CIKM Workshop on Knowledge Injection in Neural Networks

Workshop Schedule (8.00 am PST to 12 noon PST Nov 1st 2021)

Workshop Opening Remarks – 8.00 am PST

Keynote Talk 1 by Subbaro Kambhampati – 8.05 am PST

Invited paper talk: Facts As Experts (Google Research) – 8.35 am PST

Panel discussion – 9.05 am (Panelists: Gary Marcus, Luis Lamb, Vered Shwartz, Partha Talukdar; Moderator: Gadi Singer)

Break – 10.05 – 10.15 am PST

Keynote Talk 2 by Antoine Bosselut 10.15 am to 10.45 am PST

Paper presentations -> 10.45 am to 12.00 noon

Concluding remarks – 12 noon to 12.05 am PST


List of Accepted Papers

  1. Lars Hoffmann, Christian Bartelt and Heiner Stuckenschmidt. Knowledge Injection via ML-based Initialization of Neural Networks

  2. Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang, Shijing Si, Dinghan Shen, Dong Wang and Lawrence Carin. Syntactic Knowledge-Infused Transformer and BERT models

  3. Ruben Branco, António Branco, João Silva and João Rodrigues. Commonsense Reasoning: how do Neuro-only and hybrid Neuro-Symbolic approaches compare?

  4. Manisha Verma, Kapil Thadani and Shaunak Mishra. COVID community question answering in presence of external curated knowledge

  5. Hiba Ahsan and Sudarshan Lamkhede. Improving Search Results Ranking Using a Knowledge Graph


Overview

Deep learning has made rapid progress in last decade, with neural network-based language and vision models achieving state of the art performance in various tasks. Yet, purely data-driven neural network models exhibit several issues impacting real world deployment of such models adversely. These include reliance on large quantities of training data, poor robustness, lack of generalization, poor explainability and glaring gaps in implicit and commonsense knowledge.

The availability of rich structured knowledge sources has spurred the research community into exploring Knowledge Injection in Neural Networks (KINN) as a means of mitigating the above-mentioned challenges. This has led to the development of hybrid AI systems which combine the purely data-driven learning of the neural network models with an infusion of knowledge from external sources. Such KINN systems include the development of retrieval augmented neural models, neuro symbolic systems and a plethora of combinations of NNs and knowledge graphs and structured knowledge bases.

Lastly, building AI systems that share human knowledge may have other interesting consequences, such as improved predictability and human-AI trust. But, humans often rely on both explicitly codified and implicitly understood forms of knowledge, and current AI systems seem to lack such knowledge. Hence, we are also interested in systems and methods that explore how knowledge injection can help compensate for the lack of such knowledge in AI systems.

Given the considerable potential and promise of knowledge injection in overcoming the current challenges associated with purely data-driven NN models, we propose a workshop on the theme of Knowledge Injection in Neural Networks (KINN).

Confirmed Speakers/Panelists


Important dates (Tentative)

Paper submission due: July 31st 2021

Notification of acceptance: August 29th 2021

Camera-ready copy due: Sep 4th 2021

Workshop date: November 1st 2021

All deadlines are 23:59 Hours PST


Workshop Organizers/Co-chairs

Vasudev Lal, Intel Labs USA

Yezhou Yang, ASU

Pasquale Minervini, UCL

Somak Aditya, MSR India

Sandya Mannarswamy, Intel India