Knowledge Retrieval as Specialized Inference

摘要

Artificial intelligence reasoning systems commonly contain a large corpus of declarative knowledge, called a knowledge base (KB), and provide facilities with which the system's components can retrieve this knowledge. Formal specifications that capture certain informal intuitions about retrieval are developed, studied, and implemented by retrieval algorithms. Consistent with the necessity for fast retrieval is the guiding intuition that a retriever is, at least in simple cases, a pattern matcher, though in more complex cases it may perform selected inferences such as property inheritance. The entire process of retrieval can be reviewed as a form of inference and hence the KB as a representation, not merely a data structure. A retriever makes a limited attempt to prove that a queried sentence is a logical consequence of the KB. When constrained by the no-chaining-restriction, inference becomes indistinguishable from pattern matching. Imagining the KB divided into quanta, a retriever that respects this restriction cannot combine two quanta in order to derive a third. The techniques of model theory will build non-procedural specifications of retrievability relations, which determine what sentences are retrievable from what KBs. Model-theoretic specifications are presented for four retrievers, each extending the capabilities of the previous one. Each is accompanied by a rigorous investigation into its properties and a presentation of an efficient, terminating algorithm that provably meets the specification.