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The proposed method is\nrelated to holographic models of associative memory in that it employs circular\ncorrelation to create compositional representations. By using correlation as\nthe compositional operator HolE can capture rich interactions but\nsimultaneously remains efficient to compute, easy to train, and scalable to\nvery large datasets. In extensive experiments we show that holographic\nembeddings are able to outperform state-of-the-art methods for link prediction\nin knowledge graphs and relational learning benchmark datasets.","url_abs":"http://arxiv.org/abs/1510.04935v2","url_pdf":"http://arxiv.org/pdf/1510.04935v2.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":"holographic-embeddings-of-knowledge-graphs","repo_url":"https://github.com/mnick/holographic-embeddings","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"holographic-embeddings-of-knowledge-graphs","repo_url":"https://github.com/bi-graph/emgraph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"holographic-embeddings-of-knowledge-graphs","repo_url":"https://github.com/mnick/scikit-kge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"holographic-embeddings-of-knowledge-graphs","repo_url":"https://github.com/Accenture/AmpliGraph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"relational-reasoning","task_name":"Relational Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-fb15k-1","task":"Link Prediction","dataset":"FB15k","model":"HolE","rank_in_archive_order":9,"of":10,"metrics":{"Hits@1":"0.402","Hits@10":"0.739","Hits@3":"0.613","MRR":"0.524"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wn18","task":"Link Prediction","dataset":"WN18","model":"HolE","rank_in_archive_order":21,"of":37,"metrics":{"Hits@1":"0.930","Hits@10":"0.949","Hits@3":"0.945","MRR":"0.938"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1510.04935","atlas_url":"https://app.syntology.ai/?focus=1510.04935","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1510.04935"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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