2020 IEEE International Conference on Knowledge Graph (ICKG)最新文献

Medical Entity Extraction from Health Insurance Documents 从健康保险文件中提取医疗实体

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00085

Tianling Pu, Qifan Zhang, Junjie Yao, Yingjie Zhang

引用次数: 1

TriNE: Network Representation Learning for Tripartite Heterogeneous Networks triine:三方异构网络的网络表示学习

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00076

Zhabiz Gharibshah, Xingquan Zhu

{"title":"TriNE: Network Representation Learning for Tripartite Heterogeneous Networks","authors":"Zhabiz Gharibshah, Xingquan Zhu","doi":"10.1109/ICBK50248.2020.00076","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00076","url":null,"abstract":"In this paper, we study network representation learning for tripartite heterogeneous networks which learns node representation features for networks with three types of node entities. We argue that tripartite networks are common in real-world applications, and the essential challenge of the representation learning is the heterogeneous relations between various node types and links in the network. To tackle the challenge, we develop a tripartite heterogeneous network embedding called TriNE. The method considers unique user-item-tag tripartite relationships, to build an objective function to model explicit relationships between nodes (observed links), and also capture implicit relationships between tripartite nodes (unobserved links across tripartite node sets). The method organizes metapath-guided random walks to create heterogeneous neighborhood for all node types in the network. This information is then utilized to train a heterogeneous skip-gram model based on a joint optimization. Experiments on real-world tripartite networks validate the performance of TriNE for the online user response prediction using embedding node features.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"38 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":"124463934","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: 1

Coreference Resolution Improves Educational Knowledge Graph Construction 共同参考解析促进教育知识图谱构建

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00094

Tai Wang, Huan Li

{"title":"Coreference Resolution Improves Educational Knowledge Graph Construction","authors":"Tai Wang, Huan Li","doi":"10.1109/ICBK50248.2020.00094","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00094","url":null,"abstract":"An educational knowledge graph provides students and teachers with detailed knowledge organization and a clear concept structure by extracting knowledge points and relationships from textbooks. A high-fidelity knowledge graph is essential for precise teaching and personalized learning. However, as an important step in knowledge graph construction, coreference resolution is often ignored or left to the end. This neglect leads to a loss in the high fidelity of the knowledge graph and may also cause clearly underestimated focuses and fewer associations between knowledge points, although the ratio of the pronouns to the entire corpus is very small (less than 5‰). In this paper, a rule and semantic-based method is proposed to resolve coreference in the knowledge graph constructed from a biology textbook. Compared with the other three algorithms, it has a better precision ratio and recall ratio. By comparing the two knowledge graphs constructed before and after coreference resolution, it can be seen that the focus has changed significantly to better align with the text, and the associations between the knowledge points are more consistent with intuition. This outcome suggests that coreference resolution improves the high fidelity of an educational knowledge graph.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"44 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":"125611473","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: 1

An OpenCV-based Framework for Table Information Extraction 基于opencv的表信息抽取框架

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00093

Jiayi Yuan, Hongye Li, Meng Wang, Ruyang Liu, Chuanyou Li, Beilun Wang

{"title":"An OpenCV-based Framework for Table Information Extraction","authors":"Jiayi Yuan, Hongye Li, Meng Wang, Ruyang Liu, Chuanyou Li, Beilun Wang","doi":"10.1109/ICBK50248.2020.00093","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00093","url":null,"abstract":"Portable Document Format (PDF), as one of the most popular file format, is especially useful for educational documents such as text books, articles, or papers in which we can preserve the original graphic appearance and conveniently share online. Detecting and extracting information from tables in PDF files can provide a plethora of structural data to construct educational knowledge graphs. However, most of the existing methods rely on PDF parsing tools and natural language processing techniques, which generally require training samples and are frail in handling cross-page tables. In light of this, in this paper, we propose a novel OpenCV-based framework to extract the metadata and specific values from PDF tables. Specifically, we first highlight the visual outline of the tables. Then, we locate tables using horizontal and vertical lines and get the coordinates of tabular frames in each PDF page. Once the tables are successfully detected, for each table, we detect the cross-page scenarios and use the Optical Character Recognition (OCR) engine to extract the specific values in each table cell. Differing from other machine learning based methods, the proposed method can achieve table information extraction accurately without labeled data. We conduct extensive experiments on real-world PDF files. The results demonstrate that our approach can effectively deal with cross-page tables and only need 6.12 seconds on average to process a table.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"250 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":"115589572","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: 2

PZXG: A Genealogy Data Service Platform for Kinship Management and Application 面向亲属关系管理与应用的家谱数据服务平台

