Companion Proceedings of the The Web Conference 2018最新文献_第8页

DARQL: Deep Analysis of SPARQL Queries DARQL: SPARQL查询的深度分析

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186975

A. Bonifati, W. Martens, Thomas Timm

{"title":"DARQL: Deep Analysis of SPARQL Queries","authors":"A. Bonifati, W. Martens, Thomas Timm","doi":"10.1145/3184558.3186975","DOIUrl":"https://doi.org/10.1145/3184558.3186975","url":null,"abstract":"In this demonstration, we showcase DARQL, the first tool for deep, large-scale analysis of SPARQL queries. We have harvested a large corpus of query logs with different lineage and sizes, from DBPedia to BioPortal and Wikidata, whose total number of queries amounts to 180M. We ran a wide range of analyses on the corpus, spanning from simple tasks (keyword counts, triple counts, operator distributions), moderately deep tasks (projection test, query classification), and deep analysis (shape analysis, well-designedness, weakly well-designedness, hypertreewidth, and fractional edge cover). The key goal of our demonstration is to let the users dive into the SPARQL query logs of our corpus and let them discover the inherent characteristics of the queries. The entire corpus of SPARQL queries is stored in a DBMS. The tool has a GUI that allows users to ask sophisticated analytical queries on the SPARQL logs. These analytical queries can both be directly written in SQL or composed by a visual query builder tool. The results of the analytical queries are represented both textually (as SPARQL queries) and visually. The DBMS performs the searches within the corpus quite efficiently. To the best of our knowledge, this is the first demonstration of this kind on such a large corpus and with such a number of varied tests.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128587028","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

Tink: A Temporal Graph Analytics Library for Apache Flink Tink:一个用于Apache Flink的时序图分析库

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186934

Wouter Ligtenberg, Yulong Pei, G. Fletcher, Mykola Pechenizkiy

引用次数: 17

The Quantum Collective 量子集体

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191550

Lora Aroyo, Chris Welty

{"title":"The Quantum Collective","authors":"Lora Aroyo, Chris Welty","doi":"10.1145/3184558.3191550","DOIUrl":"https://doi.org/10.1145/3184558.3191550","url":null,"abstract":"AI and collective intelligence systems universally suffer from a deficiency of context. There are innumerable possible contexts that may possibly change the interpretation of some signal, that may change the proper response to some stimulus. For example, an image understanding system that does not recognize an arrest event in a zoomed image of a person's face. How is it possible to know there is more information, outside of what the system can access, that affects the interpretation of data The solution to the context problem in practice today is a pragmatic, engineering one: analyze errors (in recommendations, question answers, image recognition, etc.), classify the kinds of contextual information that caused the wrong behavior, find the most common type of context that causes errors, and add information about that kind of context to the system. Clearly this approach is neither general nor scalable, and ignores the infamous long tail of possible contextual information that may affect a system's understanding and its behavior. In this paper we outline a new, more general, approach to recognizing context. The approach is grounded in a fairly simple intuition: the mathematics underlying quantum mechanics is far more appropriate for modeling, and therefore simulating, human cognitive behavior than the standard toolset from classical statistics. Notions such as Heisenberg's uncertainty principle, superpositions of states, and entanglement have direct and measurable analogs in collective intelligence.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116003400","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

Detection of Stress and Relaxation Magnitudes for Tweets 推文的应力和松弛大小检测

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191627

Reshmi Gopalakrishna Pillai, M. Thelwall, Constantin Orasan

{"title":"Detection of Stress and Relaxation Magnitudes for Tweets","authors":"Reshmi Gopalakrishna Pillai, M. Thelwall, Constantin Orasan","doi":"10.1145/3184558.3191627","DOIUrl":"https://doi.org/10.1145/3184558.3191627","url":null,"abstract":"The ability to automatically detect human stress and relaxation is crucial for timely diagnosing stress-related diseases, ensuring customer satisfaction in services and managing human-centric applications such as traffic management. Traditional methods employ stress-measuring scales or physiological monitoring which may be intrusive and inconvenient. Instead, the ubiquitous nature of the social media can be leveraged to identify stress and relaxation, since many people habitually share their recent life experiences through social networking sites. This paper introduces an improved method to detect expressions of stress and relaxation in social media content. It uses word sense disambiguation by word sense vectors to improve the performance of the first and only lexicon-based stress/relaxation detection algorithm TensiStrength. Experimental results show that incorporating word sense disambiguation substantially improves the performance of the original TensiStrength. It performs better than state-of-the-art machine learning methods too in terms of Pearson correlation and percentage of exact matches. We also propose a novel framework for identifying the causal agents of stress and relaxation in tweets as future work.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121218404","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: 29

A Feasibility Study on Crowdsourcing to Monitor Municipal Resources in Smart Cities 智慧城市中众包监控市政资源的可行性研究

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191519

Thivya Kandappu, Archan Misra, Desmond Koh, Randy Tandriansyah, Nikita Jaiman

引用次数: 8

DL-Learner Structured Machine Learning on Semantic Web Data 基于语义Web数据的DL-Learner结构化机器学习

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186235

Lorenz Bühmann, Jens Lehmann, Patrick Westphal, Simon Bin

引用次数: 27

Analyzing and Predicting Emoji Usages in Social Media 分析和预测社交媒体中表情符号的使用

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186344

Peijun Zhao, Jia Jia, Yongsheng An, Jie Liang, Lexing Xie, Jiebo Luo

{"title":"Analyzing and Predicting Emoji Usages in Social Media","authors":"Peijun Zhao, Jia Jia, Yongsheng An, Jie Liang, Lexing Xie, Jiebo Luo","doi":"10.1145/3184558.3186344","DOIUrl":"https://doi.org/10.1145/3184558.3186344","url":null,"abstract":"Emojis can be regarded as a language for graphical expression of emotions, and have been widely used in social media. They can express more delicate feelings beyond textual information and improve the effectiveness of computer-mediated communication. Recent advances in machine learning make it possible to automatic compose text messages with emojis. However, the usages of emojis can be complicated and subtle so that analyzing and predicting emojis is a challenging problem. In this paper, we first construct a benchmark dataset of emojis with tweets and systematically investigate emoji usages in terms of tweet content, tweet structure and user demographics. Inspired by the investigation results, we further propose a multitask multimodality gated recurrent unit (mmGRU) model to predict the categories and positions of emojis. The model leverages not only multimodality information such as text, image and user demographics, but also the strong correlations between emoji categories and their positions. Our experimental results show that the proposed method can significantly improve the accuracy for predicting emojis for tweets (+9.0% in F1-value for category and +4.6% in F1-value for position). Based on the experimental results, we further conduct a series of case studies to unveil how emojis are used in social media.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127167544","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: 25

Hybrid.AI: A Learning Search Engine for Large-scale Structured Data 混合动力车。AI:大规模结构化数据的学习搜索引擎

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191600

Sean Soderman, Anusha Kola, Maksim Podkorytov, Michaela Geyer, M. Gubanov

引用次数: 9

A User-Centric Diversity by Design Recommender System for the Movie Application Domain 电影应用领域以用户为中心的多样性设计推荐系统

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191580

Michele Zanitti, Sokol Kosta, J. Sørensen

引用次数: 14

Trusternity: Auditing Transparent Log Server with Blockchain 信任:用区块链审计透明日志服务器

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186938

H. L. Nguyen, C. Ignat, O. Perrin

引用次数: 11