---
title: "Measuring and Improving the Effectiveness of Representations"
authors:
  - "Russell Greiner"
  - "Charles Elkan"
doi: ""
url: "https://ijcai.org/proceedings/1991/78"
pdf: "https://ijcai.org/proceedings/1991/0078.pdf"
venue: "Proceedings of the Twelfth International Joint Conference on Artificial Intelligence"
year: 1991
pages: "518-523"
track: "Knowledge Representation"
keywords:
---

# Measuring and Improving the Effectiveness of Representations

Russell Greiner, Charles Elkan

*Proceedings of the Twelfth International Joint Conference on Artificial Intelligence*, pages 518-523.

## Abstract

This report discusses what it means to claim that a representation is an effective encoding of knowledge. We first present dimensions of merit for evaluating representations, based on the view that usefulness is a behavioral property, and is necessarily relative to a specified task. We then provide methods (based on results from mathematical statistics) for reliably measuring effectiveness empirically, and hence for comparing different representations. We also discuss weak but guaranteed methods of improving inadequate representations. Our results are an application of the ideas of formal learning theory to concrete knowledge representation formalisms.