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00077

Yuwei Peng, Hua Jiang, Rongrong Li, Zhiyong Peng

{"title":"PZXG: A Genealogy Data Service Platform for Kinship Management and Application","authors":"Yuwei Peng, Hua Jiang, Rongrong Li, Zhiyong Peng","doi":"10.1109/ICBK50248.2020.00077","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00077","url":null,"abstract":"Genealogy is one of the three kinds of historical literatures which are valuable to historical research and humanities research. However, traditionally, it is hard to build and revise a genealogy. And it is even impossible to manage and query the genealogy efficiently. In this paper, we proposed PZXG, which is a genealogy data service platform, to solve these issues. Firstly, for structured and unstructured genealogy data, we employ relational database, graph database and distributed file system to store them respectively. Secondly, depending on the hybrid data model of genealogy data, various management features like data acquisition, kin seeking, genealogy exhibition and automatic typesetting are provided. Then in order to extract knowledge from genealogy data, the features of routine statistics and query, root tracing, correlation analysis and data visualization in genealogical data are discussed, and several methods and ideas of text mining for genealogical text are given. Finally, two more urgent research points in the genealogical data management platform are suggested: extracting structural information from unstructured genealogy data and construction of genealogy-specific text language model.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"51 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":"115708250","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 Event-Centric Prediction System for COVID-19 以事件为中心的COVID-19预测系统

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00037

Xiaoyi Fu, Xu Jiang, Yunfei Qi, Mengqi Xu, Yuhang Song, Jie Zhang, Xindong Wu

引用次数: 7

Unfairness Discovery and Prevention For Few-Shot Regression 少次回归的不公平发现与预防

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00029

Chengli Zhao, Feng Chen

{"title":"Unfairness Discovery and Prevention For Few-Shot Regression","authors":"Chengli Zhao, Feng Chen","doi":"10.1109/ICBK50248.2020.00029","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00029","url":null,"abstract":"We study fairness in supervised few-shot meta-learning models that are sensitive to discrimination (or bias) in historical data. A machine learning model trained based on biased data tends to make unfair predictions for users from minority groups. Although this problem has been studied before, existing methods mainly aim to detect and control the dependency effect of the protected variables (e.g. race, gender) on target prediction based on a large amount of training data. These approaches carry two major drawbacks that (1) lacking showing a global cause-effect visualization for all variables; (2) lacking generalization of both accuracy and fairness to unseen tasks. In this work, we first discover discrimination from data using a causal Bayesian knowledge graph which not only demonstrates the dependency of the protected variable on target but also indicates causal effects between all variables. Next, we develop a novel algorithm based on risk difference in order to quantify the discriminatory influence for each protected variable in the graph. Furthermore, to protect prediction from unfairness, a fast-adapted bias-control approach in meta-learning is proposed, which efficiently mitigates statistical disparity for each task and it thus ensures independence of protected attributes on predictions based on biased and few-shot data samples. Distinct from existing meta-learning models, group unfairness of tasks are efficiently reduced by leveraging the mean difference between (un)protected groups for regression problems. Through extensive experiments on both synthetic and real-world data sets, we demonstrate that our proposed unfairness discovery and prevention approaches efficiently detect discrimination and mitigate biases on model output as well as generalize both accuracy and fairness to unseen tasks with a limited amount of training samples.","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":"121127862","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: 14

Balanced Tree Partitioning with Succinct Logic 具有简洁逻辑的平衡树分区

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00083

Xindong Wu, Shaojing Sheng, Peng Zhou

{"title":"Balanced Tree Partitioning with Succinct Logic","authors":"Xindong Wu, Shaojing Sheng, Peng Zhou","doi":"10.1109/ICBK50248.2020.00083","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00083","url":null,"abstract":"As a widely used data structure, graphs are good at characterizing data with internal associations, such as social and biological data. Tree structured data are special and are widely used in many real-world applications, such as organizational structure analysis and genealogical knowledge graph reasoning. For example, in kinship knowledge graph analysis, when a genealogical tree is particularly large (more than 25 levels and 45,000 nodes), it is a great challenge to partition this large tree into a specified number of subtrees with succinct logic and a balanced number of nodes. Therefore, in this paper, we propose the TPA (tree partitioning algorithm) algorithm to achieve a balanced and succinct logic partition of large-scale tree structured data. TPA first extracts all related nodes from a massive graph database and then constructs the convergent subgraph into a complete tree with a specified root node. Specifically, several virtual nodes are supplemented for generation-skipping connected nodes to achieve correct node numbering and partitioning. Finally, a graph partitioning algorithm is executed on the complete tree to obtain a specified number of subtrees with succinct logic and balanced node scales. Experiments conducted on four real-world datasets verify the effectiveness of our TPA algorithm.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"192 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":"115246522","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: 1

The Integrated Organization of Data and Knowledge Based on Distributed Hash 基于分布式哈希的数据和知识集成组织

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00043

Hengli Wang, Yanlei Shang, Xiuquan Qiao

引用次数: 1

B-SPARQL: A typed language for Querying the Big Knowledge B-SPARQL:查询大知识的类型化语言

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00034

R. Lu, Chuanqing Wang, Xikun Huang, Songmao Zhang

引用次数: 0