Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics最新文献

Implicit Links based Web Page Representation for Web Page Classification 网页分类中基于隐式链接的网页表示

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797125

Abdelbadie Belmouhcine, M. Benkhalifa

{"title":"Implicit Links based Web Page Representation for Web Page Classification","authors":"Abdelbadie Belmouhcine, M. Benkhalifa","doi":"10.1145/2797115.2797125","DOIUrl":"https://doi.org/10.1145/2797115.2797125","url":null,"abstract":"With the rapid growth of the web's size, web page classification becomes more prominent. The representation way of a web page and contextual features used for this representation have both an impact on the classification's performance. Thus, finding an adequate representation of web pages is essential for a better web page classification. In this paper, we propose a web page representation based on the structure of the implicit graph built using implicit links extracted from the query-log. In this representation, we represent web pages using their textual contents along with their neighbors as features instead of using features of their neighbors. When two or more web pages in the implicit graph share the same direct neighbors and belong to the same class ci, it is most likely that every other web page, having the same immediate neighbors, will belong to the same class ci. We propose two kinds of web page representations: Boolean Neighbor Vector (BNV) and Weighted Neighbor Vector (WNV). In BNV, we supplement the feature vector, which represents the textual content of a web page, by a Boolean vector. This vector represents the target web page's neighbors and shows whether a web page is a direct neighbor of the target web page or not. In WNV, we supplement the feature vector, which represents the textual content of a web page, by a weighted vector. This latter represents the target web page's neighbors and shows strengths of relations between the target web page and its neighbors. We conduct experiments using four classifiers: SVM (Support Vector Machine), NB (Naive Bayes), RF (Random Forest) and KNN (K-Nearest Neighbors) on two subsets of ODP (Open Directory Project). Results show that: (1) the proposed representation helps obtain better classification results when using SVM, NB, RF and KNN for both Bag of Words (BW) and 5-gram representations. (2) The performances based on BNV are better than those based on WNV.","PeriodicalId":386229,"journal":{"name":"Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126922826","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: 6

A LOD-based, query construction and refinement service for web search engines 一个基于lod的web搜索引擎查询构建和细化服务

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797122

I. Papadakis, Ioannis Apostolatos, Dimitris Apostolou

引用次数: 1

Creating Semantic Fingerprints for Web Documents 为Web文档创建语义指纹

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797132

K. Krieger, J. Schneider, Christian Nywelt, D. Rösner

引用次数: 5

A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time schema.org词汇的采用和演变的网络尺度研究

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797124

R. Meusel, Christian Bizer, Heiko Paulheim

引用次数: 40

Matching HTML Tables to DBpedia 匹配HTML表到DBpedia

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797118

Dominique Ritze, O. Lehmberg, Christian Bizer

{"title":"Matching HTML Tables to DBpedia","authors":"Dominique Ritze, O. Lehmberg, Christian Bizer","doi":"10.1145/2797115.2797118","DOIUrl":"https://doi.org/10.1145/2797115.2797118","url":null,"abstract":"Millions of HTML tables containing structured data can be found on the Web. With their wide coverage, these tables are potentially very useful for filling missing values and extending cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph. As a prerequisite for being able to use table data for knowledge base extension, the HTML tables need to be matched with the knowledge base, meaning that correspondences between table rows/columns and entities/schema elements of the knowledge base need to be found. This paper presents the T2D gold standard for measuring and comparing the performance of HTML table to knowledge base matching systems. T2D consists of 8 700 schema-level and 26 100 entity-level correspondences between the WebDataCommons Web Tables Corpus and the DBpedia knowledge base. In contrast related work on HTML table to knowledge base matching, the Web Tables Corpus (147 million tables), the knowledge base, as well as the gold standard are publicly available. The gold standard is used afterward to evaluate the performance of T2K Match, an iterative matching method which combines schema and instance matching. T2K Match is designed for the use case of matching large quantities of mostly small and narrow HTML tables against large cross-domain knowledge bases. The evaluation using the T2D gold standard shows that T2K Match discovers table-to-class correspondences with a precision of 94%, row-to-entity correspondences with a precision of 90%, and column-to-property correspondences with a precision of 77%.","PeriodicalId":386229,"journal":{"name":"Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133393499","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: 160

