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.
ScholarlyData ( http: www.scholarlydata.org ) @cite_26 is a project that refactors the Semantic Web Dog Food so as to keep the dataset growing in good health. It uses the Conference Ontology, an improvement version of the Semantic Web Conference Ontology, to describe metadata of documents (5,415, as of March 31, 2019), people (more than 1,100), and data about academic events (592) where such documents have been presented.
@cite_26
2522413525 2007269563 1966541562 2084989254
The Semantic Web Dog Food (SWDF) is the reference linked dataset of the Semantic Web community about papers, people, organisations, and events related to its academic conferences. In this paper we analyse the existing problems of generating, representing and maintaining Linked Data for the SWDF. With this work (i) we provide a refactored and cleaned SWDF dataset; (ii) we use a novel data model which improves the Semantic Web Conference Ontology, adopting best ontology design practices and (iii) we provide an open source workflow to support a healthy growth of the dataset beyond the Semantic Web conferences. In this paper we present a novel index data structure tailored towards semantic full-text search. Semantic full-text search, as we call it, deeply integrates keyword-based full-text search with structured search in ontologies. Queries are SPARQL-like, with additional relations for specifying word-entity co-occurrences. In order to build such queries the user needs to be guided. We believe that incremental query construction with context-sensitive suggestions in every step serves that purpose well. Our index has to answer queries and provide such suggestions in real time. We achieve this through a novel kind of posting lists and query processing, avoiding very long (intermediate) result lists and expensive (non-local) operations on these lists. In an evaluation of 8000 queries on the full English Wikipedia (40 GB XML dump) and the YAGO ontology (26.6 million facts), we achieve average query and suggestion times of around 150ms. Representing the semantics of unstructured scientific publications will certainly facilitate access and search and hopefully lead to new discoveries. However, current digital libraries are usually limited to classic flat structured metadata even for scientific publications that potentially contain rich semantic metadata. In addition, how to search the scientific literature of linked semantic metadata is an open problem. We have developed a semantic digital library oreChem ChemxSeer that models chemistry papers with semantic metadata. It stores and indexes extracted metadata from a chemistry paper repository Chemx Seer using "compound objects". We use the Open Archives Initiative Object Reuse and Exchange (OAI-ORE) (http: www.openarchives.org ore standard to define a compound object that aggregates metadata fields related to a digital object. Aggregated metadata can be managed and retrieved easily as one unit resulting in improved ease-of-use and has the potential to improve the semantic interpretation of shared data. We show how metadata can be extracted from documents and aggregated using OAI-ORE. ORE objects are created on demand; thus, we are able to search for a set of linked metadata with one query. We were also able to model new types of metadata easily. For example, chemists are especially interested in finding information related to experiments in documents. We show how paragraphs containing experiment information in chemistry papers can be extracted and tagged based on a chemistry ontology with 470 classes, and then represented in ORE along with other document-related metadata. Our algorithm uses a classifier with features that are words that are typically only used to describe experiments, such as "apparatus", "prepare", etc. Using a dataset comprised of documents from the Royal Society of Chemistry digital library, we show that the our proposed methodperforms well in extracting experiment-related paragraphs from chemistry documents. This article proposes a process to retrieve the URL of a document for which metadata records exist in a digital library catalog but a pointer to the full text of the document is not available. The process uses results from queries submitted to Web search engines for finding the URL of the corresponding full text or any related material. We present a comprehensive study of this process in different situations by investigating different query strategies applied to three general purpose search engines (Google, Yahoo!, MSN) and two specialized ones (Scholar and CiteSeer), considering five user scenarios. Specifically, we have conducted experiments with metadata records taken from the Brazilian Digital Library of Computing (BDBComp) and The DBLP Computer Science Bibliography (DBLP). We found that Scholar was the most effective search engine for this task in all considered scenarios and that simple strategies for combining and re-ranking results from Scholar and Google significantly improve the retrieval quality. Moreover, we study the influence of the number of query results on the effectiveness of finding missing information as well as the coverage of the proposed scenarios.