NVL - a knowledge representation language based on semantic networks
Taxonomic hierarchical networks or semantic networks have been widely used in representing knowledge in AI applications. Semantic networks have been the preferred form of representation in AI, rather than predicate logic because of the need to represent complex structured knowledge. However, the formal semantics of these networks has not been dealt with adequately in the literature. In this thesis, semantic networks are described by means of a formal relational logic called NVL. The characteristic features of NVL are limitor lists and binary predicates. Limitor lists are similar to restricted quantifiers but are more expressive. Several special binary relations are used to express the key ideas of semantic networks. NVL is based on the principles of semantic networks and taxonomic reasoning. The unification and inference mechanisms of NVL have considerable inherent parallelism which makes the language suitable for parallel implementation. The current opinion in AI is that semantic networks represent a subset of first order logic. Rather than modify predicate logic by adding features of semantic networks, the approach has been to devise a new form of logic by considering the basic principles and epistemological primitives of semantic networks such as properties, class concepts, relations, and inheritance. The syntax and semantics of NVL are first presented. Rules in the knowledge based are represented by V relation which also plays an important role in deriving inferences. The (mathematical) correctness of NVL is proved and concepts of unification of lists and inference in NVL are introduced. Parallel algorithms for unification and inference are developed.
- Research Organization:
- Nebraska Univ., Lincoln, NE (USA)
- OSTI ID:
- 5692557
- Country of Publication:
- United States
- Language:
- English
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