Tutorial at WSDM'21, March 8, 2021, 9am IST
Commonsense knowledge is a foundational cornerstone of artificial intelligence applications. Whereas information extraction and knowledge base construction for instance-oriented assertions, such as Brad Pitt's birth date, or Angelina Jolie's movie awards, has received much attention, commonsense knowledge on general concepts (politicians, bicycles, printers) and activities (eating pizza, fixing printers) has only been tackled recently. In this tutorial we present state-of-the-art methodologies towards the compilation and consolidation of such commonsense knowledge (CSK). We cover text-extraction-based, multi-modal and Transformer-based techniques, with special focus on the issues of web search and ranking, as of relevance to the WSDM community.
1. Introduction to commonsense knowledge (Simon)
2. Text extraction (Simon)
3. Multimodal knowledge(Niket)
4. Deep learning-based techniques (Niket)
5. Evaluation of the acquired knowledge (Aparna)
6. Highlights, outlook and open issues (Aparna)
- Simon Razniewski is a senior researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany, where he heads the Knowledge Base Construction and Quality research area. He was previously assistant professor at the Free University of Bozen Bolzano (2014-2017), and spent research visits at the University of Queensland, AT&T Labs-Research and the University of California, San Diego. His research includes commonsense knowledge extraction, and he has relevant didactical experience from a lecture on information extraction, and seminars on commonsense knowledge.
- Niket Tandon is a senior research scientist at Allen Institute for Artificial Intelligence, Seattle, WA. His research interests are in commonsense knowledge acquisition and injecting commonsense into neural models for text understanding applications under the Aristo system which aced science exams and had wide press coverage. He completed his PhD from the Max Planck Institute for Informatics in Germany in 2016, that resulted in the largest automatically extracted commonsense knowledge base called WebChild, which has since been used in NLP applications and in Visual Question Answering. He founded an online project mentorship organization, PQRS Research, where he has supervised several undergraduate and graduate theses. He has taught in summer schools on relevant topics and has given a tutorial at CIKM 2017 on ``Commonsense for Machine Intelligence''.
- Aparna Varde is an associate professor of computer science at Montclair State University. Her research spans commonsense knowledge, smart cities, text mining and robotics. She gave tutorials at CIKM 2017 (Commonsense for machine intelligence), DASFAA 2015 (Scalable learning technologies for big data mining) and EDBT 2010 (The hidden web, XML and semantic web). Her tutorials have 2000+ views on SlideShare. Aparna teaches courses such as AI, data mining, machine learning, HCI, database systems and advanced databases. Her tutorial at DASFAA 2009 in the web knowledge discovery area was part of a summer school at QUT, Australia. She was the founder and chair of PIKM (PhD workshop in CIKM) and co-chaired it 5 times. She gave a talk in the Bloomberg Distinguished Speaker Series on commonsense knowledge in the smart cities paradigm. She has 3 best paper awards at IEEE conferences, around 100 publications, and grants from NSF, PSEG etc. She has been a visiting researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany. For details, please see (www.montclair.edu/~vardea).
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