{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/expeditious-generation-of-knowledge-graph","title":"Expeditious Generation of Knowledge Graph Embeddings","arxiv_id":"1803.07828","date":"2018-03-21","proceeding":null,"authors":["Tommaso Soru","Stefano Ruberto","Diego Moussallem","André Valdestilhas","Alexander Bigerl","Edgard Marx","Diego Esteves"],"abstract":"Knowledge Graph Embedding methods aim at representing entities and relations\nin a knowledge base as points or vectors in a continuous vector space. Several\napproaches using embeddings have shown promising results on tasks such as link\nprediction, entity recommendation, question answering, and triplet\nclassification. However, only a few methods can compute low-dimensional\nembeddings of very large knowledge bases without needing state-of-the-art\ncomputational resources. In this paper, we propose KG2Vec, a simple and fast\napproach to Knowledge Graph Embedding based on the skip-gram model. Instead of\nusing a predefined scoring function, we learn it relying on Long Short-Term\nMemories. We show that our embeddings achieve results comparable with the most\nscalable approaches on knowledge graph completion as well as on a new metric.\nYet, KG2Vec can embed large graphs in lesser time by processing more than 250\nmillion triples in less than 7 hours on common hardware.","url_abs":"http://arxiv.org/abs/1803.07828v2","url_pdf":"http://arxiv.org/pdf/1803.07828v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"expeditious-generation-of-knowledge-graph","repo_url":"https://github.com/AKSW/KG2Vec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"knowledge-graph-completion","task_name":"Knowledge Graph Completion"},{"task_slug":"knowledge-graph-embedding","task_name":"Knowledge Graph Embedding"},{"task_slug":"knowledge-graph-embeddings","task_name":"Knowledge Graph Embeddings"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":null,"task_name":"Triplet"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-aksw-bib","task":"Link Prediction","dataset":"AKSW-bib","model":"KG2Vec LSTM","rank_in_archive_order":1,"of":1,"metrics":{"Hits@1":"0.0384","Hits@10":"0.1923","Hits@3":"0.0979"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}