---
title: "Deductive Methods for Large Data Bases"
authors:
  - "Charles Kellogg"
  - "Philip Klahr"
  - "Larry Travis"
doi: ""
url: "https://ijcai.org/proceedings/1977/44"
pdf: "https://ijcai.org/proceedings/1977/0044.pdf"
venue: "Proceedings of the Fifth International Joint Conference on Artificial Intelligence"
year: 1977
pages: "203-208"
track: "Representation of Knowledge"
keywords:
---

# Deductive Methods for Large Data Bases

Charles Kellogg, Philip Klahr, Larry Travis

*Proceedings of the Fifth International Joint Conference on Artificial Intelligence*, pages 203-208.

## Abstract

The design and prototype implementation of a deductive processor for efficient extraction of implicit information from explicit data stored within a relational data-base system is described. General statements (premises or inference rules) as well as queries are expressed in a canonical form as implications. From user queries, the system constructs skeletal derivations (proof plans) through the use of a predicate connection graph, a pre-computed net structure representing possible deductive interactions among the general statements. The system incorporates techniques for rapid selection of small sets of relevant premises (by proof planning); development and elaboration of proof plans; proof plan verification; use of proof plans as a basis for determining data-base access strategies; and instantiation of plans (i.e., turning proof plans into proofs) with retrieved data-base values. Examples of the current capability of the system are illustrated.