2020 IEEE International Conference on Knowledge Graph (ICKG)最新文献_第5页

Web Log Analysis in Genealogy System 宗谱系统中的Web日志分析

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00081

Xiaojian Liu, Yi Zhu, Shengwei Ji

{"title":"Web Log Analysis in Genealogy System","authors":"Xiaojian Liu, Yi Zhu, Shengwei Ji","doi":"10.1109/ICBK50248.2020.00081","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00081","url":null,"abstract":"A large amount of log data is generated every moment in many real-world applications. System logs, mobile application logs, web logs etc., all of which contain rich information and can be analyzed for valuable information. At the same time, genealogy attracts more and more attention in recent decades, especially for Chinese. People utilize the genealogy system to seek roots, find source of family, build a family tree and so on. Due to the complexity of genealogy data and specific features of genealogy system, how to extract and analyze the valued genealogy information effectively is a huge challenge. Motivated by this, we propose a novel analysis method using web logs in genealogy system for drawing user profile and providing personalized recommendations. More specifically, firstly, all web logs are extracted from genealogy system based on the enhanced logs which record the behaviors and data of user. Secondly, the extracted logs are formatted for analysis, and we analyze the logs to extract various user-related attributes for drawing user profile. Finally, personalized recommendations like interested people and family tree are provided for each user in system. The high availability and effectiveness of our proposed method is verified on genealogy system-Huapu system.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"156 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":"125913873","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

EntityLDA: A Topic Model for Entity Retrieval on Knowledge Graph EntityLDA:知识图上实体检索的主题模型

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00062

Yu Hong, Suo Feng, Yanghua Xiao

引用次数: 0

Integrating Bi-Dynamic Routing Capsule Network with Label-Constraint for Text classification 基于标签约束的双动态路由胶囊网络文本分类

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00011

Xiang Guo, Youquan Wang, Kaiyuan Gao, Jie Cao, Haicheng Tao, Chaoyue Chen

{"title":"Integrating Bi-Dynamic Routing Capsule Network with Label-Constraint for Text classification","authors":"Xiang Guo, Youquan Wang, Kaiyuan Gao, Jie Cao, Haicheng Tao, Chaoyue Chen","doi":"10.1109/ICBK50248.2020.00011","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00011","url":null,"abstract":"Neural-based text classification methods have attracted increasing attention in recent years. Unlike the standard text classification methods, neural-based text classification methods perform the representation operation and end-to-end learning on the text data. Many useful insights can be derived from neural based text classifiers as demonstrated by an ever-growing body of work focused on text mining. However, in the real-world, text can be both complex and noisy which can pose a problem for effective text classification. An effective way to deal with this issue is to incorporate self-attention and capsule networks into text mining solutions. In this paper, we propose a Bi-dynamic routing Capsule Network with Label-constraint (BCNL) model for text classification, which moves beyond the limitations of previous methods by automatically learning the task-relevant and label-relevant words of text. Specifically, we use a Bi-LSTM and self-attention with position encoder network to learn text embeddings. Meanwhile, we propose a bi-dynamic routing capsule network with label-constraint to adjust the category distribute of text capsules. Through extensive experiments on four datasets, we observe that our method outperforms state-of-the-art baseline methods.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"14 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":"126563255","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

Approximate Single-Peakedness in Large Elections 大型选举中的近似单峰性

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00068

Zhihuai Chen, Q. Li, Xiaoming Sun, Lirong Xia, Jialin Zhang

引用次数: 0

Hybrid Framework of Convolution and Recurrent Neural Networks for Text Classification 基于卷积和递归神经网络的文本分类混合框架

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00052

Shengfei Lyu, Jiaqi Liu

引用次数: 7

Improving Document-level Relation Extraction via Contextualizing Mention Representations andWeighting Mention Pairs 通过上下文化提及表示和加权提及对改进文档级关系提取

