Research on Information Extraction of Technical Documents and Construction of Domain Knowledge Graph
With the rapid development of knowledge graph related technologies, domain knowledge graph has become a research hotspot in academia and industry. However, the domain knowledge graph for technical documents is not mature enough, and the semantic information implicit in unstructured technical documen...
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doaj-ac3d7c1ead5c45aaa972fac62335f5242021-03-30T03:48:29ZengIEEEIEEE Access2169-35362020-01-01816808716809810.1109/ACCESS.2020.30240709195862Research on Information Extraction of Technical Documents and Construction of Domain Knowledge GraphHuaxuan Zhao0https://orcid.org/0000-0002-9962-597XYueling Pan1Feng Yang2https://orcid.org/0000-0002-4854-6331School of Information Science and Engineering, Shandong Normal University, Jinan, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan, ChinaWith the rapid development of knowledge graph related technologies, domain knowledge graph has become a research hotspot in academia and industry. However, the domain knowledge graph for technical documents is not mature enough, and the semantic information implicit in unstructured technical documents has not been fully tapped. Combining the characteristics of technical documents, the paper proposes a TextCNN-based topic information extraction model and constructs a domain knowledge graph for technical documents. It uses the graph database Neo4j for knowledge storage and visualization. The information extraction model based on TextCNN can automatically extract the subject information of the document and the summary information such as title, ID, status, meeting, organization, etc. Experiments show that the model has high accuracy on the technical document dataset, which can effectively reduce the cost of manual annotation and data collation. At the same time, knowledge graph visualization can facilitate scientific researchers to search, track and update technical documents, which can show the evolution of technology more clearly.https://ieeexplore.ieee.org/document/9195862/Domain knowledge graphinformation extractiongraph databaseTextCNNNeo4jresource retrieval |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Huaxuan Zhao Yueling Pan Feng Yang |
spellingShingle |
Huaxuan Zhao Yueling Pan Feng Yang Research on Information Extraction of Technical Documents and Construction of Domain Knowledge Graph IEEE Access Domain knowledge graph information extraction graph database TextCNN Neo4j resource retrieval |
author_facet |
Huaxuan Zhao Yueling Pan Feng Yang |
author_sort |
Huaxuan Zhao |
title |
Research on Information Extraction of Technical Documents and Construction of Domain Knowledge Graph |
title_short |
Research on Information Extraction of Technical Documents and Construction of Domain Knowledge Graph |
title_full |
Research on Information Extraction of Technical Documents and Construction of Domain Knowledge Graph |
title_fullStr |
Research on Information Extraction of Technical Documents and Construction of Domain Knowledge Graph |
title_full_unstemmed |
Research on Information Extraction of Technical Documents and Construction of Domain Knowledge Graph |
title_sort |
research on information extraction of technical documents and construction of domain knowledge graph |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
With the rapid development of knowledge graph related technologies, domain knowledge graph has become a research hotspot in academia and industry. However, the domain knowledge graph for technical documents is not mature enough, and the semantic information implicit in unstructured technical documents has not been fully tapped. Combining the characteristics of technical documents, the paper proposes a TextCNN-based topic information extraction model and constructs a domain knowledge graph for technical documents. It uses the graph database Neo4j for knowledge storage and visualization. The information extraction model based on TextCNN can automatically extract the subject information of the document and the summary information such as title, ID, status, meeting, organization, etc. Experiments show that the model has high accuracy on the technical document dataset, which can effectively reduce the cost of manual annotation and data collation. At the same time, knowledge graph visualization can facilitate scientific researchers to search, track and update technical documents, which can show the evolution of technology more clearly. |
topic |
Domain knowledge graph information extraction graph database TextCNN Neo4j resource retrieval |
url |
https://ieeexplore.ieee.org/document/9195862/ |
work_keys_str_mv |
AT huaxuanzhao researchoninformationextractionoftechnicaldocumentsandconstructionofdomainknowledgegraph AT yuelingpan researchoninformationextractionoftechnicaldocumentsandconstructionofdomainknowledgegraph AT fengyang researchoninformationextractionoftechnicaldocumentsandconstructionofdomainknowledgegraph |
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1724182875767570432 |