{"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/a-novel-embedding-model-for-knowledge-base","title":"A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network","arxiv_id":"1712.02121","date":"2017-12-06","proceeding":"NAACL 2018 6","authors":["Dai Quoc Nguyen","Tu Dinh Nguyen","Dat Quoc Nguyen","Dinh Phung"],"abstract":"In this paper, we propose a novel embedding model, named ConvKB, for\nknowledge base completion. Our model ConvKB advances state-of-the-art models by\nemploying a convolutional neural network, so that it can capture global\nrelationships and transitional characteristics between entities and relations\nin knowledge bases. In ConvKB, each triple (head entity, relation, tail entity)\nis represented as a 3-column matrix where each column vector represents a\ntriple element. This 3-column matrix is then fed to a convolution layer where\nmultiple filters are operated on the matrix to generate different feature maps.\nThese feature maps are then concatenated into a single feature vector\nrepresenting the input triple. The feature vector is multiplied with a weight\nvector via a dot product to return a score. This score is then used to predict\nwhether the triple is valid or not. Experiments show that ConvKB achieves\nbetter link prediction performance than previous state-of-the-art embedding\nmodels on two benchmark datasets WN18RR and FB15k-237.","url_abs":"http://arxiv.org/abs/1712.02121v2","url_pdf":"http://arxiv.org/pdf/1712.02121v2.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":"a-novel-embedding-model-for-knowledge-base","repo_url":"https://github.com/daiquocnguyen/ConvKB","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"a-novel-embedding-model-for-knowledge-base","repo_url":"https://github.com/bi-graph/emgraph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"knowledge-base-completion","task_name":"Knowledge Base Completion"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":null,"task_name":"valid"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-wn18rr","task":"Link Prediction","dataset":"WN18RR","model":"ConvKB","rank_in_archive_order":61,"of":75,"metrics":{"Hits@10":"0.525","MR":"2554.0","MRR":"0.248"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.02121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.02121"}},"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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