Abstract :
The focus of this paper is showing how linguistic information can be modeled in an ontological engineering environment for knowledge management and acquisition, and on this basis made accessible for hierarchical and axiomatic processing. The simplicity of relational network notation models stratal linguistic information solely with reference to sets of interconnecting nodes. Axioms can be effortlessly declared upon the simplicity of the notation such that the knowledge base can be easily extended with the power of inference. Fruitful new knowledge can thus be acquired through axiomatic inference in terms of uncovering latent links between concepts and/or instances in the knowledge base. With this model, various linguistic resources, WordNet and FrameNet originally encoding different domains of linguistic knowledge, are now capable of interfacing with each other, retrieving and generating underlying linguistic information, serving as a more comprehensive NLP tool
Keywords :
computational linguistics; knowledge acquisition; knowledge management; natural language processing; ontologies (artificial intelligence); text analysis; FrameNet; NLP tool; WordNet; axiomatic inference; axiomatic processing; hierarchical processing; knowledge acquisition; knowledge base; knowledge engineering; knowledge management; linguistic knowledge; linguistic resources; ontological engineering; relational network notation model; stratal linguistic information; Data mining; Databases; Encoding; Information retrieval; Intelligent networks; Knowledge engineering; Knowledge management; Natural languages; Ontologies; Recurrent neural networks;
