{"id":"https://openalex.org/W4410632686","doi":"https://doi.org/10.1145/3701716.3715560","title":"AC-TKG+: Actor-Critic with Multi-Level Loss for Effective Few-shot Inductive Learning on Temporal Knowledge Graphs","display_name":"AC-TKG+: Actor-Critic with Multi-Level Loss for Effective Few-shot Inductive Learning on Temporal Knowledge Graphs","publication_year":2025,"publication_date":"2025-05-08","ids":{"openalex":"https://openalex.org/W4410632686","doi":"https://doi.org/10.1145/3701716.3715560"},"language":"en","primary_location":{"id":"doi:10.1145/3701716.3715560","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3715560","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715560","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715560","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107885833","display_name":"Mingyuan Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Mingyuan Zhao","raw_affiliation_strings":["University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037376402","display_name":"Jianren Gu","orcid":"https://orcid.org/0009-0005-3413-4829"},"institutions":[{"id":"https://openalex.org/I146552867","display_name":"University of Puget Sound","ror":"https://ror.org/042drmv40","country_code":"US","type":"education","lineage":["https://openalex.org/I146552867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiafeng Gu","raw_affiliation_strings":["University of Puget Sound, Tacoma, WA, USA"],"raw_orcid":"https://orcid.org/0009-0005-3413-4829","affiliations":[{"raw_affiliation_string":"University of Puget Sound, Tacoma, WA, USA","institution_ids":["https://openalex.org/I146552867"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113159317","display_name":"Wenjie Zhang","orcid":"https://orcid.org/0009-0008-9897-8416"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wenjie Zhang","raw_affiliation_strings":["University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I31746571"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5107885833"],"corresponding_institution_ids":["https://openalex.org/I31746571"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05017301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1544","last_page":"1548"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9995999932289124,"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":0.9995999932289124,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9980999827384949,"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":0.9952999949455261,"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/computer-science","display_name":"Computer science","score":0.6296157836914062},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.6205623149871826},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.42919737100601196},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38718879222869873},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.07993406057357788}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6296157836914062},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.6205623149871826},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.42919737100601196},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38718879222869873},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.07993406057357788},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3701716.3715560","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3715560","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715560","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3701716.3715560","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3715560","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715560","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality"}],"awards":[{"id":"https://openalex.org/G4664391394","display_name":null,"funder_award_id":"DP230101445","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410632686.pdf","grobid_xml":"https://content.openalex.org/works/W4410632686.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W2997738974","https://openalex.org/W3182741322","https://openalex.org/W4289839702","https://openalex.org/W4306801146","https://openalex.org/W4383106474","https://openalex.org/W4386803880"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2074502265","https://openalex.org/W4214877189","https://openalex.org/W2773965352","https://openalex.org/W2381179799","https://openalex.org/W2980279061","https://openalex.org/W2334685461","https://openalex.org/W2366718574"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3,14],"enhanced":[4],"approach":[5],"for":[6],"few-shot":[7],"temporal":[8],"knowledge":[9],"graph":[10],"reasoning":[11],"based":[12],"on":[13],"Actor-Critic":[15],"framework.Through":[16],"the":[17,33,37,66,69],"integration":[18],"of":[19,68],"discrete":[20],"entity-level":[21],"supervision,":[22],"continuous":[23],"state-value":[24],"estimation,":[25],"and":[26,54],"path-based":[27],"global":[28,55],"signals":[29],"from":[30],"Dijkstra's":[31],"algorithm,":[32],"method":[34,71],"effectively":[35],"addresses":[36],"optimization":[38],"challenges":[39],"in":[40,85],"existing":[41],"approaches.Our":[42],"tri-level":[43],"loss":[44],"architecture":[45],"enables":[46],"comprehensive":[47],"learning":[48],"at":[49],"entity":[50],"selection,":[51],"state":[52],"evaluation,":[53],"path":[56],"assessment":[57],"levels,":[58],"while":[59],"maintaining":[60],"dynamic":[61],"exploration-exploitation":[62],"balance.Empirical":[63],"evaluations":[64],"demonstrate":[65],"superiority":[67],"proposed":[70],"over":[72],"current":[73],"state-of-the-art":[74],"baselines":[75],"across":[76],"multiple":[77],"performance":[78],"metrics,":[79],"with":[80],"notably":[81],"substantial":[82],"gains":[83],"observed":[84],"sparse-data":[86],"scenarios.":[87]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
