Robust Classification for Imprecise Environments
Machine Learning
Algorithms on Trees and Graphs
Algorithms on Trees and Graphs
Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Kernel methods for mining instance data in ontologies
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
The fundamentals of iSPARQL: a virtual triple approach for similarity-based semantic web tasks
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Semantics and complexity of SPARQL
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Statistical Relational Learning with Formal Ontologies
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Query Results Clustering by Extending SPARQL with CLUSTER BY
OTM '09 Proceedings of the Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009
Semantic web enabled software analysis
Web Semantics: Science, Services and Agents on the World Wide Web
Mining association rules from semantic web data
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Relational kernel machines for learning from graph-structured RDF data
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
Multivariate prediction for learning on the semantic web
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Finding association rules in semantic web data
Knowledge-Based Systems
Application and evaluation of inductive reasoning methods for the semantic web and software analysis
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
Learning relational bayesian classifiers from RDF data
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Factorizing YAGO: scalable machine learning for linked data
Proceedings of the 21st international conference on World Wide Web
Data Mining and Knowledge Discovery
Learning probabilistic Description logic concepts: under different Assumptions on missing knowledge
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Unsupervised generation of data mining features from linked open data
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
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
Generating possible interpretations for statistics from linked open data
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
First steps towards a context aware ontology-driven reporting system
Proceedings of the 8th International Conference on Semantic Systems
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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.