A Cognitive Reference Based Model for Learning Compositional Hierarchies with Whole-Composite Tags

A compositional hierarchy is the default organization of knowledge acquired for the purpose of specifying the design requirements of a service. Existing methods for learning compositional hierarchies from natural language text, interpret composition as an exclusively propositional form of part-whole relations. Nevertheless, the lexico-syntactic patterns used to identify the occurrence of part-whole relations fail to decode the experientially grounded information, which is very often embedded in various acts of natural language expression, e.g. construction and delivery. The basic idea is to take a situated view of conceptualization and model composition as the cognitive act of invoking one category to refer to another. Mutually interdependent set of categories are considered conceptually inseparable and assigned an independent level of abstraction in the hierarchy. Presence of such levels in the compositional hierarchy highlight the need to model these categories as a unified-whole wherein they can only be characterized in the context of the behavior of the set as a whole. We adopt an object-oriented representation approach that models categories as entities and relations as cognitive references inferred from syntactic dependencies. The resulting digraph is then analyzed for cyclic references, which are resolved by introducing an additional level of abstraction for each cycle.