2020 IEEE International Conference on Knowledge Graph (ICKG)最新文献_第6页

Graph-based Keyphrase Extraction Using Word and Document Em beddings* 基于图形的关键短语提取，使用Word和文档Em寝具*

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00020

Xian Zu, Fei Xie, Xiaojian Liu

引用次数: 4

ICKG 2020 List Reviewer Page ICKG 2020名单审核页面

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/icbk50248.2020.00008

Huanhuan Chen

引用次数: 0

An Adaptive Ant Colony optimization in Knowledge Graphs 知识图谱中的自适应蚁群优化

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00014

Wei Li, Le Xia, Ying Huang

引用次数: 1

Conversational Music Recommendation based on Bandits 基于土匪的会话音乐推荐

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00016

Chunyi Zhou, Yuanyuan Jin, Xiaoling Wang, Yingjie Zhang

{"title":"Conversational Music Recommendation based on Bandits","authors":"Chunyi Zhou, Yuanyuan Jin, Xiaoling Wang, Yingjie Zhang","doi":"10.1109/ICBK50248.2020.00016","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00016","url":null,"abstract":"Music is one of the most popular products in the recommender system, and there have been many various methods of exploring music recommendations. Traditional music recommendations commonly collect users’ feedbacks in limited ways for preference analysis. The text dialogue is a direct and natural interactive mode, providing diversified information. In this paper, we discuss the music recommendation in an innovative scenario – a conversational music recommendation model, which integrates the advantages both from the recommender system and dialog system. This paper adopts a ”user ask, system respond” interactive way to obtain users’ real-time requirements, and users are allowed to express their requirements on music in free text. In order to face the fast-changing music preferences, this paper adopts the bandit-based algorithm to absorb users’ attitudes to the current recommendation, and the results show these methods achieve better performance than baselines. Besides, it also constructs a music-domain knowledge graph to support the richer users’ musical expressions with millions of music items and tens of millions of relations.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"91 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":"127760727","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: 9

Online Streaming Feature Selection Based on Feature Interaction 基于特征交互的在线流媒体特征选择

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00017

Yan Lv, Yaojin Lin, Xiangyan Chen, Dongxing Wang, Chenxi Wang

{"title":"Online Streaming Feature Selection Based on Feature Interaction","authors":"Yan Lv, Yaojin Lin, Xiangyan Chen, Dongxing Wang, Chenxi Wang","doi":"10.1109/ICBK50248.2020.00017","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00017","url":null,"abstract":"In many big data applications, online streaming feature selection plays a critical role in processing feature stream and dealing with high-dimensional problems. However, traditional online streaming feature selection methods focus on relevant features, irrelevant and/or redundant features, ignore the interaction between features. i.e., individual feature and label are irrelevant or weakly correlated, but when it is combined with another irrelevant or weakly feature, they show strongly correlated with label. In this paper, we propose a novel feature selection algorithm that considers feature interaction based on neighborhood rough set. This algorithm select features based on the following principles: the discrimination capability of the selected feature subset should be greater than or equal to the original feature space, and the number of features subset should be as small as possible by using feature interaction. Under this framework, we propose an online significance analysis criterion to select significance features relative to the currently selected features, and design an online redundancy analysis criterion to retain highly interactive features and filter out redundant features. Experimental results on a series of benchmark datasets show that the proposed algorithm significantly outperforms other state-of-the-art online streaming feature selection methods.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"25 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":"126580923","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: 6

Imbalanced Learning for Hospital Readmission Prediction using National Readmission Database 利用国家再入院数据库进行医院再入院预测的不平衡学习

