Frame Instance Extraction and Clustering for Default Knowledge Building

Obtaining and representing common-sense knowledge, useful in a robotics scenario for planning and making inference about the robots’ surroundings, is a challenging problem, because such knowledge is typically found in unstructured repositories such as text corpora or small handmade resources. The work described in this paper presents a methodology for automatically creating a default knowledge base about real-world objects for the robotics domain. The proposed method relies on clustering frame instances extracted from natural language text as a way of distilling default knowledge. We collect and parse a natural language corpus using the Web as a source, then perform an agglomerative clustering of frame instances according to an appropriately defined similarity measure, and finally extract prototypical frame instances from each cluster and publish them in LOD-complaint format to promote reuse and interoperability.