Semantic Networks and the Generation of Context

This paper outlines a representation of knowledge based on semantic networks and organized in terms of semantic axes and "scenarios". A context mechanism for such nets is also discussed, along with an algorithm for integrating new information to a semantic net. Finally, an example is given to illustrate the representation and the context mechanism. 0. Introduction The representation of knowledge is one of the most important problems of AI today. Early approaches to AI attempted to simplify the problems tackled by proposing syntactic solutions to inherently semantic problems, thus understating the importance of the representation of knowledge. The history of AI has taught us, however, that there are no shortcuts to the process of "understanding". This process can only be accomplished by a system that possesses knowledge about the topic it is expected to understand. Moreover, the degree of understanding is proportional to the degree to which this knowledge can be used for recognition and prediction. The importance of the representation of knowledge stems from these premises. In this paper we outline a model of semantic representation that includes a semantic net(work) and a context mechanism, and illustrate its use with several examples. Our semantic net is organized around the concept of a "scenario", i.e. a semantic unit that may encompass more than just isolated propositions. This is in agreement with current trends in this area of research (Minsky [1], Schank [2], Abelson [3] etc.)* Scenarios as well as more elementary semantic entities are organized on the semantic net along two axes we have called the SUB and DEF axes. The SUB axis has been used elsewhere (Norman and Rumelhart [4], Martin I 5]) and a form of the DEF axis has also been used in [4l. Given a semantic net, we consider the problem of defining and using a context mechanism for referent determination, (semantic) disambiguation and inference. The problem of integrating new information to the semantic net is also treated through a process we call "graph fitting" and the relationship of context and graph fitting is discussed. Finally, a long example is presented to illustrate both the suitability of the representation and the use of the context mechanism. The knowledge we have represented and used for most of the examples is derived from the "educa-tional world", i.e. the world of students, courses and grades, applications to universities and decisions by universities regarding admission, etc. We consider the educational …