2020 IEEE International Conference on Knowledge Graph (ICKG)最新文献_第2页

Neural Attentive Knowledge Tracing Model for Student Performance Prediction 学生成绩预测的神经关注知识追踪模型

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00096

Junrui Zhang, Yun Mo, Changzhi Chen, Xiaofeng He

{"title":"Neural Attentive Knowledge Tracing Model for Student Performance Prediction","authors":"Junrui Zhang, Yun Mo, Changzhi Chen, Xiaofeng He","doi":"10.1109/ICBK50248.2020.00096","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00096","url":null,"abstract":"A large number of anonymous log files are collected from the online education platform, and it is of great educational significance to use efficient algorithms for mining student’s characteristics and predicting student’s performance. To the best of our knowledge, existing models lack attention to the long-term performance of students. The interpretability of the operating results is weak. In addition, these models simplify the tracking of student knowledge points and are essentially unable to capture the relationship between skills in multi-skill exercises. We propose a new model, NAKTM, which divides user features into long-term and short-term features, and uses both to comprehensively express student abilities. At the same time, it uses the skills involved in the exercises as much as possible to jointly represent the characteristics of the exercises. Finally, we use the bilinear matching scheme in the hidden space to calculate the similarity between the students’ ability and the exercises, and finally directly predict the learner’s performance at the exercise level at the next moment. The experiment shows that our model achieves good experimental results without special processing of datasets.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"36 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":"134093465","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

Message from the Program Chairs - ICKG 2020 2020年ICKG会议主持人致辞

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/icbk50248.2020.00005

引用次数: 0

Neural Restaurant-aware Dish Recommendation 神经餐馆感应菜肴推荐

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00090

Yuanyuan Jin, Wei Zhang, Mingyou Sun, Xing Luo, Xiaoling Wang

{"title":"Neural Restaurant-aware Dish Recommendation","authors":"Yuanyuan Jin, Wei Zhang, Mingyou Sun, Xing Luo, Xiaoling Wang","doi":"10.1109/ICBK50248.2020.00090","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00090","url":null,"abstract":"Food is the first necessity of the people. Due to the fast-paced modern life, people usually choose to dine out for convenience. While existing methods have paid efforts for the food recommendation, they are mainly limited in inferring users’ personal preferences for online recipes, and ignore the dish ordering process in dine-out scenarios. Given the same recipe, different restaurants may produce various tastes due to food cuisines or chefs’ cooking habits. In the current restaurant, users’ general favored dish may have bad word-of-mouth. Thus, apart from their personal taste preferences, users also turn to restaurant specialties to guarantee the dish quality. As such, the restaurant-related dish quality and users’ personal taste should be considered simultaneously. To address this task, we propose a neural restaurant-aware dish recommender to infer users’ preferences for dishes in a specific restaurant. Given a dish in the current restaurant, whether to order it or not is mainly decided by two factors: users’ personal taste and the dish quality in this restaurant. Our proposed model can: 1) capture users’ personal diet preferences by the strong expressiveness of neural networks; 2) evaluate how good the current restaurant is at cooking certain dishes. To show the effectiveness of our proposed model, we conduct extensive experiments on a real dataset, demonstrating significant improvements over the several competing models, such as NCF with an average improvement of 36%, and PITF with 3.4%.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"28 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":"125030662","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

Traffic Signal Classification with Cost-Sensitive Deep Learning Models 基于代价敏感深度学习模型的交通信号分类

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00088

T. Tsoi, Charles Wheelus

{"title":"Traffic Signal Classification with Cost-Sensitive Deep Learning Models","authors":"T. Tsoi, Charles Wheelus","doi":"10.1109/ICBK50248.2020.00088","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00088","url":null,"abstract":"Deep learning has many successful real-world applications including traffic signal recognition, which are used in driver assistance systems and autonomous vehicles. Accurate detection of traffic signal indications is critical to ensure safety under autonomous driving. Many past studies have been completed on traffic signal recognition including detection, classification and tracking with datasets which are typically highly imbalanced due to the nature of traffic signal displays. However, most studies simply ignored the minority classes and did not consider cost-sensitive information inherent to traffic signal indications. This paper evaluated several cost-sensitive techniques applicable to deep learning models in traffic signal classification. A convolutional neural network (CNN) was used in the evaluation as the baseline model. Cost-sensitive techniques including cost-proportionate rejection sampling and the use of cost-sensitive loss function was then applied to the baseline CNN model to evaluate and compare the effects of using cost information in traffic signal classification. Arbitrary cost information was assumed in this evaluation, but the resulting models did not improve accuracy in prediction. Future studies may consider more carefully crafted cost information and/or other cost-sensitive techniques.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"2 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":"123004277","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

