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Companion Proceedings of the The Web Conference 2018最新文献

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A journey from the Physical Web to the Physical Semantic Web 从物理网到物理语义网的旅程
Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186981
M. Ruta, F. Scioscia, G. Loseto, F. Gramegna, S. Ieva, A. Pinto, E. Sciascio
{"title":"A journey from the Physical Web to the Physical Semantic Web","authors":"M. Ruta, F. Scioscia, G. Loseto, F. Gramegna, S. Ieva, A. Pinto, E. Sciascio","doi":"10.1145/3184558.3186981","DOIUrl":"https://doi.org/10.1145/3184558.3186981","url":null,"abstract":"ThePhysical Semantic Web (PSW) is a novel paradigm built upon the Google Physical Web (PW) approach and devoted to improve the quality of interactions in the Web of Things. Beacons expose semantic annotations instead of basic identifiers, ıe machine-understandable descriptions of physical resources. This enables novel ontology-based object advertisement and discovery and --in turn-- advanced user-to-thing and autonomous thing-to-thing interactions. The demo shows the evolution from the PW to the PSW in a discovery scenario set in a winery, where bottles are equipped with Bluetooth Low Energy beacons and a customer can discover them using her smartphone. The final goal is to prove benefits of PSW over basic PW, including: rich semantic-based object annotation; dynamic annotations exploiting on-board sensors; enhanced discovery and ranking of nearby objects through semantic matchmaking; availability of interactions even without working Internet infrastructure, by means of point-to-point data exchanges.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"235 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":"122448135","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
FAUST Domain Specific Audio DSP Language Compiled to WebAssembly FAUST领域特定音频DSP语言编译成WebAssembly
Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3185970
S. Letz, Y. Orlarey, D. Fober
{"title":"FAUST Domain Specific Audio DSP Language Compiled to WebAssembly","authors":"S. Letz, Y. Orlarey, D. Fober","doi":"10.1145/3184558.3185970","DOIUrl":"https://doi.org/10.1145/3184558.3185970","url":null,"abstract":"beginabstract This paper demonstrates how FAUST, a functional programming language for sound synthesis and audio processing, can be used to develop efficient audio code for the Web. After a brief overview of the language, its compiler and the architecture system allowing to deploy the same program as a variety of targets, the generation of WebAssembly code and the deployment of specialized WebAudio nodes will be explained. Several use cases will be presented. Extensive benchmarks to compare the performance of native and WebAssembly versions of the same set of DSP have be done and will be commented. endabstract","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"71 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":"121885228","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
Increasing Transparency through Web Maps 通过网络地图增加透明度
Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191515
Auriol Degbelo, Tomi Kauppinen
{"title":"Increasing Transparency through Web Maps","authors":"Auriol Degbelo, Tomi Kauppinen","doi":"10.1145/3184558.3191515","DOIUrl":"https://doi.org/10.1145/3184558.3191515","url":null,"abstract":"Recent years have witnessed progress of public institutions in making their datasets available online, free of charge, for re-use. This notwithstanding, there is still a long way to go to put the power of data in the hands of citizens. This article suggests that transparency in the context of open government can be increased through web maps featuring: i) Application Programming Interfaces (APIs) which support app and data usage tracking; and (ii) 'transparency badges' which inform the users about the presence/absence of extra, useful contextual information. Eight examples of web maps are introduced as proof of concept for the idea. Designing and implementing these web maps has reminded of the need of interactive guidelines to help non-experts select vocabularies, and datasets to link to. The ideas presented are relevant to making existing open data more user friendly (and ultimately more usable).","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"27 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":"128246493","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: 14
SmartPub: A Platform for Long-Tail Entity Extraction from Scientific Publications SmartPub:从科学出版物中提取长尾实体的平台
Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186976
S. Mesbah, A. Bozzon, C. Lofi, G. Houben
