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.
WikiCite ( https: meta.wikimedia.org wiki WikiCite ) is a proposal, with a related series of workshops, which aims at building a bibliographic database in Wikidata @cite_10 to serve all Wikimedia projects. Currently Wikidata hosts (as of March 29, 2019) more than 170 million citations.
@cite_10
150699991 2148517649 2031046392 1970396504
Wikidata is the central data management platform of Wikipedia. By the efforts of thousands of volunteers, the project has produced a large, open knowledge base with many interesting applications. The data is highly interlinked and connected to many other datasets, but it is also very rich, complex, and not available in RDF. To address this issue, we introduce new RDF exports that connect Wikidata to the Linked Data Web. We explain the data model of Wikidata and discuss its encoding in RDF. Moreover, we introduce several partial exports that provide more selective or simplified views on the data. This includes a class hierarchy and several other types of ontological axioms that we extract from the site. All datasets we discuss here are freely available online and updated regularly. Wikipedia is an online encyclopedia which anyone can edit. While most edits are constructive, about 7 are acts of vandalism. Such behavior is characterized by modifications made in bad faith; introducing spam and other inappropriate content. In this work, we present the results of an effort to integrate three of the leading approaches to Wikipedia vandalism detection: a spatio-temporal analysis of metadata (STiki), a reputation-based system (WikiTrust), and natural language processing features. The performance of the resulting joint system improves the state-of-the-art from all previous methods and establishes a new baseline for Wikipedia vandalism detection. We examine in detail the contribution of the three approaches, both for the task of discovering fresh vandalism, and for the task of locating vandalism in the complete set of Wikipedia revisions. The online encyclopedia Wikipedia is a vast, constantly evolving tapestry of interlinked articles. For developers and researchers it represents a giant multilingual database of concepts and semantic relations, a potential resource for natural language processing and many other research areas. This paper introduces the Wikipedia Miner toolkit, an open-source software system that allows researchers and developers to integrate Wikipedia@?s rich semantics into their own applications. The toolkit creates databases that contain summarized versions of Wikipedia@?s content and structure, and includes a Java API to provide access to them. Wikipedia@?s articles, categories and redirects are represented as classes, and can be efficiently searched, browsed, and iterated over. Advanced features include parallelized processing of Wikipedia dumps, machine-learned semantic relatedness measures and annotation features, and XML-based web services. Wikipedia Miner is intended to be a platform for sharing data mining techniques. Wikipedia is the largest monolithic repository of human knowledge. In addition to its sheer size, it represents a new encyclopedic paradigm by interconnecting articles through hyperlinks. However, since these links are created by human authors, links one would expect to see are often missing. The goal of this work is to detect such gaps automatically. In this paper, we propose a novel method for augmenting the structure of hyperlinked document collections such as Wikipedia. It does not require the extraction of any manually defined features from the article to be augmented. Instead, it is based on principal component analysis, a well-founded mathematical generalization technique, and predicts new links purely based on the statistical structure of the graph formed by the existing links. Our method does not rely on the textual content of articles; we are exploiting only hyperlinks. A user evaluation of our technique shows that it improves the quality of top link suggestions over the state of the art and that the best predicted links are significantly more valuable than the 'average' link already present in Wikipedia. Beyond link prediction, our algorithm can potentially be used to point out topics an article misses to cover and to cluster articles semantically.