A Hybrid Approach for Learning SNOMED CT Definitions from Text

In recent years approaches for extracting formal definitions from natural language have been developed. These approaches typically use methods from natural language processing, such as relation extraction or syntax parsing. They make only limited use of description logic reasoning. We propose a hybrid approach combining natural language processing methods and description logic reasoning. In a first step description candidates are obtained using a natural language processing method. Description logic reasoning is used in a post-processing step to select good quality candidate definitions. We identify the corresponding reasoning problem and examine its complexity.