{"title":"SmartPub: A Platform for Long-Tail Entity Extraction from Scientific Publications","authors":"S. Mesbah, A. Bozzon, C. Lofi, G. Houben","doi":"10.1145/3184558.3186976","DOIUrl":"https://doi.org/10.1145/3184558.3186976","url":null,"abstract":"This demo presents SmartPub, a novel web-based platform that supports the exploration and visualization of shallow meta-data (e.g., author list, keywords) and deep meta-data--long tail named entities which are rare, and often relevant only in specific knowledge domain--from scientific publications. The platform collects documents from different sources (e.g. DBLP and Arxiv), and extracts the domain-specific named entities from the text of the publications using Named Entity Recognizers (NERs) which we can train with minimal human supervision even for rare entity types. The platform further enables the interaction with the Crowd for filtering purposes or training data generation, and provides extended visualization and exploration capabilities. SmartPub will be demonstrated using sample collection of scientific publications focusing on the computer science domain and will address the entity types Dataset (i.e. dataset presented or used in a publication), and Methods (i.e. algorithms used to create/enrich/analyse a data set)","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"1 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":"129319539","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
Do Violent People Smile: Social Media Analysis of their Profile Pictures 暴力的人会微笑吗:社交媒体对他们头像的分析
Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191594
Mauro Coletto, C. Lucchese, S. Orlando
{"title":"Do Violent People Smile: Social Media Analysis of their Profile Pictures","authors":"Mauro Coletto, C. Lucchese, S. Orlando","doi":"10.1145/3184558.3191594","DOIUrl":"https://doi.org/10.1145/3184558.3191594","url":null,"abstract":"The popularity of online social platforms has also determined the emergence of violent and abusive behaviors reflecting real life issues into the digital arena. Cyberbullying, Internet banging, pedopornography, sexting are examples of these behaviors, as witnessed in the social media environments. Several studies have shown how to approximately detect those behaviors by analyzing the social interactions and in particular the content of the exchanged messages. The features considered in the models basically include detection of o ensive language through NLP techniques and vocabularies, social network structural measures and, if available, user context information. Our goal is to investigate those users who adopt offensive language and hate speech in Twitter by analyzing their profile pictures. Results show that violent people smile less and they are dominating by anger, fear and sadness.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"144 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":"124611185","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
Real-time Event-based News Suggestion for Wikipedia Pages from News Streams 基于新闻流的维基百科页面的实时事件新闻建议
Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191642
Lijun Lyu, B. Fetahu
{"title":"Real-time Event-based News Suggestion for Wikipedia Pages from News Streams","authors":"Lijun Lyu, B. Fetahu","doi":"10.1145/3184558.3191642","DOIUrl":"https://doi.org/10.1145/3184558.3191642","url":null,"abstract":"Wikipedia is one of the top visited resources on the Web, furthermore, it is used extensively as the main source of information for applications like Web search, question & answering etc. This is mostly attributed to Wikipedia's coverage in terms of topics and real-world entities and the fact that Wikipedia articles are constantly updated with new and emerging facts. However, only a small fraction of articles are considered to be of good quality. The large majority of articles are incomplete and have other quality issues. A strong quality indicator is the presence of external references from third-party sources (e.g. news sources) as suggested by the verifiability principle in Wikipedia. Even for the existing references in Wikipedia there is an inherent lag in terms of the publication time of cited resources and the time they are cited in Wikipedia articles. We propose a near real-time suggestion of news references for Wikipedia from a daily news stream. We model daily news into specific events, spanning from a day up to year. Thus, we construct an event-chain from which we determine when the information in an event has converged and consequentially based on a learning-to-rank approach suggest the most authoritative and complete news article to Wikipedia articles involved in a specific event. We evaluate our news suggestion approach on a set of 41 events extracted from Wikipedia currents event portal, and on new corpus consisting of daily news between the period of 2016-2017 with more than 14 million news articles. We are able to suggest news articles to Wikipedia pages with an overall accuracy of MAP=0.77 and with a minimal lag w.r.t the publication time of the news article.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"34 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":"123643881","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
Dynamic Local Models for Online Recommendation 在线推荐的动态局部模型
Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191586
Marie Al-Ghossein, T. Abdessalem, Anthony Barré
