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Matching Tasks with Industry Groups for Augmenting Commonsense Knowledge

arXiv 2505.07440 ↗First posted 12 May 2025cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 12 May 2025.

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References · 8

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,067
  3. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 308
  4. 2019Commonsense Knowledge Mining from Pretrained Modelscited by 189
  5. 2020Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answeringcited by 122
  6. 2018Improving Question Answering by Commonsense-Based Pre-Trainingcited by 66
  7. 2018A Survey on Open Information Extractioncited by 51
  8. 2019Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networkscited by 8

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