Multi-mode Natural Language Processing for Extracting Open Knowledge

As more and more open knowledge sources become available, it is interesting to explore opportunities of enhancing autonomous agents' capacities by utilizing the knowledge in these sources, instead of hand-coding knowledge for agents. A major challenge towards this goal lies in the translation of the open knowledge organized in multiple modes, unstructured or semi-structured, into the internal representations of agents. In this paper we present a set of multi-mode NLP techniques to formalize the open knowledge for autonomous agents. Two case studies are reported in which our robot KeJia, equipped with the multi-mode NLP techniques, succeeded in acquiring knowledge from the microwave oven manual and from the open knowledge database, OMICS, and solving problems that could not be solved before the robot acquired the knowledge.