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00026

Shuwen Wang, Magdalyn E. Elkin, Xingquan Zhu

{"title":"Imbalanced Learning for Hospital Readmission Prediction using National Readmission Database","authors":"Shuwen Wang, Magdalyn E. Elkin, Xingquan Zhu","doi":"10.1109/ICBK50248.2020.00026","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00026","url":null,"abstract":"In this paper, we propose to use imbalanced learning for hospital readmission prediction. The goal is to predict whether a patient, based on his/her current hospital visit records, is likely going to be re-admitted or not within 30-days after being discharged from the current hospital visit. The main challenge of hospital readmission prediction is twofold: (1) the readmission visits (i.e., the positive class) are a small portion of the total hospital visits, representing a severe class imbalance problem for learning; (2) due to privacy and health regulation, the information available for patient characterization is limited; and is often only limited to the payment level information. However, there are over 80,000 procedures code, representing a high dimensionality and high sparsity problem for learning. Motivated by the above challenges, in this paper, we design an imbalanced learning strategy to create features from patient hospital visit, by combining patient demographic information, ICD-10 clinical modification (CM) and procedure codes (PCS), and Clinical Classification Software Refined (CCSR) conversion. Instead of directly using ICD-10-CM/PCS code to characterize patients, we convert each patient’s visit to CCSR code space with a smaller feature space. By using random sampling approach to balance the sample distributions in the training set, our method achieves good performance to predict patient readmission.","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":"125778188","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: 3

Subject Event Extraction from Chinese Court Verdict Case via Frame-filling 基于框架填充的中国法院判决案件主体事件提取

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00012

Gongqing Wu, Shengjie Hu, Yinghuan Wang, Zan Zhang, Xianyu Bao

{"title":"Subject Event Extraction from Chinese Court Verdict Case via Frame-filling","authors":"Gongqing Wu, Shengjie Hu, Yinghuan Wang, Zan Zhang, Xianyu Bao","doi":"10.1109/ICBK50248.2020.00012","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00012","url":null,"abstract":"At present, the query and acquisition of the fragmented knowledge in Chinese court verdicts mainly adopt the class case retrieval method based on the search engine and the rough extraction method for a part of the data in court verdicts. These traditional methods cannot structurally extract fragmented knowledge in Chinese court verdicts and meet the needs of people for the follow-up analysis of court verdicts. Thus, in this paper, we present a structured subject event extraction method (SEE) for Chinese court verdict cases combining with techniques of event extraction (EE) and attribute-value pair extraction (AVPE). Specifically, we provide a subject event representation frame for organizing fragmented knowledge in Chinese court verdict cases. Then, we extract subject events from the unstructured cases based on the trained sequence labeling models and constructed heuristic rules, and fill them into the subject event representation frame in the form of attribute-value pairs (AVPs). The experimental results show that SEE can efficiently and automatically extract subject events from Chinese court verdict cases and visually display them via frame-filling, which promotes the efficiency of people in searching for legal materials and facilitates further research and analysis.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"62 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":"126012645","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: 3

Cyber Security Meets Big Knowledge: Towards a Secure HACE Theorem 网络安全遇上大知识:迈向安全HACE定理

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00010

B. Thuraisingham

引用次数: 0

A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition 低资源命名实体识别的鲁棒域自适应方法

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00050

Houjin Yu, Xian-Ling Mao, Zewen Chi, Wei Wei, Heyan Huang

引用次数: 9

Clustering via Meta-path Embedding for Heterogeneous Information Networks 基于元路径嵌入的异构信息网络聚类

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00036

Yongjun Zhang, Xiaoping Yang, Liang Wang

{"title":"Clustering via Meta-path Embedding for Heterogeneous Information Networks","authors":"Yongjun Zhang, Xiaoping Yang, Liang Wang","doi":"10.1109/ICBK50248.2020.00036","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00036","url":null,"abstract":"A low-dimensional embedding of multiple nodes is very convenient for clustering, which is one of the most fundamental tasks for heterogeneous information networks (HINs). On the other hand, the random walk-based network embedding is proved to be equivalent to the method of matrix factorization whose computational cost is very expensive. Moreover, mapping different types of nodes into one metric space may result in incompatibility. To cope with the two challenges above, a meta-path embedding based clustering method (called MPEClus) is proposed in this paper. Firstly, the original network is transformed into several subnetworks with independent semantics specified by meta-paths to solve the incompatibility problem. Secondly, an approximate commute embedding method, bypassing eigen-decomposition to reduce computational cost, is leveraged to the representation learning of the nodes in each subnetwork. At last, a unified probabilistic generation model is designed to aggregate the vectorized representations learned in different metric spaces for clustering. Experiment results show that MPEClus is effective in HIN clustering and outperforms the state-of-the-art baselines on two real-world datasets.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"7 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":"115844318","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