Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City
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 prese...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8536375/ |
| _version_ | 1818557074843369472 |
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| author | Jing Qiu Yuhan Chai Yan Liu Zhaoquan Gu Shudong Li Zhihong Tian |
| author_facet | Jing Qiu Yuhan Chai Yan Liu Zhaoquan Gu Shudong Li Zhihong Tian |
| author_sort | Jing Qiu |
| collection | DOAJ |
| description | 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 nontaxonomic 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. |
| first_indexed | 2024-12-13T23:55:16Z |
| format | Article |
| id | doaj.art-31f331552a47403fa918ab8bd1e9320d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T23:55:16Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-31f331552a47403fa918ab8bd1e9320d2022-12-21T23:26:34ZengIEEEIEEE Access2169-35362018-01-016748547486410.1109/ACCESS.2018.28814228536375Automatic Non-Taxonomic Relation Extraction from Big Data in Smart CityJing Qiu0Yuhan Chai1Yan Liu2Zhaoquan Gu3Shudong Li4Zhihong Tian5https://orcid.org/0000-0002-9409-5359Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, ChinaCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, ChinaCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, ChinaCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, ChinaCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, ChinaThe 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 nontaxonomic 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.https://ieeexplore.ieee.org/document/8536375/Non-taxonomic relationssemantic graphdependency relationssmart city |
| spellingShingle | Jing Qiu Yuhan Chai Yan Liu Zhaoquan Gu Shudong Li Zhihong Tian Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City IEEE Access Non-taxonomic relations semantic graph dependency relations smart city |
| title | Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City |
| title_full | Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City |
| title_fullStr | Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City |
| title_full_unstemmed | Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City |
| title_short | Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City |
| title_sort | automatic non taxonomic relation extraction from big data in smart city |
| topic | Non-taxonomic relations semantic graph dependency relations smart city |
| url | https://ieeexplore.ieee.org/document/8536375/ |
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