Hypernym and Spatial-Taxon Hierarchy. A Cognitive Informatics & Fuzzy Logic Approach to Combining Linguistic and Image Taxonomies

Images and language convey meaning that depend on the viewpoints and contextual background of those perceiving them. Taxonomies help order meaning such that information granules, image elements and words, make sense in relation to one another and to their mutual global context. In linguistics, hypernyms cover semantically broader context then their subordinate hyponym. In images, superordinate spatial-taxons (object groups or foreground) cover more abstract regions then their child subordinate spatial-taxons (objects or salient object parts). In this paper I use fuzzy granularization and fuzzy perceptualization as proposed by Zadeh 2002 to explore image annotation by using Zadeh's Restriction-centered Theory of Truth and Meaning as proposed in 2013. The approach uses human annotated image data, search engine queries and data collected from WordNet (A Lexical Database for English maintained by Princeton University). I discuss implications for Shannon, Integrated, and Zadeh Information Theory.