检索结果-南通市图书馆

第四届全国信息检索与内容安全学术会议

作者： Li HUANG~(1,2) Wei FANG~(1,2) Hongpin CHEN~(1,2) Pengpeng ZHAO~(1,2) Zhiming CUI~(1,2↑) ~1 Jiangsu Key Laboratory of Computer Information Processing Technology,Soochow University,215006 ~2 Institute of Intelligent Information Processing and Application,Soochow University,Jiangsu Suzhou,215006

Deep Web sources classification is one of key steps in Large-scale data integration,and structured query interface of Deep Web serves as a valid approach for research on online databases organization by *** paper proposes a novel classification strategy on Deep Web *** features and co-occurrence features could be obtained by semantic inference from visible features of query interface,which enrich semantic features in *** of semantic inference and machine learning method effectively can further improve traditional classification performance on Deep Web *** results on real web datasets show that our method is improved with higher classification accuracy.

关键词： Deep Web semantic inference Decision Feature Co-occurrence.

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Detecting Vicious Cycles in Urban Problem Knowledge Graph using inference Rules

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Data Intelligence 2022年第1期4卷 88-111页

作者： Shusaku Egami Takahiro Kawamura Kouji Kozaki Akihiko Ohsuga National Institute of Advanced Industrial Science and Technology(AIST) Tokyo 135-0064Japan Graduate School of Informatics and Engineering The University of Electro-CommunicationsTokyo 182-8585Japan National Agriculture and Food Research Organization Ibaraki 305-8517Japan Faculty of Information and Communication Engineering Osaka Electro-Communication UniversityOsaka 572-8530Japan

Urban areas have many problems,including homelessness,graffiti,and *** problems are influenced by various factors and are linked to each other;thus,an understanding of the problem structure is required in order to detect and solve the root problems that generate vicious ***,before implementing action plans to solve these problems,local governments need to estimate cost-effectiveness when the plans are carried ***,this paper proposed constructing an urban problem knowledge graph that would include urban problems’causality and the related cost information in budget *** addition,this paper proposed a method for detecting vicious cycles of urban problems using SPARQL queries with inference rules from the knowledge ***,several root problems that led to vicious cycles were ***-problem experts evaluated the extracted causal relations.

关键词： Linked data Social problem Causality extraction Crowdsourcing Text mining Open city data semantic inference SPARQL semantic Web Rule Language(SWRL)

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