Recommending Customizable Products: A Multiple Choice Knapsack Solution 推荐可定制的产品:一个多重选择的背包解决方案

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797116

A. Sivaramakrishnan, Madhusudhan Krishnamachari, Vidhya Balasubramanian

引用次数: 2

What Makes Ontology Reasoning so Arduous?: Unveiling the key ontological features 是什么让本体论推理如此艰难?:揭示关键的本体特征

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797117

N. Alaya, S. Yahia, M. Lamolle

{"title":"What Makes Ontology Reasoning so Arduous?: Unveiling the key ontological features","authors":"N. Alaya, S. Yahia, M. Lamolle","doi":"10.1145/2797115.2797117","DOIUrl":"https://doi.org/10.1145/2797115.2797117","url":null,"abstract":"Reasoning with ontologies is one of the core fields of research in Description Logics. A variety of efficient reasoner with highly optimized algorithms have been developed to allow inference tasks on expressive ontology languages such as OWL(DL). However, reasoner reported computing times have exceeded and sometimes fall behind the expected theoretical values. From an empirical perspective, it is not yet well understood, which particular aspects in the ontology are reasoner performance degrading factors. In this paper, we conducted an investigation about state of art works that attempted to portray potential correlation between reasoner empirical behaviour and particular ontological features. These works were analysed and then broken down into categories. Further, we proposed a set of ontology features covering a broad range of structural and syntactic ontology characteristics. We claim that these features are good indicators of the ontology hardness level against reasoning tasks. In order to assess the worthiness of our proposals, we adopted a supervised machine learning approach. Features served as the bases to learn predictive models of reasoners robustness. These models was trained for 6 well known reasoners and using their evaluation results during the ORE'2014 competition. Our prediction models showed a high accuracy level which witness the effectiveness of our set of features.","PeriodicalId":386229,"journal":{"name":"Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133773695","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

User Modeling in Folksonomies: Relational Clustering and Tag Weighting 大众分类法中的用户建模:关系聚类和标签加权

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797129

Takuya Kitazawa, M. Sugiyama

引用次数: 0

bacon: Linked Data Integration based on the RDF Data Cube Vocabulary bacon:基于RDF数据立方体词汇表的关联数据集成

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797126

Sebastian P. Bayerl, M. Granitzer

引用次数: 4

Modeling and predicting information search behavior 建模和预测信息搜索行为

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797123

Saraschandra Karanam, H. Oostendorp, M. Sanchiz, A. Chevalier, Jessie Chin, W. Fu

{"title":"Modeling and predicting information search behavior","authors":"Saraschandra Karanam, H. Oostendorp, M. Sanchiz, A. Chevalier, Jessie Chin, W. Fu","doi":"10.1145/2797115.2797123","DOIUrl":"https://doi.org/10.1145/2797115.2797123","url":null,"abstract":"This paper looks at two limitations of cognitive models of web-navigation: first, they do not account for the entire process of information search and second, they do not account for the differences in search behavior caused by aging. To address these limitations, data from an experiment in which two types of information search tasks (simple and difficult), presented to both young and old participants was used. We found that in general difficult tasks demand significantly more time, significantly more clicks, significantly more reformulations and are answered significantly less accurately than simple tasks. Older persons inspect the search engine result pages significantly longer, produce significantly fewer reformulations with difficult tasks than younger persons, and are significantly more accurate than younger persons with simple tasks. We next used a cognitive model of web-navigation called CoLiDeS to predict which search engine result a user would choose to click. Old participants were found to click more often only on search engine results with high semantic similarity with the query. Search engine results generated by old participants were of higher semantic similarity value (computed w.r.t the query) than those generated by young participants only in the second cycle. Match between model-predicted clicks and actual user clicks was found to be significantly higher for difficult tasks compared to simple tasks. Potential improvements in enhancing the modeling and its applications are discussed.","PeriodicalId":386229,"journal":{"name":"Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114173044","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