Linköping University Electronic Press

Title:	Logic and the Automatic Acquisition of Scientific Knowledge
Authors:	Ross Donald King, Andreas Karwath, Amanda Clare, and Luc Dehaspe
Series:	Linköping Electronic Articles in Computer and Information Science ISSN 1401-9841
Issue:	Vol. 5 (2000), No. 031
URL:	http://www.ep.liu.se/ea/cis/2000/031/

Abstract:	This paper is a manifesto. It argues that: Science is experiencing an unprecedented explosion in the amount of available data. Traditional data analysis methods cannot deal with this increased quantity of data. There is therefore an urgent need to automate the process of refining scientific data into scientific knowledge. Inductive logic programming (ILP) is the data analysis framework best suited for this task. We describe an example of using ILP to analyse a large and complex bioinformatic database which produced unexpected and interesting scientific results. We then point a possible way forward to integrating machine learning with scientific databases to form intelligent inductive databases.

Abstract:

This paper is a manifesto. It argues that:

Science is experiencing an unprecedented explosion in the amount of available data.
Traditional data analysis methods cannot deal with this increased quantity of data.
There is therefore an urgent need to automate the process of refining scientific data into scientific knowledge.
Inductive logic programming (ILP) is the data analysis framework best suited for this task.

We describe an example of using ILP to analyse a large and complex bioinformatic database which produced unexpected and interesting scientific results. We then point a possible way forward to integrating machine learning with scientific databases to form intelligent inductive databases.

Original publication 2000-12-21	Postscript Checksum Checksum (old) Information about recalculation of checksum

LiU Electronic Press