Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-8936
Kiefer, C; Bernstein, A; Locher, A (2008). Adding data mining support to SPARQL via statistical relational learning methods. In: 5th European Semantic Web Conference (ESWC), Tenerife, Spain, 01 June 2008 - 05 June 2008, 478-492.
|PDF (Original publication) - Registered users only|
Exploiting the complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. We extend this idea to the Semantic Web by introducing our novel SPARQL-ML approach to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers.
We analyze our approach thoroughly conducting three sets of experiments on synthetic as well as real-world data sets. Our analytical results show that our approach can be used for any Semantic Web data set to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines shows that our approach is superior in terms of classification accuracy.
|Item Type:||Conference or Workshop Item (Paper), refereed, original work|
|Communities & Collections:||03 Faculty of Economics > Department of Informatics|
|DDC:||000 Computer science, knowledge & systems|
|Event End Date:||05 June 2008|
|Deposited On:||09 Jan 2009 09:30|
|Last Modified:||09 Jul 2012 05:31|
|Series Name:||Lecture Notes in Computer Science (LNCS)|
|Additional Information:||In this book, the proceedings of the 5th European Semantic Web Conference (ESWC 2008), Tenerife, Canary Islands, Spain, June 1-5, 2008 are published, at which this paper was presented. The original publication is available at www.springerlink.com|
Users (please log in): suggest update or correction for this item
Repository Staff Only: item control page