Abstract
Concept embeddings offer a practical and efficient mechanism for injecting commonsense knowledge into downstream tasks. Their core purpose is often not to predict the commonsense properties of concepts themselves, but rather to identify commonalities, i.e.\ sets of concepts which share some property of interest. Such commonalities are the basis for inductive generalisation, hence high-quality concept embeddings can make learning easier and more robust. Unfortunately, standard embeddings primarily reflect basic taxonomic categories, making them unsuitable for finding commonalities that refer to more specific aspects (e.g.\ the colour of objects or the materials they are made of). In this paper, we address this limitation by explicitly modelling the different facets of interest when learning concept embeddings. We show that this leads to embeddings which capture a more diverse range of commonsense properties, and consistently improves results in downstream tasks such as ultra-fine entity typing and ontology completion.
Abstract (translated)
概念嵌入提供了一个实用且高效的机制，将常识知识注入到下游任务中。其核心目的通常不是预测概念本身的同义词，而是识别共同点，即具有共同兴趣的一组概念。这些共同点是归纳推理的基础，因此高质量的概念嵌入可以使学习更容易和更稳健。然而，标准嵌入主要反映了基本的分类范畴，因此它们不适合寻找指涉更具体方面（例如物体颜色或它们所使用的材料）的共同点。在本文中，我们通过明确建模学习概念嵌入时的不同兴趣方面，从而解决了这一局限。我们证明了这种方法导致具有更丰富多样性的概念嵌入，并且在下游任务（如超细实体类型和本体完成）中始终如一地提高结果。
URL
https://arxiv.org/abs/2403.16984
