Knowledge Representation for Interrogatives in E-HowNet

In order to train machines to ‘understand’ natural language, we proposed a universal concept representational mechanism called E-HowNet to encode lexical semantics. In this paper, we take interrogative constructions as examples, i.e. concepts or sentences for asking questions or making inquiries, to demonstrate the mechanisms of semantic representation and composition under the framework of E-HowNet. We classify the interrogative words into five types according to their semantic distinctions, and represent each type with fine-grained features and operators. The process of semantic composition and the difficulties of the representation, such as word sense disambiguation, will be addressed. Finally, we’ll show how machine discriminates two synonymous sentences with different syntactic structures and surface strings to prove that machine understanding is achievable.