Object-Action Association Extraction from Knowledge Graphs

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is of utmost importance and a long-lasting goal in Artificial Intelligence (AI) research. Training systems with relevant data is a common approach; yet, it is not always feasible to find the data needed, especially since a big portion of this knowledge is commonsense. In this paper, we propose a novel method for extracting and evaluating relations between objects and actions from knowledge graphs, such as ConceptNet and WordNet. We present a complete methodology of locating, enriching, evaluating, cleaning and exposing knowledge from such resources, taking into consideration semantic similarity methods. One important aspect of our method is the flexibility in deciding how to deal with the noise that exists in the data. We compare our method with typical approaches found in the relevant literature, such as methods that exploit the topology or the semantic information in a knowledge graph, and embeddings. We test the performance of these methods on the Something-Something Dataset.