We present SemOpenAlex, 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. 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, and other forms of
scholarly big data analytics within and across scientific disciplines.
Additionally, it enables academic recommender systems, such as recommending
collaborators, publications, and venues, including explainability capabilities.
Finally, SemOpenAlex can serve for RDF query optimization benchmarks, creating
scholarly knowledge-guided language models, and as a hub for semantic
scientific publishing.

SemOpenAlex 是一个拥有超过 260 亿三元组的 RDF 知识图谱，包含科学出版物及其关联实体（例如作者、机构、期刊和概念）。它提供多种数据访问方式，并支持知识图谱实体的嵌入，可用于探索性语义搜索、科学影响度量、学术推荐系统和语义科学出版中的其他应用。

SemOpenAlex: 260 亿 RDF 三元组的科学领域

SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples

With the remarkable increase in the number of scientific entities such as
publications, researchers, and scientific topics, and the associated
information overload in science, academic recommender systems have become
increasingly important for millions of researchers and science enthusiasts.
However, it is often overlooked that these systems are subject to various
biases. In this article, we first break down the biases of academic recommender
systems and characterize them according to their impact and prevalence. In
doing so, we distinguish between biases originally caused by humans and biases
induced by the recommender system. Second, we provide an overview of methods
that have been used to mitigate these biases in the scholarly domain. Based on
this, third, we present a framework that can be used by researchers and
developers to mitigate biases in scholarly recommender systems and to evaluate
recommender systems fairly. Finally, we discuss open challenges and possible
research directions related to scholarly biases.

本文研究学术推荐系统中存在的偏见问题，对这些偏见进行分析并提出了缓解偏见及公正评估的方法和框架。同时，讨论相关研究的挑战和方向。