Matching Tasks with Industry Groups for Augmenting Commonsense Knowledge

TL;DR

This paper introduces a weakly-supervised neural framework to augment commonsense knowledge bases with industry-specific tasks by matching tasks to industry groups, validated with high precision on news datasets.

Contribution

It presents a novel neural model for matching tasks with industry groups to enhance commonsense knowledge bases, addressing the scarcity of industry-specific task data.

Findings

Extracted 2339 task-Industry Group triples with 0.86 precision.

Validated the approach on two public news datasets.

Enhanced commonsense KBs with industry-specific tasks.

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.