LSQ 2.0: SPARQL查询日志的链接数据集

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Semantic Web Pub Date : 2022-11-29 DOI:10.3233/sw-223015

Claus Stadler, Muhammad Saleem, Qaiser Mehmood, C. Buil-Aranda, M. Dumontier, A. Hogan, Axel-Cyrille Ngonga Ngomo

{"title":"LSQ 2.0: SPARQL查询日志的链接数据集","authors":"Claus Stadler, Muhammad Saleem, Qaiser Mehmood, C. Buil-Aranda, M. Dumontier, A. Hogan, Axel-Cyrille Ngonga Ngomo","doi":"10.3233/sw-223015","DOIUrl":null,"url":null,"abstract":"We present the Linked SPARQL Queries (LSQ) dataset, which currently describes 43.95 million executions of 11.56 million unique SPARQL queries extracted from the logs of 27 different endpoints. The LSQ dataset provides RDF descriptions of each such query, which are indexed in a public LSQ endpoint, allowing interested parties to find queries with the characteristics they require. We begin by describing the use cases envisaged for the LSQ dataset, which include applications for research on common features of queries, for building custom benchmarks, and for designing user interfaces. We then discuss how LSQ has been used in practice since the release of four initial SPARQL logs in 2015. We discuss the model and vocabulary that we use to represent these queries in RDF. We then provide a brief overview of the 27 endpoints from which we extracted queries in terms of the domain to which they pertain and the data they contain. We provide statistics on the queries included from each log, including the number of query executions, unique queries, as well as distributions of queries for a variety of selected characteristics. We finally discuss how the LSQ dataset is hosted and how it can be accessed and leveraged by interested parties for their use cases.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"68 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"LSQ 2.0: A linked dataset of SPARQL query logs\",\"authors\":\"Claus Stadler, Muhammad Saleem, Qaiser Mehmood, C. Buil-Aranda, M. Dumontier, A. Hogan, Axel-Cyrille Ngonga Ngomo\",\"doi\":\"10.3233/sw-223015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present the Linked SPARQL Queries (LSQ) dataset, which currently describes 43.95 million executions of 11.56 million unique SPARQL queries extracted from the logs of 27 different endpoints. The LSQ dataset provides RDF descriptions of each such query, which are indexed in a public LSQ endpoint, allowing interested parties to find queries with the characteristics they require. We begin by describing the use cases envisaged for the LSQ dataset, which include applications for research on common features of queries, for building custom benchmarks, and for designing user interfaces. We then discuss how LSQ has been used in practice since the release of four initial SPARQL logs in 2015. We discuss the model and vocabulary that we use to represent these queries in RDF. We then provide a brief overview of the 27 endpoints from which we extracted queries in terms of the domain to which they pertain and the data they contain. We provide statistics on the queries included from each log, including the number of query executions, unique queries, as well as distributions of queries for a variety of selected characteristics. We finally discuss how the LSQ dataset is hosted and how it can be accessed and leveraged by interested parties for their use cases.\",\"PeriodicalId\":48694,\"journal\":{\"name\":\"Semantic Web\",\"volume\":\"68 1\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2022-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Semantic Web\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.3233/sw-223015\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Semantic Web","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.3233/sw-223015","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 6

摘要

我们提供了链接SPARQL查询(LSQ)数据集，该数据集目前描述了从27个不同端点的日志中提取的1156万个唯一SPARQL查询的4395万次执行。LSQ数据集提供了每个此类查询的RDF描述，这些描述在公共LSQ端点中建立了索引，从而允许感兴趣的各方查找具有所需特征的查询。我们首先描述为LSQ数据集设想的用例，其中包括用于研究查询的常见特征、构建自定义基准和设计用户界面的应用程序。然后，我们讨论了自2015年发布四个初始SPARQL日志以来，LSQ在实践中是如何使用的。我们将讨论用于在RDF中表示这些查询的模型和词汇表。然后，我们简要概述了27个端点，根据它们所属的域和它们包含的数据，我们从中提取了查询。我们提供了每个日志中包含的查询的统计信息，包括查询执行的次数、唯一查询以及针对各种选定特征的查询分布。我们最后讨论了LSQ数据集是如何托管的，以及感兴趣的各方如何为他们的用例访问和利用它。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

LSQ 2.0: A linked dataset of SPARQL query logs

We present the Linked SPARQL Queries (LSQ) dataset, which currently describes 43.95 million executions of 11.56 million unique SPARQL queries extracted from the logs of 27 different endpoints. The LSQ dataset provides RDF descriptions of each such query, which are indexed in a public LSQ endpoint, allowing interested parties to find queries with the characteristics they require. We begin by describing the use cases envisaged for the LSQ dataset, which include applications for research on common features of queries, for building custom benchmarks, and for designing user interfaces. We then discuss how LSQ has been used in practice since the release of four initial SPARQL logs in 2015. We discuss the model and vocabulary that we use to represent these queries in RDF. We then provide a brief overview of the 27 endpoints from which we extracted queries in terms of the domain to which they pertain and the data they contain. We provide statistics on the queries included from each log, including the number of query executions, unique queries, as well as distributions of queries for a variety of selected characteristics. We finally discuss how the LSQ dataset is hosted and how it can be accessed and leveraged by interested parties for their use cases.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Semantic Web COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS

CiteScore

8.30

自引率

6.70%

发文量

期刊介绍： The journal Semantic Web – Interoperability, Usability, Applicability brings together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future internet and elsewhere. As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust, and provenance core topics for Semantic Web research. New retrieval paradigms, user interfaces, and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. We especially welcome papers which add a social, spatial, and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics.