DBpedia graph embeddings using RDF2Vec. RDF2Vec embedding generation code can be found here and is based on a publication by Portisch et al. [1]. The embeddings dataset consists of 200-dimensional vectors of DBpedia entities (from 1/9/2021). Generating Embeddings The code for generating these embeddings can be found here. Run the run.sh script that wraps all the necessary commmands to generate embeddings bash run.sh The script downloads a set of DBpedia files, which are listed in dbpedia_files.txt. It then builds a Docker image and runs a container of that image that generates the embeddings for the DBpedia graph defined by the DBpedia files. A folder files is created containing all the downloaded DBpedia files, and a folder embeddings/dbpedia is created containing the embeddings in vectors.txt along a set of random walk files. Run Time of Embeddings Generation Generating embeddings can take more than a day, but it depends on the number of DBpedia files chosen to be downloaded. Following are some basic run time statistics when embeddings are generated on a 64 GB RAM, 8 cores (AMD EPYC), 1 TB SSD, 1996.221 MHz machine. Total: 1 day, 8 hours, 52 minutes, 41 seconds Walk generation: 0 days, 7 minutes, 24 minutes, 36 seconds Training: 1 day, 1 hour, 28 minutes, 5 seconds Parameters Used Here is listed the parameters used to generate the embeddings provided here: Number of walks per entity: 100 Depth (hops) per walk: 4 Walk generation mode: RANDOM_WALKS_DUPLICATE_FREE Threads: # of processors / 2 Training mode: sg Embeddings vector dimension: 200 Minimum word2vec word count: 1 Sample rate: 0.0 Training window size: 5 Training epochs: 5

{"references": ["Portisch, J., Hladik, M. and Paulheim, H., 2020. RDF2Vec Light--A Lightweight Approach for Knowledge Graph Embeddings. arXiv preprint arXiv:2009.07659."]}