{"id":"W4400904993","doi":"10.1109/tkde.2024.3432767","title":"One Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completion","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Knowledge graph; Subgraph isomorphism problem; Induced subgraph isomorphism problem; Graph; Inductive reasoning; Theoretical computer science; Artificial intelligence; Line graph; Voltage graph","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00181579,0.001643479,0.001656705,0.002802323,0.001059988,0.001492216,0.003691169,0.0018266,0.005117625],"category_scores_gemma":[0.008066889,0.0006908782,0.00281506,0.002768902,0.001749441,0.00484566,0.003768218,0.003395834,0.001779691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001420182,"about_ca_system_score_gemma":0.002337858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008483996,"about_ca_topic_score_gemma":0.01273264,"domain_scores_codex":[0.9977525,0.0004944248,0.0001274713,0.0008393127,0.0005980736,0.000188184],"domain_scores_gemma":[0.9964918,0.001506156,0.0003373474,0.001026587,0.0004472465,0.0001907378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002283396,0.0003725745,0.002275139,0.000533672,0.0001611143,0.0006160329,0.0007208472,0.2978209,0.006834484,0.05517454,0.02343268,0.6118298],"study_design_scores_gemma":[0.00002406282,0.00004355514,0.0002306419,0.000029024,0.00003597059,0.0001161112,0.0001210866,0.9235815,0.003082531,0.068637,0.004076935,0.00002166429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00633413,0.0001751529,0.9897981,0.0002330102,0.000022956,0.0001184719,0.0003876977,0.00219683,0.0007336403],"genre_scores_gemma":[0.2248367,0.0004214643,0.7649707,0.0003990751,0.00008850302,0.0003676591,0.005340536,0.0005299234,0.003045302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008483996,"threshold_uncertainty_score":0.01712018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0577649098250396,"score_gpt":0.3025058440658079,"score_spread":0.2447409342407683,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}