Knowledge-based question answering using the semantic embedding space
作者：
Highlights：
• We extract semantic links of words and logical properties from unstructured data.
• We jointly encode semantics of words and logical properties into an embedding space.
• Embedding space provides semantic similarities between word and logical properties.
• Questions and potential answers can be represented on the embedding space.
• Potential answers are ranked based on semantic similarities with a given question.
摘要
•We extract semantic links of words and logical properties from unstructured data.•We jointly encode semantics of words and logical properties into an embedding space.•Embedding space provides semantic similarities between word and logical properties.•Questions and potential answers can be represented on the embedding space.•Potential answers are ranked based on semantic similarities with a given question.
论文关键词：Question answering,Knowledge base,Embedding model,Neural networks,Labeled-LDA,Distributional semantics
论文评审过程：Received 30 March 2015, Revised 17 June 2015, Accepted 6 July 2015, Available online 13 August 2015, Version of Record 2 September 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.07.009
