Confirmed Speakers and Panelists
George Mason University
Achim RettingerTrier University
Rensselaer Polytechnic Institute
Knowledge Graphs & Open Knowledge Network
Knowledge Graphs (KGs) have emerged as a compelling abstraction for organizing and interconnecting the world’s structured knowledge in order to integrate information extracted from multiple data sources. Such knowledge graphs, and knowledge networks, are beginning to play a central role in representing information extracted by AI systems and improving the predictions of AI systems by providing knowledge expressed in KGs as inputs. The interconnectedness among data elements that is represented by these graphs/networks is essential to the future of AI, particularly machine learning, as we move towards developing more sophisticated algorithms to solve more complex problems, and to solve problems more comprehensively. Big data and AI have already provided powerful solution across a very broad range of domains, e.g., medicine, healthcare, clean energy, climate change, and social issues. Knowledge graphs (i.e., connected data with semantically enriched context) have become essential technology to the extent that Gartner has predicted that knowledge graph (i.e., connected data with semantically enriched context) applications and graph mining will grow 100% annually through 2022 to enable more complex and adaptive data science. The US National Science Foundation’s Harnessing the Data Revolution (HDR) Big Idea identified an “Open Knowledge Network” as a core element of a data science environment.
Knowledge networks/graphs provide a powerful approach for data discovery, integration, and reuse. The NSF’s new Convergence Accelerator program, which focuses on transitioning research to practice and translational research, announced Track A on the Open Knowledge Network (OKN). The program calls for multidisciplinary and multi-sector teams to work together to build a cooperative and shared open knowledge network infrastructure to drive innovation across science, engineering, and humanities. The technical construction of knowledge graphs may leverage machine learning, deep learning, natural language processing, and AI algorithms to extract concepts and relationships, identify hidden connections, generate deep learning embeddings, predict new potential linkages, and infer new knowledge, not only based on pre-defined rules but also through embedding-driven representation learning methods to enable automatic reasoning based on large scale data. The applications of this technology are literally unbounded and apply to almost any domain, including homelessness, supply chain, climate change, bias in research, and disaster response. OKN initiative has generated best practices in the biomedical, geoscience, finance, and smart manufacturing domains.
Why attend the
On February 23-25, 2022, the NSF and White House Office of Science and Technology Policy (OSTP) sponsored a workshop to kick of a 4-month “OKN Innovation Sprint”. The use cases and applications identified in that meeting were from a very broad range of domains including climate change, disaster response and management, supply chain management, identifying bias in research, decarceration, homelessness, and nutrition security. Sprint teams will investigate construction and use of open knowledge networks within the context of specific applications in these domains. All teams will reassemble at a workshop in June to assess progress and develop a common strategy for an OKN, going forward. This workshop will also provide an excellent venue for reports from that exercise–as invited talks, reviewed papers, and/or posters.
workshop aims to invite
researchers, practitioners, and the general public to brainstorm the ideas related to OKN,
collaboratively build KGs for different domains or applications, develop AI algorithms to provide
intelligent services based on OKN, and discuss the social and economic implications related to OKN.
We are interested in (including but not limited to) the following
based on quantitative, qualitative, and mixed research methods.
- Building KGs for specific domains or applications
- Semantic annotation and ontologies for KGs
- Natural Language Processing (NLP) algorithms to build KGs
- Relationship extraction using Natural Language Processing (NLP) and deep learning
- Semantic reasoning based on semantic technologies or deep learning
- Smart services powered by OKN
- Best practices of open knowledge graphs
- Heterogeneous graph embedding, graph transformer & graph convolutional neural network
- Graph deep learning for semantic reasoning
- Visual searching and browsing of KGs
- Industrial applications of KGs: banking, financing, retail, healthcare, medicine, pharma, etc.
- KGs in computer vision, medical imaging
- KGs for explainable AI
- Social issues related to KGs and OKN
- Design principles for successful adoption and value delivery to data and user communities
- UI and UX for knowledge graphs and their applications
All deadlines are 11:59 PM Anywhere on Earth
Workshop paper submissions: May 26th, 2022
Workshop paper notifications: June 20th, 2022
Final submission of workshop program & materials: July 9th, 2022
Workshop date: August 15th, 2022
The workshop will be open for the whole conference.
Each submitted paper will be evaluated by three reviewers from the aspects of novelty, significance, technique sound, experiments, and presentations.
The reviewers will be program committee members or researchers recommended by the members.
All papers submitted should have a maximum length of 8 pages and demo papers should be no more than 4 pages.
All must be prepared using the ACM Conference Proceeding templates.
Authors are required to submit their papers electronically in PDF format.
At least one of the authors of the accepted papers must register
for the workshop in KDD 2022.
Online registration is available at https://kdd.org/kdd2022/index.html
Registration Deadline: TBA
University of Texas at Austin
University of South Carolina
Krzysztof W. JanowiczUniversity of California
Sergio BaranziniUCSF Weill Institute for Neurosciences
Sharat IsraniUCSF Institute for Computational Health Sciences
Lilit YeghiazarianUniversity of Cincinnati
Samuel KleinHarvard University
University of South Carolina