Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics最新文献_第4页

Data Management in Japanese Planetary Explorations for Big Data Era 大数据时代下日本行星探索的数据管理

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405970

Yukio Yamamoto, H. Ishikawa

引用次数: 0

Classifying News Media Coverage for Corruption Risks Management with Deep Learning and Web Intelligence 基于深度学习和网络智能的腐败风险管理新闻报道分类

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405988

A. Weichselbraun, Sandro Hörler, Christian Hauser, Anina Havelka

{"title":"Classifying News Media Coverage for Corruption Risks Management with Deep Learning and Web Intelligence","authors":"A. Weichselbraun, Sandro Hörler, Christian Hauser, Anina Havelka","doi":"10.1145/3405962.3405988","DOIUrl":"https://doi.org/10.1145/3405962.3405988","url":null,"abstract":"A substantial number of international corporations have been affected by corruption. The research presented in this paper introduces the Integrity Risks Monitor, an analytics dashboard that applies Web Intelligence and Deep Learning to english and german-speaking documents for the task of (i) tracking and visualizing past corruption management gaps and their respective impacts, (ii) understanding present and past integrity issues, (iii) supporting companies in analyzing news media for identifying and mitigating integrity risks. Afterwards, we discuss the design, implementation, training and evaluation of classification components capable of identifying English documents covering the integrity topic of corruption. Domain experts created a gold standard dataset compiled from Anglo-American media coverage on corruption cases that has been used for training and evaluating the classifier. The experiments performed to evaluate the classifiers draw upon popular algorithms used for text classification such as Naïve Bayes, Support Vector Machines (SVM) and Deep Learning architectures (LSTM, BiLSTM, CNN) that draw upon different word embeddings and document representations. They also demonstrate that although classical machine learning approaches such as Naïve Bayes struggle with the diversity of the media coverage on corruption, state-of-the art Deep Learning models perform sufficiently well in the project's context.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125024128","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 Holistic Approach for Semantic Interpretation of Relational Web tables 关系型Web表语义解释的整体方法

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405986

Samia Knani, N. Y. Ayadi

{"title":"A Holistic Approach for Semantic Interpretation of Relational Web tables","authors":"Samia Knani, N. Y. Ayadi","doi":"10.1145/3405962.3405986","DOIUrl":"https://doi.org/10.1145/3405962.3405986","url":null,"abstract":"The Web contains vast amounts of semi-structured data in the form of HTML tables found on Web pages which may serve for various applications. One prominent application, which is often referred to Semantic Table Interpretation, is to exploit the semantics of a widely recognized knowledge bases (KB) by matching tabular data, including column headers and cell contents, to semantically rich descriptions of classes, entities and properties in Web KBs. In this paper, we focus on relational tables which are valuable sources of facts about real-world entities (persons, locations, organizations, etc.) and we propose a robust and efficient approach for bridging the gap between millions of Web tables and large-scale Knowledge graphs such as DBpedia. Our approach is holistic and fully unsupervised for semantic interpretation of Web tables based on the DBpedia Knowledge graph. Our approach covers three phases that heavily rely on word and entity pre-trained embeddings to uncover semantics of Web tables. Our experimental evaluation is conducted using the T2D gold standard corpus. Our results are very promising compared to several existing approaches of annotation in web tables.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129466067","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

BREXIT Election: Forecasting a Conservative Party Victory through the Pound using ARIMA and Facebook's Prophet 英国脱欧选举:使用ARIMA和Facebook的Prophet通过英镑预测保守党获胜

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405967

J. Usher, Pierpaolo Dondio

{"title":"BREXIT Election: Forecasting a Conservative Party Victory through the Pound using ARIMA and Facebook's Prophet","authors":"J. Usher, Pierpaolo Dondio","doi":"10.1145/3405962.3405967","DOIUrl":"https://doi.org/10.1145/3405962.3405967","url":null,"abstract":"On the 30th October, 2019, the markets watched as British Prime Minister, Boris Johnson, took a massive political gamble to call a general election to break the Withdrawal Agreement stalemate in the House of Commons to \"Get BREXIT Done\". The pound had been politically sensitive owing to BREXIT uncertainty. With the polls indicating a Conservative win on 4th December, 2019, the margin of victory could be observed through increases in the pound. The outcome of a Conservative party victory would benefit the pound by removing the current market turbulence. We look to provide a short-term forecast of the pound. Our approach focuses on modelling the GBP/EUR and GBP/USD Fx from the inception of BREXIT referendum talks from the 1st January, 2016 to the conclusion of the BREXIT election on the 12th December, 2019, focusing on forecasted increases in the pound from the 4th December, 2019. We construct two machine learning models in the form of an Auto Regressive Integrated Moving Average (ARIMA) financial time series and an additive regression financial time series using Facebook's Prophet to investigate the hypothesis that the polls prediction of a Conservative victory could be validated by forecasted increases in the pound. The efficiency of the forecasted models was then tested based on MAPE and MSE criteria. Our results found that the ARIMA and Prophet models were effective and proficient in forecasting the polls prediction on the 4th December, 2019 of a Conservative win by validation of forecasted increases in the pound. The ARIMA (4,1,0) model resulted in forecasts with the lowest MAPE and MAE.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114984302","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: 8

Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision 基于注意力的远程监督情感态度提取神经网络

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-23 DOI: 10.1145/3405962.3405985

Nicolay Rusnachenko, Natalia V. Loukachevitch

引用次数: 1