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00051

Ping Jiang, Xian-Ling Mao, Bin-Bin Bian, Heyan Huang

{"title":"Improving Document-level Relation Extraction via Contextualizing Mention Representations andWeighting Mention Pairs","authors":"Ping Jiang, Xian-Ling Mao, Bin-Bin Bian, Heyan Huang","doi":"10.1109/ICBK50248.2020.00051","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00051","url":null,"abstract":"Document-level relation extraction (RE) has attracted considerable attention, because a large number of relational facts are expressed in multiple sentences. Recently, encoder-aggregator based models have become promising for document-level RE. However, these models have two shortcomings: (i) they cannot obtain contextualized representations of a mention by low computational cost, when the mention is involved in different entity pairs; (ii) they ignore the different weights for the mention pairs of a target entity pair. To tackle the above two problems, in this paper, we propose a novel encoder-attender-aggregator model, which introduces two attenders between the encoder and aggregator. Specifically, a mutual attender is first employed on the selected head and tail mentions to efficiently produce contextualized mention representations. Then, an integration attender is utilized to weight the mention pairs of a target entity pair. Extensive experiments on two document-level RE datasets show that the proposed model performs better than the state-of-the-art baselines. Our codes are publicly available at “https://github.com/nefujiangping/EncAttAgg”.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"263 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":"131414033","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

Prediction of Financial Big Data Stock Trends Based on Attention Mechanism 基于注意机制的金融大数据股票走势预测

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00031

Jiannan Chen, Junping Du, Zhe Xue, Feifei Kou

引用次数: 4

Relation Extraction with Proactive Domain Adaptation Strategy 基于主动领域自适应策略的关系提取

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00069

Lingfeng Zhong, Yi Zhu

{"title":"Relation Extraction with Proactive Domain Adaptation Strategy","authors":"Lingfeng Zhong, Yi Zhu","doi":"10.1109/ICBK50248.2020.00069","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00069","url":null,"abstract":"Relation extraction is an important information extraction task in many Natural Language Processing (NLP) applications, such as automatic knowledge graph construction, question answering, sentiment analysis, etc. However, relation extraction suffers from inappropriate associations between entities when the background knowledge of corpus is insufficiency. Despite the preprocessed external word vector bases can ease this problem, how to find a single word vector base as domain knowledge that contains all the required knowledge features is a huge challenge, and relation extraction with background knowledge is still open to further optimization. To address this problem, in this paper, we propose Relation Extraction method with Proactive Domain Adaptation Strategy (REPDAS for short) to introduce more knowledge features from different knowledge bases. More specifically, firstly, a convolutional network with a parameter-sharing layer is introduced for relation extraction, and word seeds that are important to relational feature exploitation are proactively picked by an attention mechanism during training. Secondly, the proactively-chosen word seeds and the previous parameter-sharing layer are utilized to establish a map between different domains. Our proposed method selectively avails both background knowledge and contextual features for relation extraction by incorporating the convolutional neural network with the proactively domain adaptation strategy. Experiments show that our method effectively enhances the performance of relation extraction compared with other baselines.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"17 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":"114989078","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

Online Markov Blanket Discovery With Streaming Features 具有流特性的在线马尔可夫毯子发现

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00023

Dianlong You, Ruiqi Li, Miaomiao Sun, Xinju Ou, Shunpan Liang, Fuyong Yuan

{"title":"Online Markov Blanket Discovery With Streaming Features","authors":"Dianlong You, Ruiqi Li, Miaomiao Sun, Xinju Ou, Shunpan Liang, Fuyong Yuan","doi":"10.1109/ICBK50248.2020.00023","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00023","url":null,"abstract":"The Markov blanket (MB) in Bayesian networks has attracted much attention since the MB of a target attribute (T) is the minimal feature subset with maximum prediction ability for classification. Nevertheless, traditional MB discovery methods such as IAMB, HITON-MB, and MMMB are not suitable for streaming features. Meanwhile, online feature selection with streaming features (OSFSF) methods such as Alpha-investing and SAOLA, focus only on the relevance and ignores causality of the features, they cannot mine the MB, or only find the parents and children (PC) such as OSFS. Therefore, these methods have weaker interpretability and do not have sufficient prediction accuracy. We propose a novel algorithm for online markov blanket discovery with streaming features to tackle abovementioned issues, named OMBSF. OMBSF finds the MB of T containing parents, children, and spouses, discards false positives from the PC and spouse sets online, distinguishes between the PC and spouse in real-time. An empirical study demonstrates that OMBSF finds a more accurate MB when the volume of features is high and it significantly improves the prediction accuracy than other algorithms. Moreover, OMBSF obtains a bigger feature subset than that obtained by OSFS, demonstrating that OMBSF can identify the spouse in the MB that are not identified using OSFS.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"39 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":"123354580","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

Multi-task Classification Model Based On Multi-modal Glioma Data 基于多模态胶质瘤数据的多任务分类模型

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00033

Jialun Li, Yuanyuan Jin, Hao Yu, Xiaoling Wang, Qiyuan Zhuang, Liang Chen

引用次数: 0