A Roadmap to Domain Knowledge Integration in Machine Learning 机器学习领域知识集成路线图

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00030

Himel Das Gupta, V. Sheng

引用次数: 2

Discovering the Most Influential Geo-Social Object Using Location Based Social Network Data 利用基于位置的社交网络数据发现最具影响力的地理社会对象

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00091

Pengfei Jin, Zhanyu Liu, Yao Xiao

{"title":"Discovering the Most Influential Geo-Social Object Using Location Based Social Network Data","authors":"Pengfei Jin, Zhanyu Liu, Yao Xiao","doi":"10.1109/ICBK50248.2020.00091","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00091","url":null,"abstract":"In the scope of knowledge engineering, discovering the most influential geo-social object is one of the most extensively studied problems, where the reverse top-k queries can be used as a key technique to detect the influence set, also refereed as potential customers in this paper. By issuing reverse top-k queries, merchants can get the knowledge of the potential influence of their products and then make effective decisions in business promotion applications. In this paper, we study the problem of discovering most influential geo-social object using LBSN data. More specifically, given a set $mathcal{U}$ of LBSN users, a set $mathcal{O}$ of geo-social objects, and a set $mathcal{O}$ of candidate objects extracted from $mathcal{O}_{c}$, we attempt to find the optimal one in $mathcal{C}$ that has the largest potential influence, where the potential influence of an object is defined by the size of users in its reverse top-k query results. Such problem is practical for merchants to monitor which product among all the products is the most popular with the potential customers. A baseline approach based on a batch processing framework is proposed to facilitate answering this problem. On the top of this solution, a series of optimizations are integrated to further improve its performance and make it more efficient in practise. Experiments on two datasets are conducted to verify the effectiveness and efficiency of the proposed methods.","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":"129515639","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

Network Analysis of Technology Stocks using Market Correlation 基于市场相关性的科技股网络分析

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00046

L. Chmielewski, Rafina Amin, Anak Wannaphaschaiyong, Xingquan Zhu

{"title":"Network Analysis of Technology Stocks using Market Correlation","authors":"L. Chmielewski, Rafina Amin, Anak Wannaphaschaiyong, Xingquan Zhu","doi":"10.1109/ICBK50248.2020.00046","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00046","url":null,"abstract":"In this paper, we propose to use network approaches to analyze correlation between stocks. Our essential goal is to directly answer four questions: (1) how stocks in certain industry sector are correlated to each other’ (2) what are the characteristics of stock networks with respect to their market behavioral correlations, and (3) do stocks in an industry sector form meaningful groups, based on on their market behaviors based correlations, and (4) how robust a correlation based network analysis approach can be used to understand stocks as a graph. In order to provide clear answers to address the above questions, we used market correlation methods to generate stock graphs. Two community detection methods, Louvain Modularity and Walk Trap, were used to study the structure of the graphs. To further test the robustness of our model, we created another graph using different correlation threshold. In the experiment we detected twelve communities using Louvain Modularity method and they consisted of stocks from different industries. Even the smallest cluster, which included only 2-3 stocks contained stocks from different industries.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"32 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":"127267421","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

Scientific Workflow Recommendation Based on Service Knowledge Graph 基于服务知识图的科学工作流推荐

