Analyzing the imagistic foundation of framality via prepositions

Natural language understanding is a vibrant research area in Artificial Intelligence that requires linguistic and commonsense knowledge. To unite both types of knowledge, FrameNet associates words with semantic frames, conceptual structures that describe a type of object, event or situation. Frames are interrelated and feature some image schematic foundations. However, the resource’s usefulness is limited by non-standard semantics. Framester, lying on a solid formal frame semantics, reengineers and links FrameNet to lexical and ontological resources to create one joint, powerful knowledge base. In this paper, we use Framester of FrameNet and of the Preposition Project (TPP) to systematically analyze the image-schematic foundation of frames via preposition senses. Framal knowledge is extracted from TPP, which contains senses for each English preposition, and frame interrelations are analyzed for the imagistic foundation of framality via preposition senses.