{"id":"W7139075339","doi":"10.5220/0006294900001535","title":"A LRAAM-based Partial Order Function for Ontology Matching in the Context of Service Discovery","year":2017,"lang":"","type":"article","venue":"","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Ontology; Context (archaeology); Matching (statistics); Function (biology); Service discovery; Order (exchange)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003861767,0.0005960092,0.002039024,0.003245794,0.001440085,0.003293518,0.002502849,0.00158282,0.004511608],"category_scores_gemma":[0.01103901,0.0004904661,0.002272165,0.003106748,0.0009132652,0.005196571,0.002606229,0.001776097,0.002082412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002058742,"about_ca_system_score_gemma":0.00424367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007729476,"about_ca_topic_score_gemma":0.008174689,"domain_scores_codex":[0.9959261,0.001218889,0.0005626083,0.0006475211,0.001321265,0.0003235471],"domain_scores_gemma":[0.9961116,0.001513941,0.000234819,0.001083239,0.0008852482,0.0001712749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006071039,0.0005402565,0.002632262,0.0005587977,0.0001927221,0.0003868841,0.0004818088,0.06888705,0.01577865,0.198359,0.009810848,0.7017646],"study_design_scores_gemma":[0.00004225068,0.0001754177,0.0006779032,0.00007400446,0.0001113379,0.0003706319,0.0001410196,0.868926,0.0129837,0.103584,0.01282958,0.00008415591],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008655837,0.0002557694,0.9865597,0.000167906,0.00007660747,0.0001248339,0.0003074119,0.002344582,0.001507288],"genre_scores_gemma":[0.1427156,0.0001954404,0.8528005,0.000139001,0.00006703391,0.0001816863,0.001089611,0.0002804247,0.002530679],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007729476,"threshold_uncertainty_score":0.02042323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02391249876911696,"score_gpt":0.2765816405357459,"score_spread":0.2526691417666289,"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."}}