Tag recommendation method of online question and answer platform based on knowledge graph and tag association

Abstract

The invention discloses a tag recommendation method of an online question and answer platform based on knowledge graph and tag association, and relates to the technical field of artificial intelligence, natural language processing and recommendation systems. The invention aims to solve the problem that the existing tag recommendation method is not suitable for the scene of a question and answer platform, so that the recommendation effect is poor. The method comprises the following specific steps: inputting a question text and an external knowledge graph of an online question and answer platform into a trained KOCIN model to obtain a recommended tag; the KOCIN model comprises a knowledge integration layer, a sequence coding layer and an association capture layer; the knowledge integration layer is used for extracting a knowledge triple from the question text qi and an external knowledge graph, and integrating the knowledge triple into the question text qi to generate a Qtree; the sequence coding layer is used for converting the Qtree into dense vectorization representation of the Qtree so as to obtain a predicted problem text original label; and the association capture layer is used for obtaining a recommendation tag of the problem text according to the predicted original tag of the problem text. The method is used for obtaining the recommendation tag of the question-answering platform.

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