IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Learning Logical Definitions from Relations
Machine Learning
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EWSL '91 Proceedings of the European Working Session on Machine Learning
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
The Journal of Machine Learning Research
Tutorial summary: Learning with dependencies between several response variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Evaluating Statistical Tests for Within-Network Classifiers of Relational Data
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Adding data mining support to SPARQL via statistical relational learning methods
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Data Mining and Knowledge Discovery
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Combining information extraction, deductive reasoning and machine learning for relation prediction
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Web Semantics: Science, Services and Agents on the World Wide Web
Scalable relation prediction exploiting both intrarelational correlation and contextual information
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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One of the main characteristics of Semantic Web (SW) data is that it is notoriously incomplete: in the same domain a great deal might be known for some entities and almost nothing might be known for others. A popular example is the well known friend-of-a-friend data set where some members document exhaustive private and social information whereas, for privacy concerns and other reasons, almost nothing is known for other members. Although deductive reasoning can be used to complement factual knowledge based on the ontological background, still a tremendous number of potentially true statements remain to be uncovered. The paper is focused on the prediction of potential relationships and attributes by exploiting regularities in the data using statistical relational learning algorithms. We argue that multivariate prediction approaches are most suitable for dealing with the resulting high-dimensional sparse data matrix. Within the statistical framework, the approach scales up to large domains and is able to deal with highly sparse relationship data. A major goal of the presented work is to formulate an inductive learning approach that can be used by people with little machine learning background. We present experimental results using a friend-of-a-friend data set.