Machine Learning
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Building rules on top of ontologies for the semantic web with inductive logic programming
Theory and Practice of Logic Programming
DL-FOIL Concept Learning in Description Logics
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Concept learning in description logics using refinement operators
Machine Learning
DL-Learner: Learning Concepts in Description Logics
The Journal of Machine Learning Research
A refinement operator based learning algorithm for the ALC description logic
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Inductive logic programming in databases: From datalog to $\mathcal{dl}+log}^{\neg\vee}$
Theory and Practice of Logic Programming
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
AutoSPARQL: let users query your knowledge base
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
Reasoning with noisy semantic data
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Creating knowledge out of interlinked data
Semantic Web
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Ontology learning - loosely, the process of knowledge extraction from diverse data sources - provides (semi-) automatic support for ontology construction. As the 'Web of Linked Data' vision of the Semantic Web is coming true, the 'explosion' of Linked Data provides more than sufficient data for ontology learning algorithms in terms of quantity. However, with respect to quality, notable issue of noises (e.g., partial or erroneous data) arises from Linked Data construction. Our doctoral researches will make theoretical and engineering contribution to ontology learning approaches for noisy Linked Data. More exactly, we will use the approach of Statistical Relational Learning (SRL) to develop learning algorithms for the underlying tasks. In particular, we will learn OWL axioms inductively from Linked Data under probabilistic setting, and analyze the noises in the Linked Data on the basis of the learned axioms. Finally, we will make the evaluation on proposed approaches with various experiments.