The schema.org initiative was designed to introduce machine readable metadata into the World Wide Web. This paper investigates conceptual biases in the schema through a mapping exercise between schema.org types and WordNet synsets. We create a mapping ontology which establishes the relationship between schema metadata types and the corresponding everyday concepts. This in turn can be used to enhance metadata annotation to include a more complete description of knowledge on the Web of data.

AutoExtend is a method for learning unambiguous vector embeddings for word senses. We visualise these word embeddings with t-SNE, which further compresses the vectors to the x,y plane. We show that the t-SNE co-ordinates can be used to reveal interesting semantic relations between word senses, and propose a new method that uses the simple x,y coordinates to compute semantic similarity. This can be used to propose new links and alterations to existing ones in WordNet. We plan to add this approach to the existing toolbox of methods in an attempt to understand learned semantic relations in word embeddings.