Using background knowledge for graph based learning: a case study in chemoinformatics
2007 (English)In: IMECS 2007: International Multiconference of Engineers and Computer Scientists, Vols I and II, HONG KONG: INT ASSOC ENGINEERS-IAENG , 2007, 153-157 p.Conference paper (Refereed)
Incorporating background knowledge in the learning process is proven beneficial for numerous applications of logic based learning methods. Yet the effect of background knowledge in graph based learning is not systematically explored. This paper describes and demonstrates the first step in this direction and elaborates on how additional relevant background knowledge could be used to improve the predictive performance of a graph learner. A case study in chemoinformatics is undertaken in this regard in which various types of background knowledge are encoded in graphs that are given as input to a graph learner. It is shown that the type of background knowledge encoded indeed has an effect on the predictive performance, and it is concluded that encoding appropriate background knowledge can be more important than the choice of the graph learning algorithm.
Place, publisher, year, edition, pages
HONG KONG: INT ASSOC ENGINEERS-IAENG , 2007. 153-157 p.
, Lecture Notes in Engineering and Computer Science, ISSN 2078-0958
graph propositionalization, machine learning, structured data
Electrical Engineering, Electronic Engineering, Information Engineering Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-39392ISI: 000246800600028ScopusID: 2-s2.0-84888342422ISBN: 978-988-98671-4-0OAI: oai:DiVA.org:kth-39392DiVA: diva2:440689
International Multiconference of Engineers and Computer Scientists. Kowloon, PEOPLES R CHINA. MAR 21-23, 2007
QC 201109132011-09-132011-09-092014-10-30Bibliographically approved