2021 IEEE International Conference on Big Knowledge (ICBK)最新文献

CSRDA: Cost-sensitive Regularized Dual Averaging for Handling Imbalanced and High-dimensional Streaming Data 处理不平衡和高维流数据的成本敏感正则化双平均

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00031

Zhong Chen, Zhide Fang, Victor S. Sheng, Andrea Edwards, Kun Zhang

{"title":"CSRDA: Cost-sensitive Regularized Dual Averaging for Handling Imbalanced and High-dimensional Streaming Data","authors":"Zhong Chen, Zhide Fang, Victor S. Sheng, Andrea Edwards, Kun Zhang","doi":"10.1109/ICKG52313.2021.00031","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00031","url":null,"abstract":"Class-imbalance is one of the most challenging problems in online learning due to its impact on the prediction capability of data stream mining models. Most existing approaches for online learning lack an effective mechanism to handle high-dimensional streaming data with skewed class distributions, resulting in insufficient model interpretation and deterioration of online performance. In this paper, we develop a cost-sensitive regularized dual averaging (CSRDA) method to tackle this problem. Our proposed method substantially extends the influential regularized dual averaging (RDA) method by formulating a new convex optimization function. Specifically, two $R$ 1 -norm regularized cost-sensitive objective functions are directly optimized, respectively. We then theoretically analyze CSRDA's regret bounds and the bounds of primal variables. Thus, CSRDA benefits from achieving a theoretical convergence of balanced cost and sparsity for severe imbalanced and high-dimensional streaming data mining. To validate our method, we conduct extensive experiments on six benchmark streaming datasets with varied imbalance ratios. The experimental results demonstrate that, compared to other baseline methods, CSRDA not only improves classification performance, but also successfully captures sparse features more effectively, hence has better interpretability.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130579015","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

YABKO-Yet Another Big Knowledge Organization yabko——又一个大型知识组织

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00041

R. Lu, Chaoqun Fei, Chuanqing Wang, Yu Huang, Songmao Zhang

{"title":"YABKO-Yet Another Big Knowledge Organization","authors":"R. Lu, Chaoqun Fei, Chuanqing Wang, Yu Huang, Songmao Zhang","doi":"10.1109/ICKG52313.2021.00041","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00041","url":null,"abstract":"Knowledge graph and its processing techniques have got wide spread attention from the AI and knowledge engineering society. However, the knowledge graph supporting platforms have gained much less concern. This paper emphasizes the role of knowledge graph platforms as an independent product of knowledge engineering. Starting from the introduction of HAPE - a programmable universal big knowledge graph platform, which is a predecessor of YABKO, we introduce the idea and technique of Web-based resource sharing public knowledge graph laboratory and its implementation YABKO, which has a threefold target. Firstly, it is an open source platform for researchers doing experimental research on knowledge graphs supported by YABKO's own resources. Secondly, it supports research on big knowledge engineering, in particular in the knowledge graph area. Thirdly, it supports a full life cycle research on big knowledge graphs. Further we introduce YABKOS, a constellation of YABKOs on the Web, which is a decentralized research lab for large scale knowledge graph experiments. Also the wide area programming language Knorc for knowledge graphs' operation orchestration is introduced.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123208903","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

Improving Gradient-based DAG Learning by Structural Asymmetry 基于结构不对称的梯度DAG学习改进

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00022

Yujie Wang, Shuai Yang, Xianjie Guo, Kui Yu

引用次数: 0

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00043

Evan Shieh, Saul Simhon, Geetha G. Aluri, Giorgos Papachristoudis, Doa Yakut, Dhanya Raghu

{"title":"Attribute Similarity and Relevance-Based Product Schema Matching for Targeted Catalog Enrichment","authors":"Evan Shieh, Saul Simhon, Geetha G. Aluri, Giorgos Papachristoudis, Doa Yakut, Dhanya Raghu","doi":"10.1109/ICKG52313.2021.00043","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00043","url":null,"abstract":"Many eCommerce catalogs rely on structured prod-uct data to provide a good experience for customers. For large scale services, product information is provided by millions of different manufacturer and vendor schemas. Due to inherent heterogeneity of this data, unifying it to a consistent catalog schema remains a challenge. Schema matching is the problem of finding such correspondences between concepts in different distributed, heterogeneous data sources. Most approaches in automated schema matching assume either a small number of source schemas, attributes, and contexts (i.e., matching movie attributes from media knowledge bases). By contrast, schema matching in product catalogs encounter the problem of scaling across millions of noisy, heterogenous schemas spanning thou-sands of categories and attributes. In this paper, we introduce a scalable schema matching framework that utilizes unsupervised domain-specific attribute representations and general attribute similarity metrics. Our method first identifies relevant attributes for a given product based on existing customer signals, and then prioritizes among candidate attributes to consolidate only those relevant product facts from multiple manufacturers and vendors with little to no labeled data. We demonstrate value by experiments that enriched catalog data containing millions of attribute enumer-ations sourced from tens of thousands of schemas across a wide range of product categories. Experimental results show reduced manual annotation efforts by 75% from competing schema matching efforts by automating schema matching on targeted product facts, resulting in high accuracy, precision, and recall for important attributes that contribute to customer interest. We also demonstrate performance improvements of 8% MRR using our approach compared against two well-established approaches to unsupervised schema matching.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116356656","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

Gaussian Model-Based Fully Convolutional Networks for Multivariate Time Series Classification 基于高斯模型的全卷积网络多变量时间序列分类

