Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics最新文献_第2页

Using Linked Open Data in Recommender Systems 在推荐系统中使用链接开放数据

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797128

Ladislav Peška, P. Vojtás

{"title":"Using Linked Open Data in Recommender Systems","authors":"Ladislav Peška, P. Vojtás","doi":"10.1145/2797115.2797128","DOIUrl":"https://doi.org/10.1145/2797115.2797128","url":null,"abstract":"In this paper, we present our work in progress on using LOD data to enhance recommending on existing e-commerce sites. We imagine a situation of e-commerce website employing content-based or hybrid recommendation. Such recommending algorithms need relevant object attributes to produce useful recommendations. However, on some domains, usable attributes may be difficult to fill in manually and yet accessible from LOD cloud. A pilot study was conducted on the domain of secondhand bookshops. In this domain, recommending is extraordinary difficult because of high ratio between objects and users, lack of significant attributes and limited availability of items. Both collaborative filtering and content-based recommendation applicability is questionable under this conditions. We queried both Czech and English language edition of DBPedia in order to receive additional information about objects (books) and used various recommending algorithms to learn user preferences. Our approach is general and can be applied on other domains as well. Proposed methods were tested in an off-line recommending scenario with promising results; however there are a lot of challenges for the future work including more complex algorithm analysis, improving SPARQL queries or improving DBPedia matching rules and resource identification.","PeriodicalId":386229,"journal":{"name":"Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics","volume":"49 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":"129417538","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: 12

Semantic Integration of Structured Data Powered by Linked Open Data 链接开放数据驱动的结构化数据语义集成

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797130

A. Subramanian, S. Srinivasa, Pavan Kumar, S. Vignesh

引用次数: 3

Towards Events Tweet Contextualization Using Social Influence Model and Users Conversations 利用社会影响模型和用户对话实现事件推文情境化

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797134

Rami Belkaroui, R. Faiz

{"title":"Towards Events Tweet Contextualization Using Social Influence Model and Users Conversations","authors":"Rami Belkaroui, R. Faiz","doi":"10.1145/2797115.2797134","DOIUrl":"https://doi.org/10.1145/2797115.2797134","url":null,"abstract":"Nowadays, microblogging sites have completely changed the manner in which people communicate and share information. They are among the most relevant source of knowledge where information is created, exchanged and transformed, as witnessed by the important number of their users and their activities during events or campaigns like the terror attack in Paris in 2015. On Twitter, users post messages (called tweets) in real time about events, natural disasters, news, etc. Tweets are short messages that do not exceed 140 characters. Due to this limitation, an individual tweet it's rarely self-content. However, user cannot effectively understand or consume information. In order, to make tweet understandable to a reader, it is therefore necessary to know their context. In fact, on Twitter, context can be derived from users interactions, content streams and friendship. Given that there are rich user interactions on Twitter. In this paper, we propose an approach for tweet contextualization task which combines different types of signals from social users interactions to provide automatically information that explains the tweet. In addition, our approach aims to help users to satisfy any contextual information need. To evaluate our approach, we construct a reference summary by asking assessors to manually select the most informative tweets as a summary. Our experimental results based on this editorial data set offers interesting results and help ensure that context summaries contain adequate correlating information with the given tweet.","PeriodicalId":386229,"journal":{"name":"Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics","volume":"53 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":"131409548","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

An Ontology Enrichment Approach by Using DBpedia 基于DBpedia的本体充实方法

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797127

Meisam Booshehri, P. Luksch

{"title":"An Ontology Enrichment Approach by Using DBpedia","authors":"Meisam Booshehri, P. Luksch","doi":"10.1145/2797115.2797127","DOIUrl":"https://doi.org/10.1145/2797115.2797127","url":null,"abstract":"Over the past decade, an increasing number of methods have been proposed for (semi-) automatic generation of ontology from text. However, the ontology generated by these methods usually does not meet the needs of many reasoning-based applications in different domains since most of these methods aim at generating inexpressive ontologies e.g. bare taxonomies. In this paper, a new ontology enrichment approach is proposed in which Web of Linked Data (in particular, DBpedia as one of the huge Linked Data datasets) is used as background knowledge beside text in order to recognize new ontological relations, specifically object properties, for ontology enrichment. In other words, this enrichment approach can be considered as a post-processing step for the \"Relations\" layer (i.e. the fifth layer) in Ontology Learning Stack, aiming at recommending new object properties to the ontology engineers enabling them to create much more expressive ontologies. This is actually a complementary approach to our recent approach towards adding Linked Data to ontology learning layers where we aimed at improving the functions associated to the \"Synonyms\" layer, the \"Concept Formation\" layer and the \"Concept Hierarchy\" layer of ontology learning stack. In order to evaluate the approach, a customized experimental design is introduced called the \"Pseudo Gold Standard based Ontology Evaluation\" in which the results obtained by a human expert are compared against those obtained automatically. Finally, the experimental results showed a satisfactory improvement in learning object properties.","PeriodicalId":386229,"journal":{"name":"Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics","volume":"1 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":"116444087","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

Ontology-Based Data Integration for Event Recognition in the Maritime Domain 基于本体的海事领域事件识别数据集成

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797133

Georgios M. Santipantakis, Konstantinos I. Kotis, G. Vouros

引用次数: 8

SensoMan: Remote management of context sensors SensoMan:远程管理环境传感器

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797121

Athina C. Paphitou, Stella Constantinou, G. Kapitsaki

引用次数: 2

Improving Semantic Search through Entity-Based Document Ranking 通过基于实体的文档排序改进语义搜索

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2015-07-13 DOI: 10.1145/2797115.2797120

Benjamin Großmann, Alexandru Todor, A. Paschke

引用次数: 0

Overlapping Community Detection Optimization and Nash Equilibrium 重叠社团检测优化与纳什均衡

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2014-06-26 DOI: 10.1145/2797115.2797131

M. Crampes, Michel Plantié

引用次数: 11

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics 第五届网络智能、挖掘和语义国际会议论文集

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics Pub Date : 1900-01-01 DOI: 10.1145/2797115

引用次数: 0