{"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/message-passing-for-hyper-relational","title":"Message Passing for Hyper-Relational Knowledge Graphs","arxiv_id":"2009.10847","date":"2020-09-22","proceeding":"EMNLP 2020 11","authors":["Mikhail Galkin","Priyansh Trivedi","Gaurav Maheshwari","Ricardo Usbeck","Jens Lehmann"],"abstract":"Hyper-relational knowledge graphs (KGs) (e.g., Wikidata) enable associating additional key-value pairs along with the main triple to disambiguate, or restrict the validity of a fact. In this work, we propose a message passing based graph encoder - StarE capable of modeling such hyper-relational KGs. Unlike existing approaches, StarE can encode an arbitrary number of additional information (qualifiers) along with the main triple while keeping the semantic roles of qualifiers and triples intact. We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K. Our experiments demonstrate that StarE based LP model outperforms existing approaches across multiple benchmarks. We also confirm that leveraging qualifiers is vital for link prediction with gains up to 25 MRR points compared to triple-based representations.","url_abs":"https://arxiv.org/abs/2009.10847v1","url_pdf":"https://arxiv.org/pdf/2009.10847v1.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":"message-passing-for-hyper-relational","repo_url":"https://github.com/migalkin/StarE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"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-jf17k","task":"Link Prediction","dataset":"JF17K","model":"StarE (H) + Transformer (H)","rank_in_archive_order":2,"of":3,"metrics":{"Hit@1":"0.496","Hit@10":"0.725","Hit@5":"0.658","MRR":"0.574"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wd50k","task":"Link Prediction","dataset":"Temp8","model":"StarE (H) + Transformer (H)","rank_in_archive_order":2,"of":2,"metrics":{"Hit@1":"0.271","Hit@10":"0.496","MRR":"0.349"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2009.10847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.10847"}},"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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