Access to scientific data is dependent on the proper indexing of such data for findability (alongside other FAIR standards) on portals, aggregators and more generally-speaking on the Web. Due to a lack of uptake in terms of standards (e.g., on unique identifiers, ORCID records, etc.), author name disambiguation continues to represent a major issue in organizing such research data. In this work, we present a novel approach to resolving name ambiguity for scientific authors as they appear in data about publications, grants or scientific datasets. Specifically, we leverage metadata present in a document in order to cluster similar authors: In addition to commonly-used information such as co-authorship, we include named entity similarities obtained from knowledge graphs as an additional source of information to further improve document representation and, subsequently, cluster the documents by authors. Due to the computational complexity of graph algorithms, we leverage knowledge graph embeddings to approximate the structure of large graphs. We evaluate our approach against an existing solution on a gold standard dataset and show that our approach provides notable improvement, especially when other information is sparse. In addition, we provide a novel, manually-annotated dataset for this task, consisting of scientific publications and project data.