{"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/augmenting-and-tuning-knowledge-graph","title":"Augmenting and Tuning Knowledge Graph Embeddings","arxiv_id":"1907.01068","date":"2019-07-01","proceeding":null,"authors":["Robert Bamler","Farnood Salehi","Stephan Mandt"],"abstract":"Knowledge graph embeddings rank among the most successful methods for link prediction in knowledge graphs, i.e., the task of completing an incomplete collection of relational facts. 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Our approach is agnostic to details of the model and results in a new state of the art in link prediction on standard benchmark data.","url_abs":"https://arxiv.org/abs/1907.01068v1","url_pdf":"https://arxiv.org/pdf/1907.01068v1.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":"augmenting-and-tuning-knowledge-graph","repo_url":"https://github.com/mandt-lab/knowledge-graph-tuning","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"knowledge-graph-embeddings","task_name":"Knowledge Graph Embeddings"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"link-prediction","task_name":"Link Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-fb15k","task":"Link Prediction","dataset":"FB15k","model":"DistMult (after variational EM)","rank_in_archive_order":2,"of":23,"metrics":{"Hits@10":"0.914","MRR":"0.841"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-fb15k-237","task":"Link Prediction","dataset":"FB15k-237","model":"DistMult (after variational EM)","rank_in_archive_order":60,"of":75,"metrics":{"Hits@10":"0.548","MRR":"0.357"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wn18","task":"Link Prediction","dataset":"WN18","model":"DistMult (after variational EM)","rank_in_archive_order":34,"of":37,"metrics":{"MRR":"0.911"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wn18rr","task":"Link Prediction","dataset":"WN18RR","model":"DistMult (after variational EM)","rank_in_archive_order":75,"of":75,"metrics":{"MRR":"0.455"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1907.01068","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.01068"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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