{"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/dhge-dual-view-hyper-relational-knowledge","title":"DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing","arxiv_id":"2207.08562","date":"2022-07-18","proceeding":"AAAI 2023 6","authors":["Haoran Luo","Haihong E","Ling Tan","Gengxian Zhou","Tianyu Yao","Kaiyang Wan"],"abstract":"In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute-value descriptions, which is considered more comprehensive and specific than a triple-based fact. However, currently available hyper-relational KG embedding methods in a single view are limited in application because they weaken the hierarchical structure that represents the affiliation between entities. To overcome this limitation, we propose a dual-view hyper-relational KG structure (DH-KG) that contains a hyper-relational instance view for entities and a hyper-relational ontology view for concepts that are abstracted hierarchically from the entities. This paper defines link prediction and entity typing tasks on DH-KG for the first time and constructs two DH-KG datasets, JW44K-6K, extracted from Wikidata, and HTDM based on medical data. Furthermore, we propose DHGE, a DH-KG embedding model based on GRAN encoders, HGNNs, and joint learning. DHGE outperforms baseline models on DH-KG, according to experimental results. Finally, we provide an example of how this technology can be used to treat hypertension. Our model and new datasets are publicly available.","url_abs":"https://arxiv.org/abs/2207.08562v4","url_pdf":"https://arxiv.org/pdf/2207.08562v4.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":"dhge-dual-view-hyper-relational-knowledge","repo_url":"https://github.com/lhrlab/dhge","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"entity-typing","task_name":"Entity Typing"},{"task_slug":"entity-typing-on-dh-kgs","task_name":"Entity Typing on DH-KGs"},{"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":"link-prediction-on-dh-kgs","task_name":"Link prediction on DH-KGs"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"ontology","method_name":"Ontology"}],"datasets_introduced":[{"slug":"htdm","name":"HTDM","full_name":"Hypertention Disease Medication"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-dh-kgs-on-jw44k-6k","task":"Link prediction on DH-KGs","dataset":"JW44K-6K","model":"DHGE","rank_in_archive_order":1,"of":1,"metrics":{"ET-H@1":"0.637","ET-H@10":"0.805","ET-MRR":"0.690","LP-H@1":"0.388","LP-H@10":"0.575","LP-MRR":"0.453"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.08562","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}