Abstract: The present disclosure discloses a self-learning method based on a universal self-learning system. The self-learning method includes: performing inference based on a knowledge in a knowledge base to generate an inference knowledge; performing inference on the basis of acquired nth inference knowledge to obtain (n+1)th inference knowledge, and performing several rounds of inference continuously to form an inference chain; and forming an inference loop if the knowledge inferred according to the inference chain already exists in the inference chain. Through the self-learning method based on the universal self-learning system, the inference loop and an inference cycle about knowledge can be constructed in the knowledge base, and the self-consistency and activity of knowledge in the knowledge base can be maintained through the dynamic and sustainable inference cycle with high activity.

Abstract: A method for processing natural language includes generating a first layer of a multi-layer knowledge network including a plurality of word nodes arranged to represent a word or an entity name, generating a second layer of the multi-layer knowledge network with a natural language dataset, the second layer including one or more instance nodes arranged to represent a word or an entity of the natural language dataset, each of the instance nodes being linked by one or more semantic or syntactic relations to form one or more sub-graphs, and, referencing the first layer of the multi-layer knowledge network with the second layer of the multi-layer knowledge network by establishing a reference between each of the word nodes and each of the instance nodes when the word or the entity name represented by each word node is associated with the word or the entity represented by the instance node.

Abstract: A method for processing natural language includes generating a first layer of a multi-layer knowledge network including a plurality of word nodes arranged to represent a word or an entity name, generating a second layer of the multi-layer knowledge network with a natural language dataset, the second layer including one or more instance nodes arranged to represent a word or an entity of the natural language dataset, each of the instance nodes being linked by one or more semantic or syntactic relations to form one or more sub-graphs, and, referencing the first layer of the multi-layer knowledge network with the second layer of the multi-layer knowledge network by establishing a reference between each of the word nodes and each of the instance nodes when the word or the entity name represented by each word node is associated with the word or the entity represented by the instance node.