2020 IEEE International Conference on Knowledge Graph (ICKG)最新文献_第9页

The Short-Term Power Consumption Forecasting Based on the Portrait of Substation Areas 基于变电站区域画像的短期用电量预测

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00097

Ruiyang Jin, Yunlei Lu, Yunhong Wang, Jie Song

引用次数: 1

Event Extraction for Criminal Legal Text 刑事法律文本的事件提取

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00086

Qingquan Li, Qifan Zhang, Junjie Yao, Yingjie Zhang

引用次数: 5

Adaptive domain of dynamic distribution based on manifold space 基于流形空间的自适应动态分布域

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00018

Daoyuan Yu, Xuegang Hu

{"title":"Adaptive domain of dynamic distribution based on manifold space","authors":"Daoyuan Yu, Xuegang Hu","doi":"10.1109/ICBK50248.2020.00018","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00018","url":null,"abstract":"Domain adaption aims to use the source domain knowledge to assist the model learning. Most of the existing methods are based on the feature representation learning model, which are achieved by aligning the data distribution between two domains. However, in the process of feature representation learning, due to the diversity of distribution differences between domains, it faces the problems of degenerated feature transformation and unevaluated distribution alignment, which bring challenges to the existing research. Therefore, the dynamic distribution alignment factors of marginal distribution and conditional distribution are introduced to reduce the spatial distortion in the process of feature representation, and the domain adaptation is realized based on the dynamic manifold regularization constraints and structural risk minimization learning. In this paper, we propose the structural risk minimization and an improved dynamic manifold regularization constraint to solve these problems. Experimental results show that compared with traditional methods and deep level methods, the algorithm in this paper has a significant improvement in classification accuracy.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133885443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Abnormal Data Analysis and Processing Method for Genealogy Graph Databases 族谱数据库异常数据分析与处理方法

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00028

Jianxuan Shao, Guliu Liu, Shengwei Ji

{"title":"An Abnormal Data Analysis and Processing Method for Genealogy Graph Databases","authors":"Jianxuan Shao, Guliu Liu, Shengwei Ji","doi":"10.1109/ICBK50248.2020.00028","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00028","url":null,"abstract":"Large amounts of data are generated continually in the real world. The objects in the data and the relationships between them have become increasingly complex. Graph is a powerful tool for representing these data and the complex relationships between them. To effectively describe the entities and connections in these data, the concept of a knowledge graph was proposed, and knowledge graph has become one of the bases for storing graph data, and the importance of it is self-evident. Genealogy data is a kind of graph data where nodes are used to represent the person in the genealogy data, and edges are used to represent the relationship between the person. Moreover, in a genealogy graph database, the \"critical\" relationship that is unique for an entity is defined. In contrast, the abnormal data derive from the existence of multiple \"critical\" relationships between entities. In a genealogy graph, abnormal data will cause the wrong relationships and redundant entities. To avoid such abnormal data in genealogy graph database, the abnormal data are categorized into four different types, and the corresponding processing methods are proposed for each type of abnormal data, respectively. The experiment results demonstrate that the processing method can effectively solve these abnormal data.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133473209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BaKGraSTeC: A Background Knowledge Graph Based Method for Short Text Classification 基于背景知识图的短文本分类方法

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00058

Xuhui Jiang, Yinghan Shen, Yuanzhuo Wang, Xiaolong Jin, Xueqi Cheng

{"title":"BaKGraSTeC: A Background Knowledge Graph Based Method for Short Text Classification","authors":"Xuhui Jiang, Yinghan Shen, Yuanzhuo Wang, Xiaolong Jin, Xueqi Cheng","doi":"10.1109/ICBK50248.2020.00058","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00058","url":null,"abstract":"Short text classification is an important task in the area of natural language processing. Recent studies attempt to employ external knowledge to improve classification performance, but they ignore the correlation between external knowledge and have poor interpretability. This paper proposes a novel Background Knowledge Graph based method for Short Text Classification called BaKGraSTeC for short, which can not only employ external knowledge from a knowledge graph to enrich text information, but also utilize its structural information through a graph neural network to promote the understanding of texts. Specifically, we construct a background knowledge graph based on training data, then we propose a novel architecture that integrates background knowledge graph into a graph neural network to model and capture implicit interactions between its concepts and classes. Besides, we propose an attention mechanism considering both similarity and co-occurrence between concepts and classes to identify the informative concepts in texts. Our experimental results demonstrate the effectiveness with good interpretability of BaKGraSTeC through using external knowledge and their structural information for short text classification.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131344444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Drug Drug Interaction Extraction from Chinese Biomedical Literature Using Distant Supervision 远程监督下中国生物医学文献中药物相互作用的提取

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00089

Jingzhuo Zhang, Weijie Liu, Ping Wang

引用次数: 0

FEPF: A knowledge Fusion and Evaluation Method based on Pagerank and Feature Selection FEPF:一种基于网页排名和特征选择的知识融合与评价方法

