Method and system for realizing cross-language knowledge space entity alignment based on link prediction

Abstract

The invention relates to a method and a system for realizing cross-language knowledge space entity alignment based on link prediction. According to the method, predicted entity pairs are generated through four steps of cross-language knowledge space triple fusion, knowledge space representation learning, prediction of new aligned entity pairs and addition of new training data through self-learning, a new entity pair predicted based on a link prediction method is designed for small amount of training corpus, cross-language knowledge space fusion is carried out, the efficiency of judging whetherthe entity pair of the cross-language knowledge space is the same entity or not in data fusion in the two knowledge spaces is improved on the basis, the design of the method model is light and labeling manpower is saved.

Description

technical field [0001] The present invention relates to the fields of artificial intelligence and machine learning, in particular to a method for realizing entity alignment in cross-language knowledge space based on link prediction. Background technique [0002] With the development of modern Internet technology, more and more information is accumulated on the Internet. When users retrieve information and consult e-commerce companies, they are not only satisfied with the results of traditional simple keyword search and matching, but also pursue more intelligent and personalized search and Q&A services. At present, a large number of Internet companies at home and abroad, such as Google, Amazon, Baidu, and Tencent, have established their own knowledge space systems, using knowledge space technology to provide customers with more intelligent services. Knowledge space technology can combine relevant knowledge in reality, No matter in the field of information retrieval or intell...

AI Technical Summary

Problems solved by technology

However, these knowledge spaces have certain limitations. For example, the English knowledge space does not include comprehensive information on non-English regions. Therefore, by integrating information from these different language knowledge spaces, a larger and more comprehensive multilingual knowledge space system is formed. , can provide customers with more comprehensive information services

However, the traditional word translation model-based fusion technology is limited by the translation accuracy of the word and the polysemy of the word itself. For example, the word Chaoyang can refer to Chaoyang District in Beijing or Chaoyang City in Liaoning Province.

The deep learning model learns the entity and relationship vectors of the knowledge space, and uses the already marked entity pairs to train the alignment model, which requires a large amount of information to be marked and consumes a lot of manpower

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