In recent years, knowledge graphs have been proposed and made publicly available in the scholarly field, covering information about entities such as publications, authors, and venues. Our final MAKG is publicly available at and can be used for the search or recommendation of scholarly entities, as well as enhanced scientific impact quantification. Finally, we provide statistics for the updated MAKG. Third, we compute and evaluate embeddings for all 239 million publications, 243 million authors, 49,000 journals, and 16,000 conference entities in the MAKG based on several state-of-the-art embedding techniques. Second, we develop and evaluate methods for tagging publications by their discipline and by keywords, facilitating enhanced search and recommendation of publications and associated entities. First, we adopt and evaluate unsupervised approaches for large-scale author name disambiguation. Based on a qualitative analysis of the MAKG, we address three aspects. In this article, we present methods for enhancing the Microsoft Academic Knowledge Graph (MAKG), a recently published large-scale knowledge graph containing metadata about scientific publications and associated authors, venues, and affiliations. Although several large knowledge graphs have been proposed in the scholarly field, such graphs are limited with respect to several data quality dimensions such as accuracy and coverage.
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