Ontology learning: state of the art and open issues
Information Technology and Management
Acquisition of OWL DL Axioms from Lexical Resources
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
What science underlies natural language engineering?
Computational Linguistics
DL-Learner: Learning Concepts in Description Logics
The Journal of Machine Learning Research
Towards robust multi-tool tagging. An OWL/DL-based approach
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
OWL/DL formalization of the MULTEXT-East morphosyntactic specifications
LAW V '11 Proceedings of the 5th Linguistic Annotation Workshop
POWLA: modeling linguistic corpora in OWL/DL
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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Recent work in Ontology learning and Text mining has mainly focused on engineering methods to solve practical problem. In this thesis, we investigate methods that can substantially improve a wide range of existing approaches by minimizing the underlying problem: The Semantic Gap between formalized meaning and human cognition. We deploy OWL as a Meaning Representation Language and create a unified model, which combines existing NLP methods with Linguistic knowledge and aggregates disambiguated background knowledge from the Web of Data. The presented methodology here allows to study and evaluate the capabilities of such aggregated knowledge to improve the efficiency of methods in NLP and Ontology learning.