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Article . 2020
Data sources: DOAJ
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Constructing and analyzing intention knowledge graphs

Authors: Cheng CHEN; Yueguo CHEN; Chen LIU; Xiaotong LYU; Xiaoyong DU;

Constructing and analyzing intention knowledge graphs

Abstract

It is very difficult to evaluate the effects of government governance.Without a good evaluation method and evaluation system,the effects of government governance cannot be guaranteed.Understanding the intention of web users in the topic of government governance from the perspective of natural language question-and-answering was proposed.By constructing a knowledge graph of intentions,equivalent questions and intentions were associated.The definition,construction framework and usage examples in government governance were illustrated,showing that knowledge graph of intentions is an effective way to evaluate the effects of government governance.In the context of government governance,by using the knowledge graphs of intentions,the intention fields between different governance subjects under the same governance topic were analyzed and compared,the effects of specific governance subjects on specific governance topics were analyzed,and the issues remained in government governance were found.

Keywords

Electronic computers. Computer science, QA75.5-76.95, intention understanding;knowledge graph;natural language question answering;entity recognition

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold