| Title |
Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City |
| ID_Doc |
45681 |
| Authors |
Qiu, J; Chai, YH; Liu, Y; Gu, ZQ; Li, SD; Tian, ZH |
| Title |
Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City |
| Year |
2018 |
| Published |
|
| DOI |
10.1109/ACCESS.2018.2881422 |
| 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. |
| Author Keywords |
Non-taxonomic relations; semantic graph; dependency relations; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000454392300001 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
https://doi.org/10.1109/access.2018.2881422
|