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00095

Yimu Ji, Kaihang Liu, Shangdong Liu, S. Tang, Wan Xiao, Zhengyang Xu, Lin Hu, Yanlan Liu, Qiang Liu

引用次数: 0

ICKG 2020 Committees

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/icbk50248.2020.00006

Xindong Wu, Vipin Kumar, Jie Cao, Enhong Chen

{"title":"ICKG 2020 Committees","authors":"Xindong Wu, Vipin Kumar, Jie Cao, Enhong Chen","doi":"10.1109/icbk50248.2020.00006","DOIUrl":"https://doi.org/10.1109/icbk50248.2020.00006","url":null,"abstract":"Jia Wu, Macquarie University, Australia Guanfeng Liu, Macquarie University, Australia Lan Du, Monash University, Australia Mehmet Ali Orgun, Macquarie University, Australia Gabriela Montoya, Aalborg University, Denmark Qing Liu, Hong Kong Baptist University, Hong Kong, China Xiaoye Miao, Zhejiang University, China Leong Hou U, University of Macau, Macau, China James Bailey, The University of Melbourne, Australia Yuan Cao, Dalian University of Technology, China Pui Cheong Fung, Arizona State University, USA Fangqing Gu, Guangdong University of Technology China Francisco Herrara, Unv of Grenada, Spain Jane Hunter, University of Queensland, Australia Xiaolong Jin, Institute of Computing Technology, Chinese Academy of Sciences, China Guilin Qi, Southeast University, China Jie Yin, The University of Sydney, Australia Ning Zhong, Maebashi Institute of Technology, Japan Emma Xue, CSIRO' data61 Hong Huang, Huazhong University of Science and Technology, China, and University of Goettingen, Germany Hongxia Yang, Alibaba, China Huaiyu Wan, Beijing Jiaotong University, China Jibing Gong, Renmin University of China Sha Yuan, Beijing Academy of Artificial Intelligence, China Shenghua Liu, Chinese Academy of Sciences, China Yang Yang, Zhejiang University, China Yutao Zhang, Tsinghua University, China Zhilin Yang, Carnegie Mellon University, USA Bang Liu, University of Alberta, Canada Hongying Zan, Zhengzhou University, China Tieyun Qian, Wuhan University, China Mutua Zhu, Alibaba, China Zhixu Li, Suzhou University, China Chen Lin, Xiamen University, China Deqing Yang, Fudan University, China Wanyun Cui, Shanghai University of Finance and Economics, China Bo Xu, Donghua University, China Francesco Gelli, National University of Singapore, Singapore Uricchio Tiberio, Università degli Studi di Firenze Xun Yang, National University of Singapore, Singapore Farid Razzak, Rutgers University, USA Hosein Azarbonyad, KLM Research Ridho Reinanda, Bloomberg Research Julia Kiseleva, Microsoft Research Zhefeng Wang, Huawei Technologies, China","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"27 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116686734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TCMKG: A Deep Learning Based Traditional Chinese Medicine Knowledge Graph Platform 基于深度学习的中医知识图谱平台

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00084

Ziqiang Zheng, Yongguo Liu, Yun Zhang, Chuanbiao Wen

引用次数: 16

Resolving Seemingly Conflicting Fact Statements Caused by Missing Terms 解决因遗漏条款而导致的看似矛盾的事实陈述

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00044

Liang Wang, Rongrong Li, W. Meng, Zhiyong Peng

{"title":"Resolving Seemingly Conflicting Fact Statements Caused by Missing Terms","authors":"Liang Wang, Rongrong Li, W. Meng, Zhiyong Peng","doi":"10.1109/ICBK50248.2020.00044","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00044","url":null,"abstract":"The Web has become the most useful resource for people to acquire information. However, it still contains much incorrect and inaccurate information. While the problem of resolving conflicting structured Web data has been studied extensively, the corresponding problem involving unstructured text data has rarely been ad-dressed. In this paper, we consider how to resolve seemingly conflicting fact statements (SCFS). A statement is a fact statement if it attempts to state a fact, although the stated fact may not necessarily be correct. In this paper, multiple fact statements are SCFSs if they are identical except that they have different terms at a certain (same) position. In general, SCFSs are not necessarily truly in conflict. Some conflicts may result from imprecise statements and may be resolved by improving the precision of the statement. In this work, we focus on a special type of SCFSs whose corresponding statements are imprecise because they have missing terms. More specifically, given a set of SCFSs, we propose a method to mine the possible missing terms. This method effectively transforms an imprecise fact statement to precise ones. At the same time, our method also resolves the seemingly conflicts among the transformed fact statements. The experimental results show that our best solution for missing term mining on different data sets can achieve close to 90% F-score, which improves a baseline method by about 40%. We also propose a method to place the mined missing term for each SCFS at the right place in the statement and our experiments show that this method has an accuracy of about 73%.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127305406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0