Scientific Concept Extraction

A scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata. The unarXive data set contains * One million papers in plain text * 63 million citation contexts * 39 million reference strings * A citation network of 16 million connections The data is generated from all LaTeX sources on arXiv from 1991–2020/07 and therefore of higher quality than data generated from PDF files. Furthermore, as all citing papers are available in full text, citation contexts of arbitrary size can be extracted. Typical uses of the data set are approaches in * Citation recommendation * Citation context analysis * Reference string parsing The code for generating the data set is publicly available.

NLPContributionGraph was introduced as Task 11 at SemEval 2021 for the first time. The task is defined on a dataset of Natural Language Processing (NLP) scholarly articles with their contributions structured to be integrable within Knowledge Graph infrastructures such as the Open Research Knowledge Graph. The structured contribution annotations are provided as (1) Contribution sentences : a set of sentences about the contribution in the article; (2) Scientific terms and relations: a set of scientific terms and relational cue phrases extracted from the contribution sentences; and (3) Triples: semantic statements that pair scientific terms with a relation, modeled toward subject-predicate-object RDF statements for KG building. The Triples are organized under three (mandatory) or more of twelve total information units (viz., ResearchProblem, Approach, Model, Code, Dataset, ExperimentalSetup, Hyperparameters, Baselines, Results, Tasks, Experiments, and AblationAnalysis).

SemOpenAlex is an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts. * SemOpenAlex is licensed under CC0, providing free and open access to the data. * We offer the data through multiple channels, including RDF dump files, a SPARQL endpoint, and as a data source in the Linked Open Data cloud, complete with resolvable URIs and links to other data sources (ISNI, DOI, ORCID, ROR, Scopus, DOAJ, Wikidata, * Moreover, we provide embeddings for knowledge graph entities using high-performance computing. SemOpenAlex enables a broad range of use-case scenarios, such as * exploratory semantic search via our website, * large-scale scientific impact quantification, * other forms of scholarly big data analytics within and across scientific disciplines. * enables academic recommender systems, such as recommending collaborators, publications, and venues, including explainability capabilities. * can serve for RDF query optimization benchmarks, * creating scholarly knowledge-guided language models, * as a hub for semantic scientific publishing.