Matching Tasks with Industry Groups for Augmenting Commonsense Knowledge
This work addresses a gap in commonsense knowledge for industry domains, though it is incremental as it builds on existing knowledge bases and datasets.
The paper tackles the challenge of augmenting commonsense knowledge bases with industry-specific tasks by developing a weakly-supervised framework to match tasks with industry groups, resulting in 2339 extracted triples with a precision of 0.86.
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.