Matching Tasks with Industry Groups for Augmenting Commonsense Knowledge
Rituraj Singh, Sachin Pawar, Girish Palshikar
TL;DR
This work tackles the scarcity of industry-specific tasks in commonsense knowledge bases by proposing a weakly supervised framework to extract tasks from business news and map them to 24 industry groups. It integrates task extraction, canonicalization, unsupervised task–IG signals, and a self-supervised affinity model with a margin ranking objective to produce IG-tagged task triples, achieving $2339$ new triples with precision $0.86$ and outperforming baselines. Post-processing via community-detection yields representative task concepts to robustly augment KBs like ConceptNet. The approach demonstrates a practical pathway to enhance downstream NLP applications with domain-specific commonsense, enabling improved reasoning about industry contexts and actions.
Abstract
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 $\langle IG, is~capable~of, task \rangle$ 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.