Introduction to ROSS
نویسنده
چکیده
Representation is a cross-discipline topic that includes knowledge representation from the field of artificial intelligence, meaning representation from the field of natural language understanding, and structured information from information processing. The field of logic is replete with methods for representation and reasoning. In the areas of AI knowledge representation and reasoning, leading approaches include first order logic, description logic, lambda calculus, semantic networks, framebased approaches and many others. Meaning representation techniques from the field of natural language understanding include the above in addition to approaches like conceptual dependency. ROSS (“Representation, Ontology, Structure, Star”) is introduced as a new method for representation that emphasizes representational constructs for physical structure. The ROSS approach starts with a normal form that is self-consciously less-expressive than logic, and builds from the bottom-up to achieve the expressiveness that is needed for a domain. Information that uses the normal form exists within an analogical frame of reference. What is achieved is a greater degree of structure in the representations that use ROSS when compared with representations that are typically created using formal approaches such as FOL. The ROSS normal form involves a set of modeling guidelines and underlying ontological commitments that involve naive or intuitionistic
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