KGC Workshop on

Knowledge-infused Learning

Co-located with The Knowledge Graph Conference 2021 May 3 - 6, 2021 — Digitally Online Join Proceedings
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Acceptance of K-iL @ KGC2021

K-iL workshop was accepted at the Knowledge Graph Conference 2021 on Knowledge-infused Learning.

Why Attend?

This workshop will bring researchers in academia, industry, humanitarian organizations, and healthcare practitioners at the confluence of knowledge representation, natural language understanding, and deep learning. Prior exposure to the basic concepts in NLP and DL is desirable, however, there are no prerequisites for attending the tutorial. We will cover basics and advanced techniques with sufficient use cases and demonstrations. Newcomers in the area will learn the basic principles of data science and the fundamentals of knowledge-infused learning. Expert attendees will appreciate promising, reliable, and practical approaches to overcoming familiar technical obstacles in social good domains.

The Workshop

Research in artificial intelligence and data science is accelerating rapidly due to an unprecedented explosion in the amount of information on the web. In parallel, we noticed immense growth in research around Knowledge Graphs from industry and academia. Most focus of Knowledge Graphs has been on its construction, however harnessing knowledge graphs/symbolic knowledge for end tasks is yet to be well explored. Particularly, integrating information from knowledge graphs with learning techniques for solving tasks. We call this “knowledge-infused learning” (KiL), an approach to encode knowledge for using it with deep learning.

Knowledge Graphs are important in its use for neuro-symbolic AI. In this changing world, retrospective studies for building state-of-the-art AI and Data science systems have raised concerns on trust, traceability, and interactivity for prospective applications in healthcare, finance, and crisis response. We expect that the KiL paradigm would account for both pieces of knowledge that accrue from domain expertise and guidance from physical models. Further, it will allow the community to design new evaluation strategies that assess robustness, explainability, interpretability, and fairness across all comparable state-of-the-art algorithms.

The proposed forum aims to address an escalating concern to develop a common substrate attracting people having machine learning skills with unstructured data and a better handle on the conceptual underpinnings of inference on structured data. Thus, we envision an audience, within the purview of Knowledge Graph Conference and having an interest in human-centered reasoning, personalized knowledge graph creation, and sustainable computing benefitting from the workshop.