Microblog semantic context retrieval system based on linked open data and graph-based theory
作者：
Highlights：
• We present a novel information retrieval system for context similarity retrieval in microblogging platforms.
• We present a method for extracting and linking entities to DBpedia concepts.
• We contextualize all matched concepts using graph centrality property by defining a new weighting factor.
• We present two algorithms which perform the semantic similarity by considering the weight of concepts and their related concepts.
• We use a real Twitter dataset to show the effectiveness of our system.
摘要
•We present a novel information retrieval system for context similarity retrieval in microblogging platforms.•We present a method for extracting and linking entities to DBpedia concepts.•We contextualize all matched concepts using graph centrality property by defining a new weighting factor.•We present two algorithms which perform the semantic similarity by considering the weight of concepts and their related concepts.•We use a real Twitter dataset to show the effectiveness of our system.
论文关键词：Information retrieval,Semantic similarity,Linked open data,DBpedia,Named entity linking,Graph centrality
论文评审过程：Received 16 March 2014, Revised 11 January 2016, Accepted 12 January 2016, Available online 29 January 2016, Version of Record 13 February 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.01.020
