{"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/seek-segmented-embedding-of-knowledge-graphs","title":"SEEK: Segmented Embedding of Knowledge Graphs","arxiv_id":"2005.00856","date":"2020-05-02","proceeding":"ACL 2020 6","authors":["Wentao Xu","Shun Zheng","Liang He","Bin Shao","Jian Yin","Tie-Yan Liu"],"abstract":"In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at \\url{https://github.com/Wentao-Xu/SEEK}.","url_abs":"https://arxiv.org/abs/2005.00856v3","url_pdf":"https://arxiv.org/pdf/2005.00856v3.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":"seek-segmented-embedding-of-knowledge-graphs","repo_url":"https://github.com/Wentao-Xu/SEEK","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"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":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-fb15k","task":"Link Prediction","dataset":"FB15k","model":"SEEK","rank_in_archive_order":4,"of":23,"metrics":{"Hits@1":"0.792","Hits@10":"0.886","Hits@3":"0.841","MRR":"0.825"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-yago37","task":"Link Prediction","dataset":"YAGO37","model":"SEEK","rank_in_archive_order":1,"of":2,"metrics":{"Hits@1":"0.370","Hits@10":"0.622","Hits@3":"0.498","MRR":"0.454"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2005.00856","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}