Commonsense knowledge bases (KB) are a source of specialized knowledge that is widely used to improve machine learning applications. However, even for a large KB such as ConceptNet, capturing explicit knowledge from each industry domain is challenging. For example, only a few samples of general {\em tasks} performed by various industries are available in ConceptNet. Here, a task is a well-defined knowledge-based volitional action to achieve a particular goal. In this paper, we aim to fill this gap and present a weakly-supervised framework to augment commonsense KB with tasks carried out by various industry groups (IG). We attempt to {\em match} each task with one or more suitable IGs by training a neural model to learn task-IG affinity and apply clustering to select the top-k tasks per IG. We extract a total of 2339 triples of the form from two publicly available news datasets for 24 IGs with the precision of 0.86. This validates the reliability of the extracted task-IG pairs that can be directly added to existing KBs.
View on arXiv@article{singh2025_2505.07440,
title={ Matching Tasks with Industry Groups for Augmenting Commonsense Knowledge },
author={ Rituraj Singh and Sachin Pawar and Girish Palshikar },
journal={arXiv preprint arXiv:2505.07440},
year={ 2025 }
}