Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics最新文献_第4页

Crowdsourcing worker development based on probabilistic task network 基于概率任务网络的众包工人开发

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106501

Masayuki Ashikawa, Takahiro Kawamura, Akihiko Ohsuga

引用次数: 2

Large-scale taxonomy induction using entity and word embeddings 使用实体和词嵌入的大规模分类归纳

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106465

Petar Ristoski, Stefano Faralli, Simone Paolo Ponzetto, Heiko Paulheim

引用次数: 22

Affective prediction by collaborative chains in movie recommendation 协同链在电影推荐中的情感预测

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106535

Yong Zheng

引用次数: 6

LCHI: multiple, overlapping local communities LCHI:多个重叠的当地社区

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106438

Moeen Farasat, J. Scripps

引用次数: 0

Context suggestion: empirical evaluations vs user studies 背景建议:经验评价vs用户研究

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106466

Yong Zheng

引用次数: 1

Large-scale readability analysis of privacy policies 隐私政策的大规模可读性分析

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106427

Benjamin Fabian, Tatiana Ermakova, Tino Lentz

{"title":"Large-scale readability analysis of privacy policies","authors":"Benjamin Fabian, Tatiana Ermakova, Tino Lentz","doi":"10.1145/3106426.3106427","DOIUrl":"https://doi.org/10.1145/3106426.3106427","url":null,"abstract":"Online privacy policies notify users of a Website how their personal information is collected, processed and stored. Against the background of rising privacy concerns, privacy policies seem to represent an influential instrument for increasing customer trust and loyalty. However, in practice, consumers seem to actually read privacy policies only in rare cases, possibly reflecting the common assumption stating that policies are hard to comprehend. By designing and implementing an automated extraction and readability analysis toolset that embodies a diversity of established readability measures, we present the first large-scale study that provides current empirical evidence on the readability of nearly 50,000 privacy policies of popular English-speaking Websites. The results empirically confirm that on average, current privacy policies are still hard to read. Furthermore, this study presents new theoretical insights for readability research, in particular, to what extent practical readability measures are correlated. Specifically, it shows the redundancy of several well-established readability metrics such as SMOG, RIX, LIX, GFI, FKG, ARI, and FRES, thus easing future choice making processes and comparisons between readability studies, as well as calling for research towards a readability measures framework. Moreover, a more sophisticated privacy policy extractor and analyzer as well as a solid policy text corpus for further research are provided.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82807438","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: 83

Entity oriented action recommendations for actionable knowledge graph generation 面向实体的可操作知识图谱生成的行动建议

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106546

Md. Mostafizur Rahman, A. Takasu

{"title":"Entity oriented action recommendations for actionable knowledge graph generation","authors":"Md. Mostafizur Rahman, A. Takasu","doi":"10.1145/3106426.3106546","DOIUrl":"https://doi.org/10.1145/3106426.3106546","url":null,"abstract":"Popular search engines have recently utilized the power of knowledge graphs (KGs) to provide specific answers to queries in a direct way. Search engine result pages (SERPs) are expected to provide facts in response to queries that satisfy semantic meaning. This encourages researchers to propose more influential knowledge graph generation techniques. To achieve and advance the technologies related to actionable knowledge graph presentation, creating action recommendations (ARs) is an essential step and a relatively new research direction to nurture research on generating KGs that are optimized for facilitating an entity's actions. An action represents the physical or mental activity of an entity. For example, for the entity \"Donald J. Trump\", typical potential actions could be \"won the US presidential election\" or \"targets US journalists\". In this paper, we describe the generation of relevant action recommendations based on entity instance and entity type. We propose two models that employ different approaches. Our first model exploits semisupervised learning and we introduce entity context vector (ECV) as an entity's distinguishing features for capturing the context of entities to reveal the similarity between entities, grounded on the prominent word2vec model. The second model is a probabilistic approach based on the Naive Bayes Theorem. We extensively evaluate our proposed models. Our first model significantly outperforms probabilistic and supervised learning-based models.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80982480","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

Zero-shot human activity recognition via nonlinear compatibility based method 基于非线性兼容的零射击人体活动识别方法

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106526

Wei Wang, C. Miao, Shuji Hao

引用次数: 17

CEDAL: time-efficient detection of erroneous links in large-scale link repositories CEDAL:在大规模链接存储库中高效地检测错误链接

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106497

André Valdestilhas, Tommaso Soru, A. N. Ngomo

{"title":"CEDAL: time-efficient detection of erroneous links in large-scale link repositories","authors":"André Valdestilhas, Tommaso Soru, A. N. Ngomo","doi":"10.1145/3106426.3106497","DOIUrl":"https://doi.org/10.1145/3106426.3106497","url":null,"abstract":"More than 500 million facts on the Linked Data Web are statements across knowledge bases. These links are of crucial importance for the Linked Data Web as they make a large number of tasks possible, including cross-ontology, question answering and federated queries. However, a large number of these links are erroneous and can thus lead to these applications producing absurd results. We present a time-efficient and complete approach for the detection of erroneous links for properties that are transitive. To this end, we make use of the semantics of URIs on the Data Web and combine it with an efficient graph partitioning algorithm. We then apply our algorithm to the LinkLion repository and show that we can analyze 19,200,114 links in 4.6 minutes. Our results show that at least 13% of the owl :sameAs links we considered are erroneous. In addition, our analysis of the provenance of links allows discovering agents and knowledge bases that commonly display poor linking. Our algorithm can be easily executed in parallel and on a GPU. We show that these implementations are up to two orders of magnitude faster than classical reasoners and a non-parallel implementation.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76784114","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: 10

Inferring win-lose product network from user behavior 从用户行为推断产品的输赢网络

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106502

S. Iitsuka, Kazuya Kawakami, S. Hagiwara, T. Kawakami, Takayuki Hamada, Y. Matsuo

{"title":"Inferring win-lose product network from user behavior","authors":"S. Iitsuka, Kazuya Kawakami, S. Hagiwara, T. Kawakami, Takayuki Hamada, Y. Matsuo","doi":"10.1145/3106426.3106502","DOIUrl":"https://doi.org/10.1145/3106426.3106502","url":null,"abstract":"Various data mining techniques to extract product relations have been examined, especially in the context of building intelligent recommender systems. Most such techniques, however, specifically examine co-occurrences of browsed or purchased products on e-commerce websites, which provide little or no useful information related to the direct relation of superiority or the factor which forms that superiority. For marketers and product managers, understanding the competitive advantages of a given product is important to consolidate their product differentiation strategies. As described in this paper, we propose a win-lose relation, a new product relation analysis method that retrieves the superiority relation between competitive products in terms of product attractiveness. Our proposed method uses the difference between user browsing and purchasing behaviors, assuming that a purchased product is superior to products that are browsed but not purchased. We also propose superiority factor analysis to examine keywords that represent the superiority factor by mining product reviews. We evaluate our methods using an actual dataset from Zexy, the largest wedding portal website in Japan. Our experimental evaluation revealed that our proposed method can estimate actual user preferences observed from a user study using only log data. Results also show that our proposed method raises the accuracy of superiority factor extraction by around 17% by considering the win-lose relation of products.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76943515","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