Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology最新文献_第10页

Linked Data Ground Truth for Quantitative and Qualitative Evaluation of Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs 关联数据基础真值定量和定性评价关系图解释卷积网络知识图链接预测

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493921

Nicholas F Halliwell, Fabien L. Gandon, F. Lécué

{"title":"Linked Data Ground Truth for Quantitative and Qualitative Evaluation of Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs","authors":"Nicholas F Halliwell, Fabien L. Gandon, F. Lécué","doi":"10.1145/3486622.3493921","DOIUrl":"https://doi.org/10.1145/3486622.3493921","url":null,"abstract":"Relational Graph Convolutional Networks (RGCNs) identify relationships within a Knowledge Graph to learn real-valued embeddings for each node and edge. Recently, researchers have proposed explanation methods to interpret the predictions of these black-box models. However, comparisons across explanation methods for link prediction remains difficult, as there is neither a method nor dataset to compare explanations against. Furthermore, there exists no standard evaluation metric to identify when one explanation method is preferable to the other. In this paper, we leverage linked data to propose a method, including two datasets (Royalty-20k, and Royalty-30k), to benchmark explanation methods on the task of explainable link prediction using Graph Neural Networks. In particular, we rely on the Semantic Web to construct explanations, ensuring that each predictable triple has an associated set of triples providing a ground truth explanation. Additionally, we propose the use of a scoring metric for empirically evaluating explanation methods, allowing for a quantitative comparison. We benchmark these datasets on state-of-the-art link prediction explanation methods using the defined scoring metric, and quantify the different types of errors made with respect to both data and semantics.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80595145","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: 5

Analyzing Vaccination Priority Judgments for 132 Occupations Using Word Vector Models 利用词向量模型分析132个职业的疫苗接种优先级判断

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3498933

Atsushi Ueshima, Hiroki Takikawa

引用次数: 0

Handling Imbalance in Fraudulent Reviewer Detection based on Expectation Maximization and KL Divergence 基于期望最大化和KL散度的欺诈审稿人检测失衡处理

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3498989

Wen Zhang, Guan-Shi Qin, Qiang Wang

{"title":"Handling Imbalance in Fraudulent Reviewer Detection based on Expectation Maximization and KL Divergence","authors":"Wen Zhang, Guan-Shi Qin, Qiang Wang","doi":"10.1145/3498851.3498989","DOIUrl":"https://doi.org/10.1145/3498851.3498989","url":null,"abstract":"Online review fraud and review manipulation hurt the profits of stakeholders and undermine the value of online reviews. For this reason, it is critical to detect online review fraud and fraudulent reviewers effectively for the development of e-commerce. Extent studies propose various fraud detection techniques to detect fraudulent reviewers. However, most of these studies do not handle the data imbalance problem in fraudulent reviewer detection. To fill this research gap, this paper proposes a novel approach to detect fraudulent reviewers in handling the data imbalance based on Expectation Maximization (EM) and Kullback–Leibler (KL) divergence (called EMKL). We first use the expectation maximization algorithm to model the latent topic distributions of reviewers on the review features. Then, we adopt the Kullback–Leibler divergence to measure the similarities of reviewers based on their topic distributions to detect fraudulent reviewers. The experiment on Yelp dataset shows that the EMKL approach has a good performance in detecting fraudulent reviewers. In addition, the proposed EMKL method performs better than the performance of state-of-the-art techniques.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81549415","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

A Framework for Duplicate Detection from Online Job Postings 一种在线招聘信息重复检测框架

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493928

Yanchang Zhao, Haohui Chen, C. Mason

{"title":"A Framework for Duplicate Detection from Online Job Postings","authors":"Yanchang Zhao, Haohui Chen, C. Mason","doi":"10.1145/3486622.3493928","DOIUrl":"https://doi.org/10.1145/3486622.3493928","url":null,"abstract":"Online job boards have greatly improved the efficiency of job searching and have also provided valuable data for labour market research. However, there are a high proportion of duplicate job postings in most (if not all) job boards, because recruiters and job boards seek to improve their coverage of the market by integrating job postings from many different sources. These duplicate postings undermine the usability of job boards and the quality of labour market analytics derived from them. In this paper, we tackle the challenging problem of duplicate detection from online job postings. Specifically, we design a framework for duplicate detection from online job postings and, under the framework, implement and test 24 methods built with four different tokenisers, three vectorisers and six similarity measures. We conduct a comparative study and experimental evaluation of the 24 methods and compare their performance with a baseline approach. All methods are tested with a real-world dataset from a job boarding platform and are evaluated with six performance metrics. The experiment reveals that the top two methods are Overlap with skip-gram (OS) and Overlap with n-gram (OG), followed by TFIDF-cosine with n-gram (TCG) and TFIDF-cosine with skip-gram (TCS), and that all above four methods outperform the baseline approach in detecting duplicates.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81958882","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: 5

Gated Character-aware Convolutional Neural Network for Effective Automated Essay Scoring 门控字符感知卷积神经网络的有效自动作文评分

