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
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Ontology, Metadata, and Semiotics
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Kernels and Distances for Structured Data
Machine Learning
ACM SIGKDD Explorations Newsletter
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Social Networks and the Semantic Web (Semantic Web and Beyond)
Social Networks and the Semantic Web (Semantic Web and Beyond)
DL-FOIL Concept Learning in Description Logics
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Non-parametric Statistical Learning Methods for Inductive Classifiers in Semantic Knowledge Bases
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Towards LarKC: A Platform for Web-Scale Reasoning
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Foundations and Trends in Databases
Tutorial summary: Learning with dependencies between several response variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning Expressive Ontologies
Proceedings of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge
Statistical Relational Learning with Formal Ontologies
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Multi-relational learning with Gaussian processes
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
DL-Learner: Learning Concepts in Description Logics
The Journal of Machine Learning Research
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
Ontology Learning and Population: Bridging the Gap between Text and Knowledge - Volume 167 Frontiers in Artificial Intelligence and Applications
The Journal of Machine Learning Research
Multivariate prediction for learning on the semantic web
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
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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Three common approaches for deriving or predicting instantiated relations are information extraction, deductive reasoning and machine learning. Information extraction uses subsymbolic unstructured sensory information, e.g. in form of texts or images, and extracts statements using various methods ranging from simple classifiers to the most sophisticated NLP approaches. Deductive reasoning is based on a symbolic representation and derives new statements from logical axioms. Finally, machine learning can both support information extraction by deriving symbolic representations from sensory data, e.g., via classification, and can support deductive reasoning by exploiting regularities in structured data. In this paper we combine all three methods to exploit the available information in a modular way, by which we mean that each approach, i.e., information extraction, deductive reasoning, machine learning, can be optimized independently to be combined in an overall system. We validate our model using data from the YAGO2 ontology, and from Linked Life Data and Bio2RDF, all of which are part of the Linked Open Data (LOD) cloud.