{"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/differentiating-concepts-and-instances-for","title":"Differentiating Concepts and Instances for Knowledge Graph Embedding","arxiv_id":"1811.04588","date":"2018-11-12","proceeding":"EMNLP 2018 10","authors":["Xin Lv","Lei Hou","Juanzi Li","Zhiyuan Liu"],"abstract":"Concepts, which represent a group of different instances sharing common\nproperties, are essential information in knowledge representation. Most\nconventional knowledge embedding methods encode both entities (concepts and\ninstances) and relations as vectors in a low dimensional semantic space\nequally, ignoring the difference between concepts and instances. In this paper,\nwe propose a novel knowledge graph embedding model named TransC by\ndifferentiating concepts and instances. Specifically, TransC encodes each\nconcept in knowledge graph as a sphere and each instance as a vector in the\nsame semantic space. We use the relative positions to model the relations\nbetween concepts and instances (i.e., instanceOf), and the relations between\nconcepts and sub-concepts (i.e., subClassOf). We evaluate our model on both\nlink prediction and triple classification tasks on the dataset based on YAGO.\nExperimental results show that TransC outperforms state-of-the-art methods, and\ncaptures the semantic transitivity for instanceOf and subClassOf relation. Our\ncodes and datasets can be obtained from https:// github.com/davidlvxin/TransC.","url_abs":"http://arxiv.org/abs/1811.04588v1","url_pdf":"http://arxiv.org/pdf/1811.04588v1.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":"differentiating-concepts-and-instances-for","repo_url":"https://github.com/davidlvxin/TransC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"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":"triple-classification","task_name":"Triple Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-yago39k","task":"Link Prediction","dataset":"YAGO39K","model":"TransC (bern)","rank_in_archive_order":1,"of":1,"metrics":{"Hits@1":"0.298","Hits@10":"0.698","Hits@3":"0.502","MRR":"0.42"},"uses_additional_data":false},{"leaderboard":"/sota/triple-classification-on-yago39k","task":"Triple Classification","dataset":"YAGO39K","model":"TransC (bern)","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"93.8","F1-Score":"93.7","Precision":"94.8","Recall":"92.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.04588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.04588"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/davidlvxin/TransC","reach":null}],"summary":{"ran_fixture":1,"ran_honours":2},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"cd402801e7b0ddb3","entry":"norm","repo":"davidlvxin/TransC","repo_kind":"official","path":"py_version/transc.py","file_url":"https://github.com/davidlvxin/TransC/blob/HEAD/py_version/transc.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cd402801e7b0ddb3"}},{"code_sha256_prefix":"8fe984ebccd9563c","entry":"normalize_emb","repo":"davidlvxin/TransC","repo_kind":"official","path":"py_version/transc.py","file_url":"https://github.com/davidlvxin/TransC/blob/HEAD/py_version/transc.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8fe984ebccd9563c"}},{"code_sha256_prefix":"ca9a11f7f26cc61a","entry":"normalize_radius","repo":"davidlvxin/TransC","repo_kind":"official","path":"py_version/transc.py","file_url":"https://github.com/davidlvxin/TransC/blob/HEAD/py_version/transc.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ca9a11f7f26cc61a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}