchers focusing on natural language processing, automatic knowledge graph construction, and computational analysis of argumentation.
This webinar will show how applying standards-based knowledge graph technology offers a new approach to physical asset management.
Dr. Janna Hastings and Professor Egon Willighagen will speak about their work on linked data, ontologies and deep learning in science.
The Graph + AI Summit Fall 2021 conference is focused on accelerating analytics and artificial intelligence with graph.
The International Semantic Web Conference (ISWC 2021) is the premier international forum for the semantic web and knowledge graph community.
The Wikidata Workshop 2021 brings together the scientific Wikidata community and industry to discuss trends and topics around this collaborative knowledge graph.
The VOILA 2021 workshop will focus on issues surrounding visualization and interaction for ontologies and linked data.