Paper Info
Title
Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City.
Abstract
The explosive data growth in smart city is making domain big data a hot topic for knowledge extraction. Non-taxonomic relations refer to any relations between concept pairs except the is-a relation, which is an important part of Knowledge Graph. In this paper, toward big data in smart city, we present a multi-phase correlation search framework to automatically extract non-taxonomic relations from domain documents. Different kinds of semantic information are used to improve the performance of the system. First, inspired by the works of network representation; we propose a Semantic Graph-Based method to combine structure information of semantic graph and context information of terms together for non-taxonomic relationships identification. Second, different semantic types of verb sets are extracted based on the dependency syntactic information, which are ranked to act as non-taxonomic relationship labels. Extensive experiments demonstrate the efficiency of the proposed framework. The F1 value reaches 81.4% for identification of non-taxonomic relationships. The total precision of the non-taxonomic relationship labels extraction is 73.4%, and 87.8% non-taxonomic relations can be provided with "good" labels. We hope this article can provide a useful way for domain big data knowledge extraction in smart city.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2881422
IEEE ACCESS
Keywords
Field
DocType
Non-taxonomic relations,semantic graph,dependency relations,smart city
Ontology (information science),Ranking,Information retrieval,Computer science,Smart city,Knowledge extraction,Big data,Syntax,Semantics,Relationship extraction,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jing Qiu16014.01
Yuhan Chai200.34
Yan Liu324173.08
Zhaoquan Gu412528.01
Shudong Li54712.98
Zhi-Hong Tian631252.75