In particular, it focuses on classification operations that are usually performed in these languages through subsumption computations. The paper presents a simple language that encompasses and extends earlier m-ONE-based recognition frameworks. By relying on parsing algorithms, the paper shows that a significant class of recognition problems in this language can be performed in polynomial time.
A major difficulty in developing and maintaining very large knowledge bases originates from the variety of forms in which knowledge is made available to the KB builder. The objective of this research is to bring together two complementary knowledge representation schemes: term subsumption languages, which represent and reason about defining characteristics of concepts, and proximate reasoning models, which deal with uncertain knowledge and data in expert systems. Previous works in this area have primarily focused on probabilistic inheritance. In this paper, we address two other important issues regarding the integration of term subsumption-based systems and approximate reasoning models. First, we outline a general architecture that specifies the interactions between the deductive reasoner of a term subsumption system and an approximate reasoner. Second, we generalize the semantics of terminological language so that terminological knowledge can be used to make plausible inferences. The architecture, combined with the generalized semantics, forms the foundation of a synergistic tight integration of term subsumption systems and approximate reasoning models.
An intuitive four-valued semantics can be used to develop expressively powerful terminological logits which have tractable subsumption. If a fourvalued identity is also used, number restrictions can be added to the logic while retaining tracts bility. The subsumptions supported by the logic are a type of "structural" subsumption, where each structural component of one concept must have an analogue in the other concept.