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Several recent works suggest that convolutional neural network (CNN) based models generate richer and more expressive feature embeddings and hence also perform well on relation prediction. However, we observe that these KG embeddings treat triples independently and thus fail to cover the complex and hidden information that is inherently implicit in the local neighborhood surrounding a triple. To this effect, our paper proposes a novel attention based feature embedding that captures both entity and relation features in any given entity's neighborhood. Additionally, we also encapsulate relation clusters and multihop relations in our model. Our empirical study offers insights into the efficacy of our attention based model and we show marked performance gains in comparison to state of the art methods on all datasets.","url_abs":"https://arxiv.org/abs/1906.01195v1","url_pdf":"https://arxiv.org/pdf/1906.01195v1.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":"learning-attention-based-embeddings-for","repo_url":"https://github.com/deepakn97/relationPrediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"learning-attention-based-embeddings-for","repo_url":"https://github.com/KXY-PUBLIC/NASE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"knowledge-base-completion","task_name":"Knowledge Base Completion"},{"task_slug":"knowledge-graph-completion","task_name":"Knowledge Graph Completion"},{"task_slug":"knowledge-graph-embeddings","task_name":"Knowledge Graph Embeddings"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-prediction","task_name":"Relation Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/knowledge-graph-completion-on-fb15k-237","task":"Knowledge Graph Completion","dataset":"FB15k-237","model":"KBGAT","rank_in_archive_order":1,"of":4,"metrics":{"Hits@10":"62.6","Hits@3":"54"},"uses_additional_data":false},{"leaderboard":"/sota/knowledge-graph-completion-on-fb15k-237","task":"Knowledge Graph Completion","dataset":"FB15k-237","model":"KBAT","rank_in_archive_order":4,"of":4,"metrics":{"Hits@1":"46","MR":"0.210","MRR":"0.518"},"uses_additional_data":false},{"leaderboard":"/sota/knowledge-graph-completion-on-wn18rr","task":"Knowledge Graph Completion","dataset":"WN18RR","model":"KBGAT","rank_in_archive_order":2,"of":2,"metrics":{"Hits@1":"0.361","Hits@10":"0.581","Hits@3":"0.483"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wn18rr","task":"Link Prediction","dataset":"WN18RR","model":"KBGAT","rank_in_archive_order":25,"of":75,"metrics":{"Hits@1":"0.361","Hits@10":"0.581","Hits@3":"0.483","MR":"1940.0","MRR":"0.44"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1906.01195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01195"}},"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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