Math-KG: Construction and Applications of Mathematical Knowledge Graph
This work addresses integration challenges for online mathematics learners, but it is incremental as it applies existing pipeline methods to a new domain.
The authors tackled the problem of information overload and knowledge trek in online mathematics education by constructing Math-KG, a mathematical knowledge graph built from Baidu Baike and Wikipedia using NLP, and validated it through applications like fault analysis and semantic search.
Recently, the explosion of online education platforms makes a success in encouraging us to easily access online education resources. However, most of them ignore the integration of massive unstructured information, which inevitably brings the problem of \textit{information overload} and \textit{knowledge trek}. In this paper, we proposed a mathematical knowledge graph named Math-KG, which automatically constructed by the pipeline method with the natural language processing technology to integrate the resources of the mathematics. It is built from the corpora of Baidu Baike, Wikipedia. We implement a simple application system to validate the proposed Math-KG can make contributions on a series of scenes, including faults analysis and semantic search. The system is publicly available at GitHub \footnote{\url{https://github.com/wjn1996/Mathematical-Knowledge-Entity-Recognition}.}.