Glenn R. Hofford

Glenn R. Hofford

The ROSS method is a new approach in the area of knowledge representation that is useful for many artificial intelligence and natural language understanding representation and reasoning tasks. (ROSS stands for "Representation", "Ontology", "Structure", "Star" language). ROSS is a physical symbol-based representational scheme. ROSS provides a complex model for the declarative representation of physical structure and for the representation of processes and causality. From the metaphysical perspective, the ROSS view of external reality involves a 4D model, wherein discrete single-time-point unit-sized locations with states are the basis for all objects, processes and aspects that can be modeled. ROSS includes a language called "Star" for the specification of ontology classes. The ROSS method also includes a formal scheme called the "instance model". Instance models are used in the area of natural language meaning representation to represent situations. This document is an in-depth specification of the ROSS method.

Glenn R. Hofford

ROSS ("Representation, Ontology, Structure, Star") is introduced as a new method for knowledge representation that emphasizes representational constructs for physical structure. The ROSS representational scheme includes a language called "Star" for the specification of ontology classes. The ROSS method also includes a formal scheme called the "instance model". Instance models are used in the area of natural language meaning representation to represent situations. This paper provides both the rationale and the philosophical background for the ROSS method.

Glenn R. Hofford

A new natural language understanding method for disambiguation of difficult pronouns is described. Difficult pronouns are those pronouns for which a level of world or domain knowledge is needed in order to perform anaphoral or other types of resolution. Resolution of difficult pronouns may in some cases require a prior step involving the application of inference to a situation that is represented by the natural language text. A general method is described: it performs entity resolution and pronoun resolution. An extension to the general pronoun resolution method performs inference as an embedded commonsense reasoning method. The general method and the embedded method utilize features of the ROSS representational scheme; in particular the methods use ROSS ontology classes and the ROSS situation model. The overall method is a working solution that solves the following Winograd schemas: a) trophy and suitcase, b) person lifts person, c) person pays detective, and d) councilmen and demonstrators.