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.

Description

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AI Technical Summary

Benefits of technology

This patented technology proposes combining two different models - one for big data processing (KO) and another that enhances visual recognition capabilities. These combined networks are then used together like a single entity called Neural Network Model Coding Transferring Inference Engine or Deep Belief Layer Discriminator. By doing this they can improve image quality without adding unnecessary detail from other sources such as images themselves.

Problems solved by technology

This patented technical solution describes two types of existing solutions - client/server interaction frameworks and community knowledge base approaches. These techniques involve analyzing web data streams and providing suggestions about recommended items to clients or servers. While there exist different ways to suggest highly related tokens onto documents like emails, they lack flexibility due to their static nature. There exists a challenge called “hotspotting” where irrelevant concepts get added during analysis processes when discussing item attributes without being able to consider them again afterward. Additionally, it becomes challenging to efficiently assign tags to multiple parties because conventional ranking algorithms require extensive computational power.

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