Dataset for paper: "Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals"

This dataset consists in two distinct scholarly knowledge graph created from two publicly available bibliographic datasets: 1) a triplestore covering information about the journal <em>Scientometrics</em> provided by <em>OpenCitations</em> (available here), and 2) the <em>AMiner </em>AND benchmark from 2018 available here. This KG was extracted for a research project on knowledge graph embeddings (KGEs) for author disambiguation. Structural triples of the knowledge graphs are split into training, testing and validation for applying representation learning methods. Textual literals and numeric literals were stored separately in order to implement multimodal approaches for KGEs (see arXiv:1802.00934). For the same reason, textual literals and numeric literals are already stored into sentence embeddings and a numeric matrix respectively in the files <em>textual_literals.npy </em>and <em>numeric_literals.npy </em>in order to simplify the representation learning task. The file <em>and_eval.json</em> of each KG contains the evaluation dataset used for evaluating our AND architecture. For the script used to gather this dataset see https://github.com/sntcristian/and-kge/tree/main/src/AMiner-534K and https://github.com/sntcristian/and-kge/tree/main/src/OC-782K.