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00040

Jin Diao, Zhangbing Zhou

{"title":"Scientific Workflow Recommendation Based on Service Knowledge Graph","authors":"Jin Diao, Zhangbing Zhou","doi":"10.1109/ICBK50248.2020.00040","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00040","url":null,"abstract":"With the dramatically increasing emerging of external Web services, automatically creating workflows to satisfy the sophisticated requirements of users has become a significant issue. Most scientific workflow recommendation focus on mining association patterns between services in historical portfolios, including positive and negative rules, and recommending appropriate workflows based on derived patterns. However, due to the development of social network, several key social interactions are ignored which can enrich implicit associations of items and guide workflow recommendation. To tackle these problems, a Service Social Knowledge Graph (SSKG), including two types of entities service and developer and three types of relations isInk, isDlp and isFrd, is proposed to visually integrate and manage vital information which can facilitate workflows construction. Respectively, isInk shows the data flow between services, isDlp means the relation between a developer and his services and isFrd presents the friend relationships between developers. SSKG supplies indirect relations of services which inferred from isDlp and isFrnd. From SSKG, we extract several positive and negative rules to estimate the feasibility of composing services that the positive rules promote service composition and negative rules hinder the cooperation of services. According to the overall effects of rules, the $A^{*}$ and the Yen’s method are used to recommend workflows to users. We have conducted extensive experiments with real-world data. Results indicate that the accuracy and efficiency of our proposed method outperform the classical and state-of-the-art methods.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"128 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":"126876699","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

Emotion Recognition from Facial Expressions and Contactless Heart Rate Using Knowledge Graph 基于知识图谱的面部表情情感识别与非接触式心率

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00019

Wenying Yu, Shuai Ding, Zijie Yue, Shanlin Yang

{"title":"Emotion Recognition from Facial Expressions and Contactless Heart Rate Using Knowledge Graph","authors":"Wenying Yu, Shuai Ding, Zijie Yue, Shanlin Yang","doi":"10.1109/ICBK50248.2020.00019","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00019","url":null,"abstract":"The application of the knowledge graph in computer vision is a new trend in deep learning. Facial video-based emotion analysis and recognition are critical topics of research in the mental healthcare field. In this paper, we proposed a novel noncontact intelligent framework to represent the knowledge of facial features and heart rate (HR) features for predicting the emotional states of objects. The framework is divided into two parts: knowledge modeling and knowledge reasoning. In the first step of knowledge modeling, 3D-CNN is utilized to model the spatiotemporal information from the facial and forehead regions based on the remote photoplethysmography technique, separating the blood volume pulse (BVP) signal and extracting the HR from the forehead image sequence. Finally, the multichannel features are integrated and transformed into structured data and put into the knowledge graph as much as possible. Knowledge reasoning is an inferential process that associates the deep learning model with structured knowledge to predict continuous values of the emotional dimensions (pleasure, arousal, and dominance) from facial videos of subjects. Experiments conducted on the DEAP database demonstrate that this approach leads to improved emotion recognition performance and significantly outperforms recent state-of-the-art proposals. The result proved that prior knowledge from the knowledge graph ground truth on deep learning is an efficient means of emotional recognition in vision modality. Our artificial intelligence models can be popularized and applied in daily healthcare.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"2018 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120847156","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

Enhancing Knowledge Graph Embedding with Relational Constraints 利用关系约束增强知识图嵌入

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00015

Mingda Li, Zhengya Sun, Siheng Zhang, Wensheng Zhang

{"title":"Enhancing Knowledge Graph Embedding with Relational Constraints","authors":"Mingda Li, Zhengya Sun, Siheng Zhang, Wensheng Zhang","doi":"10.1109/ICBK50248.2020.00015","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00015","url":null,"abstract":"Knowledge graph embedding is studied to embed entities and relations of a knowledge graph into continuous vector spaces, which benefits a variety of real-world applications. Among existing solutions, translation-based models, which employ geometric translation to design score function, have drawn much attention. However, these models primarily concentrate on evidence from observing whether the triplets are plausible, and ignore the fact that the relation also implies certain semantic constraints on its subject or object entity. In this paper, we present a general framework for enhancing knowledge graph embedding with relational constraints (KRC). Specifically, we elaborately design the score function by encoding regularities between a relation and its arguments into the translation-based embedding space. Additionally, we propose a soft margin-based ranking loss for effectively training the KRC model, which characterizes different semantic distances between negative and positive triplets. Furthermore, we combine regularities with distributional representations to predict the missing triplets, which possesses certain robust guarantee. We evaluate our method on the task of knowledge graph completion. Extensive experiments show that KRC achieves substantial improvements against baselines.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"74 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":"127371720","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: 11