Distracting users as per their knowledge: Combining linked open data and word embeddings to enhance history learning

作者：

Highlights：

• Fight information overload by semantics-driven filtering and knowledge generation.

• A new way of learning History based on natural language processing and Linked Data.

• Customized collecting of texts to train neural networks and check user knowledge.

• Find and assess relevant interconnections among semantic entities by word vectors.

摘要

•Fight information overload by semantics-driven filtering and knowledge generation.•A new way of learning History based on natural language processing and Linked Data.•Customized collecting of texts to train neural networks and check user knowledge.•Find and assess relevant interconnections among semantic entities by word vectors.

论文关键词：Linked open data,Semantics,DBpedia SpotLight,YAGO,Word2Vec

论文评审过程：Received 16 July 2019, Revised 21 October 2019, Accepted 21 October 2019, Available online 22 October 2019, Version of Record 31 October 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.113051