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00028

Changyang Tai, Ze Yang, Huicheng Zhang, Gongqing Wu, Junwei Lv, Xianyu Bao

{"title":"Gaussian Model-Based Fully Convolutional Networks for Multivariate Time Series Classification","authors":"Changyang Tai, Ze Yang, Huicheng Zhang, Gongqing Wu, Junwei Lv, Xianyu Bao","doi":"10.1109/ICKG52313.2021.00028","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00028","url":null,"abstract":"Multivariate time series (MTS) classification has been regarded as one of the most challenging problems in data mining due to the difficulty in modeling the correlation of variables and samples. In addition, high-dimensional MTS modeling has a large time and space consumption. This paper proposes a novel method, Gaussian Model-based Fully Convolutional Networks (GM-FCN), to improve the performance of high-dimensional MTS classification. Each original MTS is converted into multivariate Gaussian model parameters as the input of FCN. These parameters effectively capture the correlation be-tween MTS variables and significantly reduce the data scale by aligning an MTS size to its dimension. FCN is designed to learn more in-depth features of MTS based on these parameters for modeling the correlation between samples. Thus, GM-FCN can not only model the correlation between variables, but also the correlation between samples. We compare GM-FCN with nine state-of-the-art MTS classification methods, INN-ED, INN-DTW-i, INN-DTW-D, KLD-GMC, MLP, ResNet, Encoder, MCNN, and MCDCNN, on four high-dimensional public datasets, experimen-tal results show that the accuracy of G M - FCN is significantly superior to the others. Besides, the training time of GM-FCN is dozens of times faster than FCN using the original equal-length MTS data as input.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121635404","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 efficient framework for sentence similarity inspired by quantum computing 基于量子计算的句子相似度分析框架

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00030

Yan Yu, Dong Qiu, Ruiteng Yan

引用次数: 0

Multi-Round Parsing-based Multiword Rules for Scientific Knowledge Extraction 基于多轮解析的多词规则科学知识抽取

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00051

Joseph Kuebler, Lingbo Tong, Meng Jiang

引用次数: 1

Recognizing Characters and Relationships from Videos via Spatial-Temporal and Multimodal Cues 通过时空和多模态线索识别视频中的人物和关系

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00032

Chenyu Cao, C. Yan, Fangtao Li, Zihe Liu, Z. Wang, Bin Wu

{"title":"Recognizing Characters and Relationships from Videos via Spatial-Temporal and Multimodal Cues","authors":"Chenyu Cao, C. Yan, Fangtao Li, Zihe Liu, Z. Wang, Bin Wu","doi":"10.1109/ICKG52313.2021.00032","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00032","url":null,"abstract":"Video contains rich semantic knowledge of multiple modalities related to a person. Mining deep or potential semantic knowledge in the video could help artificial intelligence better understand the behavior and emotion of humans in the video. The researches for deep and context semantic knowledge in the video are few at present. Many researches on the knowledge mining of characters and visual relationships between humans still remain on static picture, lacking attention to the temporal visual features and other important modalities. In order to better mine the semantic knowledge in the video, we propose the novel Global-local VLAD (GL-VLAD) module, using the convolution of different scales to enlarge different receptive fields and extract the global and local information of features in the video. In addition, we propose a Multimodal Fusion Graph(MFG) to focus on the knowledge of different modalities, which can represent the general features in multi-modal video scenes. We use this method to conduct a large number of experiments of social relation extraction and person recognition on the dataset MovieGraphs and IQIYI- VID-2019. The accuracy and mAP respectively reach 90.23% and 89.87% on IQIYI-VID-2019. The accuracy achieves 56.13 % on the fine-grained dataset MovieGraphs for relation extraction task, while the person recognition of which has values 89.31 % and 85.24% on accuracy and mAP. The experimental results show that our proposed method has better performance than the state-of-the-art methods.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122046670","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

Personalized Recommendation Based On Entity Attributes and Graph Features 基于实体属性和图特征的个性化推荐

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00011

Yi Zhu, Bingbing Dong, Zhiqing Sha

{"title":"Personalized Recommendation Based On Entity Attributes and Graph Features","authors":"Yi Zhu, Bingbing Dong, Zhiqing Sha","doi":"10.1109/ICKG52313.2021.00011","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00011","url":null,"abstract":"With the rapid increase in the amount of website data, it has been a more difficult task for users to get the infor-mation they are interested in. Personalized recommendation is an important bridge to find the information which users really need on the website. Many recent studies have introduced additional attribute information about users and/or items to the rating matrix for alleviating the problem of data sparsity. In order to make full use of the attribute information and scoring matrix, deep learning based recommendation methods are proposed, especially the autoencoder model has attracted much attention because of its strong ability to learn hidden features. However, most of the existing autoencoder- based models require that the dimension of the input layer is equal to the dimension of the output layer, which may increase model complexity and certain information loss when using attribute information. In addition, as users' awareness of privacy protection increases, user attribute information is difficult to obtain. To address the above problems, in this paper, we propose a hybrid personalized recommendation model, which uses a semi-autoencoder to jointly embed the item's score vector and internal graph features (short for Co-Agpre). Specifically, we regard the user-item historical interaction matrix as a bipartite graph, and the Laplacian of the user-item co-occurrence graph is utilized to obtain the graph features of the item for solving the problem of sparse attributes. Then a semi-autoencoder is introduced to learn the hidden features of the item and perform rating prediction. The proposed model can flexibly use information from different sources to reduce the complexity of the model. Experiments on two real-world datasets demonstrate the effectiveness of the proposed Co-Agpre compared with state-of-the-art methods.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130725767","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

Surprisingness - A Novel Objective Interestingness Measure in Hypergraph Pattern Mining from Knowledge Graphs for Common Sense Learning 惊奇度——基于常识学习的知识图超图模式挖掘中的一种新的客观兴趣度度量

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00017

Shujing Ke, P. Spronck, B. Goertzel, Alex Van der Peet

引用次数: 0