用弱监督方法为24个产业匹配常见任务，补全常识知识库。
Matching Tasks with Industry Groups for Augmenting Commonsense Knowledge
- 训练神经模型学习任务与产业的亲和度，通过聚类筛选每产业前k个任务。
- 从新闻数据中提取2339条产业-任务三元组，准确率达0.86。
- 结果可直接补充现有常识知识库，适合知识图谱构建者使用。
常识知识库（KB）是广泛用于提升机器学习应用的专业知识来源。然而，即使对于大型知识库如ConceptNet，捕捉各产业领域的显式知识仍具挑战性。例如，ConceptNet中关于各类产业执行的通用任务样本极少。本文旨在填补这一空白，提出一种弱监督框架，用于增强常识知识库中产业群体（IG）所执行的任务信息。我们尝试通过训练神经模型学习任务与产业的亲和度，并结合聚类方法，为每个产业选取前k个最相关的任务。基于两个公开新闻数据集，我们为24个产业群体提取了总计2339条形式为⟨IG, is capable of, task⟩的三元组，准确率达到0.86。该结果验证了所提取任务-产业对的可靠性，可直接添加至现有知识库中。
原文摘要 · Abstract (English)
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.
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