Infusing autonomous artificial systems with knowledge about the physical world they inhabit is a critical and long-held aim for the Artificial Intelligence community. Training systems with relevant data is a typical approach; however, finding the data required is not always possible, especially when much of this knowledge is commonsense. In this paper, we present a comparison of topology-based and semantics-based methods for extracting information about object-action and object-state association relations from knowledge graphs, such as ConceptNet, WordNet, ATOMIC, YAGO, WebChild and DBpedia. Moreover, we propose a novel method for extracting information about object-action and object-state associations from knowledge graphs. Our method is composed of a set of techniques for locating, enriching, evaluating, cleaning and exposing knowledge from such resources, relying on semantic similarity methods. Some important aspects of our method are the flexibility in deciding how to deal with the noise that exists in the data, and the capability to determine the importance of a path through training, rather than through manual annotation.

中文翻译：

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从知识图中提取对象-动作和对象-状态关联

向自主人工智能系统注入有关其所居住的物理世界的知识是人工智能社区的一个重要且长期的目标。使用相关数据训练系统是一种典型的方法；然而，找到所需的数据并不总是可能的，特别是当这些知识大部分都是常识时。在本文中，我们比较了基于拓扑和基于语义的从知识图谱中提取对象-动作和对象-状态关联关系信息的方法，例如 ConceptNet、WordNet、ATOMIC、YAGO、WebChild 和 DBpedia。此外，我们提出了一种从知识图中提取有关对象动作和对象状态关联信息的新方法。我们的方法由一组依赖语义相似性方法从此类资源中定位、丰富、评估、清理和公开知识的技术组成。我们方法的一些重要方面是决定如何处理数据中存在的噪声的灵活性，以及​​通过训练而不是通过手动注释来确定路径重要性的能力。