TY - GEN
T1 - First Workshop on Knowledge Injection in Neural Networks (KINN)
AU - Lal, Vasudev
AU - Aditya, Somak
AU - Yang, Yezhou
AU - Minervini, Pasquale
AU - Mannarswamy, Sandya
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/10/26
Y1 - 2021/10/26
N2 - Deep learning (DL) has made rapid progress in the 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 (or semi-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 that 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. Given the considerable promise of knowledge injection in overcoming the current challenges associated with purely data driven NN models, we propose a workshop on Knowledge Injection in Neural Networks (KINN) at CIKM 2021. The goal of this workshop is to focus the attention of the CIKM research community on addressing the open challenges in this emerging research area. Given that the CIKM research community has a rich mix of experts in structured knowledge representation, IR and DL, this workshop is intended to facilitate cross-disciplinary collaboration across the various CIKM research communities into building efficient and scalable KINN systems.
AB - Deep learning (DL) has made rapid progress in the 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 (or semi-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 that 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. Given the considerable promise of knowledge injection in overcoming the current challenges associated with purely data driven NN models, we propose a workshop on Knowledge Injection in Neural Networks (KINN) at CIKM 2021. The goal of this workshop is to focus the attention of the CIKM research community on addressing the open challenges in this emerging research area. Given that the CIKM research community has a rich mix of experts in structured knowledge representation, IR and DL, this workshop is intended to facilitate cross-disciplinary collaboration across the various CIKM research communities into building efficient and scalable KINN systems.
KW - deep learning
KW - information retrieval
KW - knowledge graphs
KW - knowledge representation and reasoning
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85119182796&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85119182796&partnerID=8YFLogxK
U2 - 10.1145/3459637.3482035
DO - 10.1145/3459637.3482035
M3 - Conference contribution
AN - SCOPUS:85119182796
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 4882
EP - 4883
BT - CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 30th ACM International Conference on Information and Knowledge Management, CIKM 2021
Y2 - 1 November 2021 through 5 November 2021
ER -