6th Workshop on Automated Knowledge Base Construction (AKBC)

Extracting knowledge from text, images, audio, and video and translating these extractions into a coherent, structured knowledge base (KB) is a task that spans the areas of machine learning, natural language processing, computer vision, databases, search, data mining and artificial intelligence. Over the past two decades, machine learning techniques used for information extraction, graph construction, and automated knowledge base construction have evolved from simple rule learning to end-to-end neural architectures with papers on the topic consistently appearing at NIPS. Hence, we believe this workshop will appeal to NIPS attendees and be a valuable contribution.

Furthermore, there has been significant interest and investment in knowledge base construction in both academia and industry in recent years. Most major internet companies and many startups have developed knowledge bases that power digital assistants (e.g. Siri, Alexa, Google Now) or provide the foundations for search and discovery applications. A similarly abundant set of knowledge systems have been developed at top universities such as Stanford (DeepDive), Carnegie Mellon (NELL), the University of Washington (OpenIE), the University of Mannheim (DBpedia), and the Max Planck Institut Informatik (YAGO, WebChild), among others. Our workshop serves as a forum for researchers working on knowledge base construction in both academia and industry.

With this year’s workshop we would like to continue the successful tradition of the previous five AKBC workshops. AKBC fills a unique need in the field, bringing together industry leaders and academic researchers. Our workshop is focused on stellar invited talks from high-profile speakers who identify the pressing research areas where current methods fall short and propose visionary approaches that will lead to the next generation of knowledge bases. Our workshop prioritizes a participatory environment where attendees help identify the most promising research, contribute to surveys on controversial questions, and suggest debate topics for speaker panels. In addition, for the first time, AKBC will address a longstanding issue in the AKBC, that of equitable comparison and evaluation across methods, by including a shared evaluation platform, Stanford’s KBP Online (https://kbpo.stanford.edu/), which will allow crowdsourced labels for KBs without strong assumptions about the data or methods used. Together, this slate of high-profile research talks, outstanding contributed papers, an interactive research environment, and a novel evaluation service will ensure AKBC is a popular addition to the NIPS program.