In this paper, we present COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations (this http URL). COCI is the first open citation index created by OpenCitations, in which we have applied the concept of citations as first-class data entities, and it contains more than 445 million DOI-to-DOI citation links derived from the data available in Crossref. These citations are described in RDF by means of the newly extended version of the OpenCitations Data Model (OCDM). We introduce the workflow we have developed for creating these data, and also show the additional services that facilitate the access to and querying of these data via different access points: a SPARQL endpoint, a REST API, bulk downloads, Web interfaces, and direct access to the citations via HTTP content negotiation. Finally, we present statistics regarding the use of COCI citation data, and we introduce several projects that have already started to use COCI data for different purposes.
Another important source of bibliographic data in RDF is OpenAIRE ( https: www.openaire.eu ) @cite_19 . Created by funding from the European Union, its RDF dataset makes available data for around 34 million research products created in the context of around 2.5 million research projects.
@cite_19
2612009452 109245773 2568135881 2283287182
OpenAIRE, the Open Access Infrastructure for Research in Europe, enables search, discovery and monitoring of publications and datasets from more than 100,000 research projects. Increasing the reusability of the OpenAIRE research metadata, connecting it to other open data about projects, publications, people and organizations, and reaching out to further related domains requires better technical interoperability, which we aim at achieving by exposing the OpenAIRE Information Space as Linked Data. We present a scalable and maintainable architecture that converts the OpenAIRE data from its original HBase NoSQL source to RDF. We furthermore explore how this novel integration of data about research can facilitate scholarly communication. Over a decade after RDF has been published as a W3C recommendation, publishing open and machine-readable content on the Web has recently received a lot more attention, including from corporate and governmental bodies; notably thanks to the Linked Open Data community, there now exists a rich vein of heterogeneous RDF data published on the Web (the so-called of Data") accessible to all. However, RDF publishers are prone to making errors which compromise the e ectiveness of applications leveraging the resulting data. In this paper, we discuss common errors in RDF publishing, their consequences for applications, along with possible publisher-oriented approaches to improve the quality of structured, machine-readable and open data on the Web. The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors. Based on URIs, HTTP and RDF, the Linked Data project [3] aims to expose, share and connect related data from diverse sources on the SemanticWeb. Linked Open Data (LOD) is a community effort to apply the Linked Data principles to data published under open licenses. With this effort, a large number of LOD datasets have been gathered in the LOD cloud, such as DBpedia, Freebase and FOAF profiles. These datasets are connected by links such as owl:sameAs. LOD has gained rapidly progressed and is still growing constantly. Until May 2009, there are 4.7 billion RDF triples and around 142 million RDF links [3]. After that, the total has been increased to 16 billion triples in March 2010 and another 14 billion triples have been published by the AIFB according to [17].