{"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/meim-multi-partition-embedding-interaction","title":"MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction","arxiv_id":"2209.15597","date":"2022-09-30","proceeding":null,"authors":["Hung Nghiep Tran","Atsuhiro Takasu"],"abstract":"Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs. Tensor-decomposition-based models, such as ComplEx, provide a good trade-off between efficiency and expressiveness, that is crucial because of the large size of real world knowledge graphs. The recent multi-partition embedding interaction (MEI) model subsumes these models by using the block term tensor format and provides a systematic solution for the trade-off. However, MEI has several drawbacks, some of which carried from its subsumed tensor-decomposition-based models. In this paper, we address these drawbacks and introduce the Multi-partition Embedding Interaction iMproved beyond block term format (MEIM) model, with independent core tensor for ensemble effects and soft orthogonality for max-rank mapping, in addition to multi-partition embedding. MEIM improves expressiveness while still being highly efficient, helping it to outperform strong baselines and achieve state-of-the-art results on difficult link prediction benchmarks using fairly small embedding sizes. The source code is released at https://github.com/tranhungnghiep/MEIM-KGE.","url_abs":"https://arxiv.org/abs/2209.15597v2","url_pdf":"https://arxiv.org/pdf/2209.15597v2.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":"meim-multi-partition-embedding-interaction","repo_url":"https://github.com/tranhungnghiep/meim-kge","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"meim-multi-partition-embedding-interaction","repo_url":"https://github.com/tranhungnghiep/AnalyzeKGE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"meim-multi-partition-embedding-interaction","repo_url":"https://github.com/tranhungnghiep/AnalyzingKGEmbeddings","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"meim-multi-partition-embedding-interaction","repo_url":"https://github.com/tranhungnghiep/MEI-KGE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"knowledge-graph-embedding","task_name":"Knowledge Graph Embedding"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"tensor-decomposition","task_name":"Tensor Decomposition"},{"task_slug":"world-knowledge","task_name":"World Knowledge"}],"methods":[{"method_slug":"mei","method_name":"MEI"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-fb15k-237","task":"Link Prediction","dataset":"FB15k-237","model":"MEIM","rank_in_archive_order":11,"of":75,"metrics":{"Hits@1":"0.274","Hits@10":"0.557","Hits@3":"0.406","MRR":"0.369"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wn18rr","task":"Link Prediction","dataset":"WN18RR","model":"MEIM","rank_in_archive_order":31,"of":75,"metrics":{"Hits@1":"0.458","Hits@10":"0.577","Hits@3":"0.518","MRR":"0.499"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-yago3-10","task":"Link Prediction","dataset":"YAGO3-10","model":"MEIM","rank_in_archive_order":1,"of":18,"metrics":{"Hits@1":"0.514","Hits@10":"0.716","Hits@3":"0.625","MRR":"0.585"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2209.15597","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.15597"}},"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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