{"id":"https://openalex.org/W4385439062","doi":"https://doi.org/10.24963/kr.2023/45","title":"Revisiting Inferential Benchmarks for Knowledge Graph Completion","display_name":"Revisiting Inferential Benchmarks for Knowledge Graph Completion","publication_year":2023,"publication_date":"2023-07-31","ids":{"openalex":"https://openalex.org/W4385439062","doi":"https://doi.org/10.24963/kr.2023/45"},"language":"en","primary_location":{"id":"doi:10.24963/kr.2023/45","is_oa":true,"landing_page_url":"http://dx.doi.org/10.24963/kr.2023/45","pdf_url":"https://proceedings.kr.org/2023/45/kr2023-0045-liu-et-al.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twentieth International Conference on Principles of Knowledge Representation and Reasoning","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://proceedings.kr.org/2023/45/kr2023-0045-liu-et-al.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101589565","display_name":"Shuwen Liu","orcid":"https://orcid.org/0000-0002-3487-7498"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Shuwen Liu","raw_affiliation_strings":["University of Oxford"],"affiliations":[{"raw_affiliation_string":"University of Oxford","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023732514","display_name":"Bernardo Cuenca Grau","orcid":"https://orcid.org/0000-0003-2909-5923"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Bernardo Cuenca Grau","raw_affiliation_strings":["University of Oxford"],"affiliations":[{"raw_affiliation_string":"University of Oxford","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060350556","display_name":"Ian Horrocks","orcid":"https://orcid.org/0000-0002-2685-7462"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ian Horrocks","raw_affiliation_strings":["University of Oxford"],"affiliations":[{"raw_affiliation_string":"University of Oxford","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071446856","display_name":"Egor V. Kostylev","orcid":"https://orcid.org/0000-0002-8886-6129"},"institutions":[{"id":"https://openalex.org/I184942183","display_name":"University of Oslo","ror":"https://ror.org/01xtthb56","country_code":"NO","type":"education","lineage":["https://openalex.org/I184942183"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Egor V. Kostylev","raw_affiliation_strings":["University of Oslo"],"affiliations":[{"raw_affiliation_string":"University of Oslo","institution_ids":["https://openalex.org/I184942183"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101589565"],"corresponding_institution_ids":["https://openalex.org/I40120149"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08531327,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"461","last_page":"471"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7523865699768066},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7233331203460693},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5980366468429565},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5864312648773193},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5437546372413635},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5411355495452881},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5370748043060303},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.48811110854148865},{"id":"https://openalex.org/keywords/rule-of-inference","display_name":"Rule of inference","score":0.4849162697792053},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43369239568710327},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42216402292251587},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4142375886440277},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3414105176925659},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13316896557807922}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7523865699768066},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7233331203460693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5980366468429565},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5864312648773193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5437546372413635},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5411355495452881},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5370748043060303},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.48811110854148865},{"id":"https://openalex.org/C3746660","wikidata":"https://www.wikidata.org/wiki/Q1068763","display_name":"Rule of inference","level":2,"score":0.4849162697792053},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43369239568710327},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42216402292251587},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4142375886440277},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3414105176925659},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13316896557807922},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/kr.2023/45","is_oa":true,"landing_page_url":"http://dx.doi.org/10.24963/kr.2023/45","pdf_url":"https://proceedings.kr.org/2023/45/kr2023-0045-liu-et-al.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twentieth International Conference on Principles of Knowledge Representation and Reasoning","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/kr.2023/45","is_oa":true,"landing_page_url":"http://dx.doi.org/10.24963/kr.2023/45","pdf_url":"https://proceedings.kr.org/2023/45/kr2023-0045-liu-et-al.