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.