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In order to support the domain modeling process in model-based software development, we automatically create large networks of semantically related terms from natural language. Using part-of-speech tagging, lexical patterns and co-occurrence analysis, and several semantic improvement algorithms, we construct SemNet, a network of approximately 2.7 million single and multi-word terms and 37 million relations denoting the degree of semantic relatedness. This paper gives a comprehensive description of the construction of SemNet, provides examples of the analysis process and compares it to other knowledge bases. We demonstrate the application of the network within the Eclipse/Ecore modeling tools by adding semantically enhanced class name autocompletion and other semantic support facilities like concept similarity.

Automated Construction of a Large Semantic Network of Related Terms for Domain-Specific Modeling