The Semantic Gap of Formalized Meaning

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. 1 Problem Decades of research have been spent on answering the question “How can we teach machines to understand natural language?” and the unanimous response is: It is impossible (for obvious reasons). Any approach that indulges in formalizing the meaning of natural language face...

• Type: Journal
• Year: 2010
• Where: ESWS
• Authors: Sebastian Hellmann
• Keywords: ESWS 2010, Human Cognition, Internet Technology, Meaning Representation Language, Semantic Gap