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493945

Huanyu Bai, Zhilin Huang, Anran Hao, S. Hui

{"title":"Gated Character-aware Convolutional Neural Network for Effective Automated Essay Scoring","authors":"Huanyu Bai, Zhilin Huang, Anran Hao, S. Hui","doi":"10.1145/3486622.3493945","DOIUrl":"https://doi.org/10.1145/3486622.3493945","url":null,"abstract":"Automated Essay Scoring (AES) is a challenging topic in Natural Language Processing. Many current state-of-the-art approaches are based on deep learning models. However, most AES models overlook the importance of character-level information, which is important to both the performance and the fairness. The character-level information is able to provide orthographic knowledge (e.g., spelling) and help the learning of infrequent and ⟨UNK⟩ tokens. In this paper, we propose a Gated Character-aware Convolutional Neural Network (GCCNN) model for the AES task. The proposed GCCNN model incorporates character-level information by a character-level encoder and a gated fusion mechanism. First, the character-level encoder learns word embeddings from sequences of characters by a hierarchical convolutional neural network. Next, the gated fusion mechanism adaptively controls the amount of word-level and character-level information to be fused using vector gating. Then, the essay-level encoder learns an essay representation based on the fused word embeddings. Finally, the fully connected layer maps the essay representation into its corresponding score. The experimental results show that our GCCNN model outperforms the baseline deep learning models. In addition, our qualitative analysis also demonstrates the importance of character-level information for tackling the out-of-vocabulary problem in grading essays.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82027716","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

Producing Artificial Male Voices with Maternal Features for Relaxation 制造带有母性特征的人造男声来放松

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3498964

Valentina Franzoni, Alina Elena Baia, Giulio Biondi, A. Milani

引用次数: 0

Neural text generation for query expansion in information retrieval 面向信息检索查询扩展的神经文本生成

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493957

V. Claveau

引用次数: 11

Relation Extraction with Sentence Simplification Process and Entity Information 基于句子简化过程和实体信息的关系提取

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3494010

M. Parniani, M. Reformat

{"title":"Relation Extraction with Sentence Simplification Process and Entity Information","authors":"M. Parniani, M. Reformat","doi":"10.1145/3486622.3494010","DOIUrl":"https://doi.org/10.1145/3486622.3494010","url":null,"abstract":"Graph-based Knowledge Bases (KBs) are composed of relational facts that can be perceived as two entities, called head and tail, linked via a relation. Processes of constructing KBs, i.e., populating them with such facts, as well as revising and updating them are of special interest. These should be performed automatically, especially in the case when the main sources of facts are textual documents. For this reason, a task of Relation Extraction (RE), i.e., predicting a relation that links two entities mentioned in a sentence, is one of the most important activities. Using RE processes, new relational facts can be extracted, and KBs can be built and updated using unstructured information. In this paper, we propose a novel procedure for RE. It is based on a sentence distilling technique that works on dependency trees and removes noisy tokens from sentences while preserving the most relevant and useful ones. In addition, the proposed procedure utilizes information about types of linked entities, it means types of relations’ heads and tails. Our neural network model using processed and new input information is evaluated on the widely used NYT dataset and compared to other state-of-the-art RE methods. Experimental results show the effectiveness of the proposed procedure against other methods.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85383600","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

Hamiltonian Properties of Bidimensional Compound Networks 二维复合网络的哈密顿性质

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3499003

Xiaoyu Du, Keke Qin, Zhijie Han, Shuai Ding

引用次数: 0

Analysis of Leading Communities Contributing to arXiv Information Distribution on Twitter 推特上arXiv信息分发的主要社区分析

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493947

Kyosuke Shimada, K. Kazama, Mitsuo Yoshida, Ikki Ohmukai, Sho Sato

{"title":"Analysis of Leading Communities Contributing to arXiv Information Distribution on Twitter","authors":"Kyosuke Shimada, K. Kazama, Mitsuo Yoshida, Ikki Ohmukai, Sho Sato","doi":"10.1145/3486622.3493947","DOIUrl":"https://doi.org/10.1145/3486622.3493947","url":null,"abstract":"To analyze the impact that arXiv is having on the world, in this paper we propose an arXiv information distribution model on Twitter, which has a three-layer structure: arXiv papers, information spreaders, and information collectors. First, we use the HITS algorithm to analyze the arXiv information diffusion network with users as nodes, which is created from three types of behavior on Twitter regarding arXiv papers: tweeting, retweeting, and liking. Next, we extract communities from the network of information spreaders with positive authority and hub degrees using the Louvain method, and analyze the relationship and roles of information spreaders in communities using research field, linguistic, and temporal characteristics. From our analysis using the tweet and arXiv datasets, we found that information about arXiv papers circulates on Twitter from information spreaders to information collectors, and that multiple communities of information spreaders are formed according to their research fields. It was also found that different communities were formed in the same research field, depending on the research or cultural background of the information spreaders. We were able to identify two types of key persons: information spreaders who lead the relevant field in the international community and information spreaders who bridge the regional and international communities using English and their native language. In addition, we found that it takes some time to gain trust as an information spreader.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80225404","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