2020 IEEE International Conference on Knowledge Graph (ICKG)最新文献_第10页

Dual Graph Embedding for Object-Tag Link Prediction on the Knowledge Graph 知识图上对象-标签链接预测的双图嵌入

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-07-16 DOI: 10.1109/ICBK50248.2020.00048

Chenyang Li, Xu Chen, Ya Zhang, Siheng Chen, Dan Lv, Yanfeng Wang

{"title":"Dual Graph Embedding for Object-Tag Link Prediction on the Knowledge Graph","authors":"Chenyang Li, Xu Chen, Ya Zhang, Siheng Chen, Dan Lv, Yanfeng Wang","doi":"10.1109/ICBK50248.2020.00048","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00048","url":null,"abstract":"Knowledge graphs (KGs) composed of users, objects, and tags are widely used in web applications ranging from E-commerce, social media sites to news portals. This paper concentrates on an attractive application which aims to predict the object-tag links in the KG for better tag recommendation and object explanation. When predicting the object-tag links, both the first-order and high-order proximities between entities in the KG propagate essential similarity information for better prediction. Most existing methods focus on preserving the first-order proximity between entities in the KG. However, they cannot capture the high-order proximities in an explicit way, and the adopted margin-based criterion cannot measure the first-order proximity on the global structure accurately. In this paper, we propose a novel approach named Dual Graph Embedding (DGE) that models both the first-order and high-order proximities in the KG via an auto-encoding architecture to facilitate better object-tag relation inference. Here the dual graphs contain an object graph and a tag graph that explicitly depict the high-order object-object and tag-tag proximities in the KG. The dual graph encoder in DGE then encodes these high-order proximities in the dual graphs into entity embeddings. The decoder formulates a skipgram objective that maximizes the first-order proximity between observed object-tag pairs over the global proximity structure. With the supervision of the decoder, the embeddings derived by the encoder will be refined to capture both the first-order and high-order proximities in the KG for better link prediction. Extensive experiments on three real-world datasets demonstrate that DGE outperforms the state-of-the-art methods.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"61 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130935778","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

Collaborative Adversarial Learning for Relational Learning on Multiple Bipartite Graphs 多二部图关系学习的协同对抗学习

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-07-16 DOI: 10.1109/ICBK50248.2020.00072

Jingchao Su, Xu Chen, Ya Zhang, Siheng Chen, Dan Lv, Chenyang Li

{"title":"Collaborative Adversarial Learning for Relational Learning on Multiple Bipartite Graphs","authors":"Jingchao Su, Xu Chen, Ya Zhang, Siheng Chen, Dan Lv, Chenyang Li","doi":"10.1109/ICBK50248.2020.00072","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00072","url":null,"abstract":"Relational learning aims to make relation inference by exploiting the correlations among different types of entities. Exploring relational learning on multiple bipartite graphs has been receiving attention because of its popular applications such as recommendations. How to make efficient relation inference with few observed links is the main problem on multiple bipartite graphs. Most existing approaches attempt to solve the sparsity problem via learning shared representations to integrate knowledge from multi-source data for shared entities. However, they merely model the correlations from one aspect (e.g. distribution, representation), and cannot impose sufficient constraints on different relations of the shared entities. One effective way of modeling the multi-domain data is to learn the joint distribution of the shared entities across domains. In this paper, we propose Collaborative Adversarial Learning (CAL) that explicitly models the joint distribution of the shared entities across multiple bipartite graphs. The objective of CAL is formulated from a variational lower bound that maximizes the joint log-likelihoods of the observations. In particular, CAL consists of distribution-level and feature-level alignments for knowledge from multiple bipartite graphs. The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs. Extensive experiments on two real-world datasets have shown that the proposed model outperforms the existing methods.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115445349","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

Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework 知识联盟:一个统一的、分层的隐私保护AI框架

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-02-05 DOI: 10.1109/ICBK50248.2020.00022

Hongyu Li, D. Meng, Hong Wang, Xiaolin Li

{"title":"Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework","authors":"Hongyu Li, D. Meng, Hong Wang, Xiaolin Li","doi":"10.1109/ICBK50248.2020.00022","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00022","url":null,"abstract":"With strict protections and regulations of data privacy and security, conventional machine learning based on centralized datasets is confronted with significant challenges, making artificial intelligence (AI) impractical in many mission-critical and data-sensitive scenarios, such as finance, government, and health. In the meantime, tremendous datasets are scattered in isolated silos in various industries, organizations, different units of an organization, or different branches of an international organization. These valuable data resources are well underused. To advance AI theories and applications, we propose a comprehensive framework (called Knowledge Federation- KF) to address these challenges by enabling AI while preserving data privacy and ownership. Beyond the concepts of federated learning and secure multi-party computation, KF consists of four levels of federation: (1) information level, low-level statistics and computation of data, meeting the requirements of simple queries, searching and simplistic operators; (2) model level, supporting training, learning, and inference; (3) cognition level, enabling abstract feature representation at various levels of abstractions and contexts; (4) knowledge level, fusing knowledge discovery, representation, and reasoning. We further clarify the relationship and differentiation between knowledge federation and other related research areas. We have developed a reference implementation of KF, called iBond Platform, to offer a production-quality KF platform to enable industrial applications in finance, insurance, marketing, and government. The iBond platform will also help establish the KF community and a comprehensive ecosystem and usher in a novel paradigm shift towards secure, privacy-preserving and responsible AI. As far as we know, knowledge federation is the first hierarchical and unified framework for secure multi-party computing (statistics, queries, searching, and low-level operations) and learning (training, representation, discovery, inference, and reasoning).","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126008249","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

AHINE: Adaptive Heterogeneous Information Network Embedding 自适应异构信息网络嵌入

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2019-08-20 DOI: 10.1109/ICBK50248.2020.00024

Yucheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye

{"title":"AHINE: Adaptive Heterogeneous Information Network Embedding","authors":"Yucheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye","doi":"10.1109/ICBK50248.2020.00024","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00024","url":null,"abstract":"Network embedding is an effective way to solve the network analytics problems such as node classification, link prediction, etc. It represents network elements using low dimensional vectors such that the graph structural information and properties are maximumly preserved. Many prior works focused on embeddings for networks with the same type of edges or vertices, while some works tried to generate embeddings for heterogeneous network using mechanisms like specially designed meta paths. In this paper, we propose novel Adaptive Heterogeneous Information Network Embedding (AHINE), to compute distributed representations for elements in heterogeneous networks. Specially, AHINE uses an adaptive deep model to learn network embeddings that maximizes the likelihood of preserving the relation chains not only between adjacent nodes but also between non-adjacent nodes. We apply our embeddings to a large network of points of interest (POIs) and achieve superior accuracy on some prediction problems on a ride-hailing platform. In addition, we show that AHINE outperforms state-of-the-art methods on a set of learning tasks on public datasets, including node labelling and similarity ranking in bibliographic networks.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129207428","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