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twentieth International Conference on Principles of Knowledge Representation and Reasoning","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7599999904632568,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G3253514882","display_name":"ConCur: Knowledge Base Construction and Curation","funder_award_id":"EP/V050869/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G3504612760","display_name":null,"funder_award_id":"237889","funder_id":"https://openalex.org/F4320323299","funder_display_name":"Norges Forskningsr\u00e5d"},{"id":"https://openalex.org/G3748656914","display_name":null,"funder_award_id":"Norway","funder_id":"https://openalex.org/F4320323299","funder_display_name":"Norges Forskningsr\u00e5d"},{"id":"https://openalex.org/G5652537686","display_name":"UK FIRES: Locating Resource Efficiency at the heart of Future Industrial Strategy in the UK","funder_award_id":"EP/S019111/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G667038190","display_name":"OASIS: Ontology Reasoning over Frequently-changing and Streaming Data","funder_award_id":"EP/S032347/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7097328358","display_name":null,"funder_award_id":"EP/S019111/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320323299","display_name":"Norges Forskningsr\u00e5d","ror":"https://ror.org/00epmv149"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4385439062.pdf"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W102708294","https://openalex.org/W1533230146","https://openalex.org/W1989009626","https://openalex.org/W2097266862","https://openalex.org/W2127426251","https://openalex.org/W2127795553","https://openalex.org/W2155653793","https://openalex.org/W2250184916","https://openalex.org/W2251960799","https://openalex.org/W2294131806","https://openalex.org/W2432356473","https://openalex.org/W2548746141","https://openalex.org/W2572179331","https://openalex.org/W2604314403","https://openalex.org/W2728059831","https://openalex.org/W2759136286","https://openalex.org/W2891112820","https://openalex.org/W2891420675","https://openalex.org/W2907184427","https://openalex.org/W2909137510","https://openalex.org/W2949434543","https://openalex.org/W2966298461","https://openalex.org/W3006415306","https://openalex.org/W3012474999","https://openalex.org/W3032390337","https://openalex.org/W3034374701","https://openalex.org/W3086449220","https://openalex.org/W3094451405","https://openalex.org/W3099836348","https://openalex.org/W3106439716","https://openalex.org/W3173795766","https://openalex.org/W4287029068","https://openalex.org/W4287724053","https://openalex.org/W4288083289","https://openalex.org/W6604189946","https://openalex.org/W6647556548","https://openalex.org/W6697278176","https://openalex.org/W6740216407","https://openalex.org/W6770127619","https://openalex.org/W6774977730","https://openalex.org/W6777251412"],"related_works":["https://openalex.org/W4213427734","https://openalex.org/W3203330043","https://openalex.org/W2119264717","https://openalex.org/W102683223","https://openalex.org/W2710049634","https://openalex.org/W1559068841","https://openalex.org/W2047356095","https://openalex.org/W1984225148","https://openalex.org/W2547621624","https://openalex.org/W146952150"],"abstract_inverted_index":{"Knowledge":[0],"Graph":[1],"(KG)":[2],"completion":[3,24,48,96,184],"is":[4,25,104],"the":[5,34,38,45,62,83,100,112,116,119,122,132,135,140,144,147,150,157,165,192],"problem":[6],"of":[7,18,40,67,74,85,107,118,139,142,181,194],"extending":[8],"an":[9],"incomplete":[10,202],"KG":[11,23,77,95,183],"with":[12],"missing":[13,113],"facts.":[14],"A":[15],"key":[16],"feature":[17],"Machine":[19],"Learning":[20],"approaches":[21],"for":[22,54,93],"their":[26],"ability":[27,193],"to":[28,44,58,146,155,172,197],"learn":[29,59],"inference":[30,86,199],"patterns,":[31,60],"so":[32,110],"that":[33,111],"predicted":[35],"facts":[36,114],"are":[37,51,70,115,153],"results":[39,117,141,187],"applying":[41,143],"these":[42,68],"patterns":[43,200],"KG.":[46],"Standard":[47],"benchmarks,":[49],"however,":[50],"not":[52,81,162],"well-suited":[53],"evaluating":[55],"models'":[56],"abilities":[57],"because":[61],"training":[63,123,148],"and":[64,78,131,176],"test":[65,136],"sets":[66],"benchmarks":[69,97,175],"a":[71,75,90,105,178],"random":[72],"split":[73],"given":[76],"hence":[79],"do":[80],"capture":[82],"causality":[84],"patterns.":[87],"We":[88,168],"propose":[89],"novel":[91,189],"approach":[92],"designing":[94],"based":[98],"on":[99,191],"following":[101],"principles:":[102],"there":[103],"set":[106,124,137],"logical":[108],"rules":[109,145,161],"rules'":[120],"application;":[121],"includes":[125],"both":[126],"premises":[127],"matching":[128],"rule":[129,166],"antecedents":[130],"corresponding":[133],"conclusions;":[134],"consists":[138],"set;":[149],"negative":[151],"examples":[152],"designed":[154],"discourage":[156],"models":[158,196],"from":[159,201],"learning":[160],"entailed":[163],"by":[164],"set.":[167],"use":[169],"our":[170],"methodology":[171],"generate":[173],"several":[174],"evaluate":[177],"wide":[179],"range":[180],"existing":[182,195],"systems.":[185],"Our":[186],"provide":[188],"insights":[190],"induce":[198],"KGs.":[203]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
