A BERT-Based Approach for Extracting Prerequisite Relations among Wikipedia Concepts

Youheng Bai,Yan Zhang,Yuanyuan Lou,Kai Sun,Kui Xiao,Chunlai Chai

doi:10.1155/2021/3510402

Abstract

Concept prerequisite relation prediction is a common task in the field of knowledge discovery. Concept prerequisite relations can be used to rank learning resources and help learners plan their learning paths. As the largest Internet encyclopedia, Wikipedia is composed of many articles edited in multiple languages. Basic knowledge concepts in a variety of subjects can be found on Wikipedia. Although there are many knowledge concepts in each field, the prerequisite relations between them are not clear. When we browse pages in an area on Wikipedia, we do not know which page to start. In this paper, we propose a BERT-based Wikipedia concept prerequisite relation prediction model. First, we created two types of concept pair features, one is based on BERT sentence embedding and the other is based on the attributes of Wikipedia articles. Then, we use these two types of concept pair features to predict the prerequisite relations between two concepts. Experimental results show that our proposed method performs better than state-of-the-art methods for English and Chinese datasets.

Highlights

In recent years, the emergence of online learning platforms and e-learning resources has injected new impetus into people’s learning
As everyone has a different knowledge background, the challenge faced by online learners is usually how to choose learning resources and how to rank them
We propose a method for extracting concept prerequisite relations from Wikipedia using BERT

Summary

Introduction

The emergence of online learning platforms and e-learning resources has injected new impetus into people’s learning. We propose a method for extracting concept prerequisite relations from Wikipedia using BERT. E two BERT sentence embeddings of the concept pair are used as inputs to the neural network, which is passed to the two 32-unit LSTMs. LSTM can be used to create some feature information not included in automatic feature design and achieve deeper concept feature extraction. Wikipedia’s concepts are described through articles with corresponding titles, and the articles contain links, categories, and redirects (synonyms) in the content Researchers can use this information to extract feature information from concept pairs. By manually extracting the structural features of concept pairs from Wikipedia articles, we can analyze the prerequisite relations between the two concepts. (5) OA, OB, and V(A,B) are concatenated (OA ⊕ OB⊕V(A,B)) as 78-dimensional features and input to a fully connected layer with a sigmoid activation function to realize concept prerequisite relation prediction

Experiments

Findings

Conclusion and Future Work