Speeding Up Inferences in Large Knowledge Bases

From Tetherless World Wiki

Jump to: navigation, search

Citation: Alon Y. Halevy and Richard Fikes and Yehoshua Sagiv. (1993) Speeding Up Inferences in Large Knowledge Bases. In KSL-93-66, November,1993.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Alon Y. Halevy and Richard Fikes and Yehoshua Sagiv
title Speeding Up Inferences in Large Knowledge Bases
number KSL-93-66
institution Knowledge Systems, AI Laboratory
year 1993
month November
Bibtex more
Access Paper
abstract Speeding up inferences made from large knowledge bases is a key to scaling up AI systems. The query-tree is a powerful tool for analyzing KBs containing Horn rules which takes into consideration the semantics of interpreted predicates that appear in the rules (e.g., order and sort predicates). It is a finite structure that encodes all derivations of a given set of queries and tells us which rules and ground facts can be used in deriving answers to the queries and which can be ignored. This paper investigates experimentally the impact of several methods of employing the query-tree on speeding up inference. Speedups are obtained by creating specialized indices that point only to relevant facts in the KB and by following only sequences of rule applications that are allowed by the query-tree. The experiments show that significant speedups (often orders of magnitude) are obtained by employing the query-tree. Moreover, we show that the speedups improve as the size of the KB grows, indication that the methods will scale up to large KBs.

KSL Technical Report ID: KSL-93-66
Facts about Speeding Up Inferences in Large Knowledge BasesRDF feed
Abstract Speeding up inferences made from large kno … Speeding up inferences made from large knowledge bases is a key to scaling up AI systems. The query-tree is a powerful tool for analyzing KBs containing Horn rules which takes into consideration the semantics of interpreted predicates that appear in the rules (e.g., order and sort predicates). It is a finite structure that encodes all derivations of a given set of queries and tells us which rules and ground facts can be used in deriving answers to the queries and which can be ignored. This paper investigates experimentally the impact of several methods of employing the query-tree on speeding up inference. Speedups are obtained by creating specialized indices that point only to relevant facts in the KB and by following only sequences of rule applications that are allowed by the query-tree. The experiments show that significant speedups (often orders of magnitude) are obtained by employing the query-tree. Moreover, we show that the speedups improve as the size of the KB grows, indication that the methods will scale up to large KBs. at the methods will scale up to large KBs.
Author Alon Y. Halevy and Richard Fikes and Yehoshua Sagiv  +
Bibtype techreport  +
Has author Alon Y. Halevy and Richard Fikes and Yehoshua Sagiv  +
Has identifier KSL-93-66  +
Has publishing details November,1993  +
Has title Speeding Up Inferences in Large Knowledge Bases  +
Has where published KSL-93-66  +
Has year 1993  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-93-66  +
Month November  +
Number KSL-93-66  +
Process note NO  +
Title Speeding Up Inferences in Large Knowledge Bases  +
Year 1993  +
Retrieved from "http://tw.rpi.edu/wiki/Speeding_Up_Inferences_in_Large_Knowledge_Bases"