---
title: "Towards Efficient Metaquerying"
authors:
  - "Rachel Ben-Eliyahu-Zohary"
  - "Ehud Gudes"
doi: ""
url: "https://ijcai.org/proceedings/1999/114"
pdf: "https://ijcai.org/proceedings/1999/0114.pdf"
venue: "Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence"
year: 1999
pages: "800-804"
track: "Machine Learning"
keywords:
---

# Towards Efficient Metaquerying

Rachel Ben-Eliyahu-Zohary, Ehud Gudes

*Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence*, pages 800-804.

## Abstract

Metaquery (also known as metapattern) is a datamining tool useful for learning rules involving more than one relation in the database. A metaquery is a template, or a second-order proposition in a language £, that describes the type of pattern to be discovered. This tool has already been successfully applied to several real-world applications. In this paper we advance the state of the art in metaqueries research in several ways. First, we analyze the related computational problem and classify it as NP-hard, with a tractable subset that is quite immediately evident. Second, we argue that the notion of support for metaqueries, where support is intuitively some indication to the relevance of the rules to be discovered, is not adequately defined in the literature, and propose our own definition. Third, we propose some efficient algorithms for computing support and present preliminary experimental results that indicate that our algorithms are indeed quite useful.