{"title":"Dynamic Local Models for Online Recommendation","authors":"Marie Al-Ghossein, T. Abdessalem, Anthony Barré","doi":"10.1145/3184558.3191586","DOIUrl":"https://doi.org/10.1145/3184558.3191586","url":null,"abstract":"With the explosion of the volume of user-generated data, designing online recommender systems that learn from data streams has become essential. These systems rely on incremental learning that continuously update models as new observations arrive and they should be able to adapt to drifts in real-time. User preferences evolve over time and tracking their evolution is not an easy task. In addition to the low number of observations available per user, the preferences change at different moments and in different ways for each individual. In this paper, we propose a novel approach based on local models to address this problem. Local models are known for their ability to capture diverse preferences among user subsets. Our approach automatically detects the drift of preferences that leads a user to adopt a behavior closer to the users of another subset, and adjusts the models accordingly. Our experiments on real world datasets show promising results and prove the effectiveness of using local models to adapt to changes in user preferences.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"6 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":"121405273","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: 13
Transfer Learning of Artist Group Factors to Musical Genre Classification 艺术家群体因素向音乐类型分类的迁移学习
Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191823
Jaehun Kim, Minz Won, Xavier Serra, Cynthia C. S. Liem
{"title":"Transfer Learning of Artist Group Factors to Musical Genre Classification","authors":"Jaehun Kim, Minz Won, Xavier Serra, Cynthia C. S. Liem","doi":"10.1145/3184558.3191823","DOIUrl":"https://doi.org/10.1145/3184558.3191823","url":null,"abstract":"The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist labels are available for a given audio segment. Therefore, in this work, we propose to apply the transfer learning framework, learning artist-related information which will be used at inference time for genre classification. We consider different types of artist-related information, expressed through artist group factors, which will allow for more efficient learning and stronger robustness to potential label noise. Furthermore, we investigate how to achieve the highest validation accuracy on the given FMA dataset, by experimenting with various kinds of transfer methods, including single-task transfer, multi-task transfer and finally multi-task learning.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"49 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":"116367313","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: 15
Combining RDF Graph Data and Embedding Models for an Augmented Knowledge Graph 结合RDF图数据和嵌入模型的增广知识图
Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191527
A. Nikolov, P. Haase, Daniel M. Herzig, Johannes Trame, A. Kozlov
{"title":"Combining RDF Graph Data and Embedding Models for an Augmented Knowledge Graph","authors":"A. Nikolov, P. Haase, Daniel M. Herzig, Johannes Trame, A. Kozlov","doi":"10.1145/3184558.3191527","DOIUrl":"https://doi.org/10.1145/3184558.3191527","url":null,"abstract":"Vector embedding models have recently become popular for encoding both structured and unstructured data. In the context of knowledge graphs such models often serve as additional evidence supporting various tasks related to the knowledge base population: e.g., information extraction or link prediction to expand the original dataset. However, the embedding models themselves are often not used directly alongside structured data: they merely serve as additional evidence for structured knowledge extraction. In the metaphactory knowledge graph management platform, we use federated hybrid SPARQL queries for combining explicit information stated in the graph, implicit information from the associated embedding models, and information extracted using vector embeddings in a transparent way for the end user. In this paper we show how we integrated RDF data with vector space models to construct an augmented knowledge graph to be used in customer applications.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"170 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":"124143807","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
Aspect-based Financial Sentiment Analysis with Deep Neural Networks 基于方面的深度神经网络金融情绪分析
Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191825
E. Shijia, Li Yang, Mohan Zhang, Yang Xiang
{"title":"Aspect-based Financial Sentiment Analysis with Deep Neural Networks","authors":"E. Shijia, Li Yang, Mohan Zhang, Yang Xiang","doi":"10.1145/3184558.3191825","DOIUrl":"https://doi.org/10.1145/3184558.3191825","url":null,"abstract":"Aspect-based financial sentiment analysis, which aims to classify the text instance into a pre-defined aspect class and predict the sentiment score for the mentioned target. In this paper, we propose a neural network model, Attention-based LSTM model with the Aspect information (ALA), to solve the financial opinion mining problem introduced by the WWW 2018 shared task. The proposed neural network model can adapt to the financial dataset so that the neural network can effectively understand the semantic information of the short text. We evaluate our model with the 10-fold cross-validation, and we compare our model with a variety of related deep neural network models.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"41 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":"128141096","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: 15
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