{"id":"https://openalex.org/W7168262892","doi":"https://doi.org/10.48550/arxiv.2607.10197","title":"GRATE: Temporal Extensions for Inductive KG Foundation Models via Gated Rotary Attention","display_name":"GRATE: Temporal Extensions for Inductive KG Foundation Models via Gated Rotary Attention","publication_year":2026,"publication_date":"2026-07-11","ids":{"openalex":"https://openalex.org/W7168262892","doi":"https://doi.org/10.48550/arxiv.2607.10197"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.10197","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10197","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.10197","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140671334","display_name":"Jiaxin Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Jiaxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140613353","display_name":"Osama Mohammed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammed, Osama","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140620980","display_name":"Daniel Hern\u00e1ndez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hern\u00e1ndez, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140687304","display_name":"Steffen Staab","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Staab, Steffen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.776199996471405,"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":0.776199996471405,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.09939999878406525,"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":0.034699998795986176,"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/timestamp","display_name":"Timestamp","score":0.7189000248908997},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.5853999853134155},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.45590001344680786},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.430400013923645},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4302000105381012},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.39879998564720154},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.3937999904155731},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.37880000472068787},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.3619999885559082}],"concepts":[{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.7189000248908997},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7160999774932861},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.5853999853134155},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45590001344680786},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.430400013923645},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4203000068664551},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3937999904155731},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.37880000472068787},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3612000048160553},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.3422999978065491},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3391000032424927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3375999927520752},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.33570000529289246},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C168065819","wikidata":"https://www.wikidata.org/wiki/Q845566","display_name":"Debugging","level":2,"score":0.2662999927997589}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.10197","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10197","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.10197","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10197","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.633195698261261,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Knowledge":[0],"graph":[1],"foundation":[2,110],"models":[3,36,111],"such":[4],"as":[5],"Ultra":[6],"and":[7,22,46,75,95,124,135,145,150,155],"Trix":[8],"achieve":[9],"strong":[10],"inductive":[11,137],"transfer":[12,51,138],"by":[13,82],"learning":[14],"relation-graph":[15],"representations":[16],"that":[17,70],"generalise":[18],"to":[19,27,40,50,52,88,92,100],"unseen":[20],"entities":[21],"relations.":[23],"Extending":[24],"this":[25],"transferability":[26],"temporal":[28,35,129],"knowledge":[29],"graphs":[30],"(TKGs)":[31],"remains":[32],"challenging:":[33],"existing":[34],"tie":[37],"their":[38],"parameters":[39,74],"dataset-specific":[41],"entities,":[42,143],"relations,":[43,144],"or":[44],"timestamps":[45,146],"are":[47],"not":[48],"designed":[49],"TKGs":[53],"with":[54,141],"disjoint":[55,142],"vocabularies.":[56],"We":[57],"propose":[58],"GRATE":[59,105,163],"(Gated":[60],"Rotary":[61],"Attention":[62],"for":[63],"Temporal":[64],"Encoding),":[65],"an":[66],"entity-side":[67],"message":[68,86],"function":[69],"adds":[71],"no":[72],"learnable":[73],"encodes":[76],"time":[77,80,90],"through":[78],"relative":[79],"differences":[81],"rotating":[83],"each":[84],"edge":[85],"according":[87],"its":[89],"gap":[91],"the":[93,167],"query":[94],"applying":[96],"a":[97,159],"query-conditioned":[98],"gate":[99],"select":[101],"temporally":[102],"relevant":[103],"signals.":[104],"integrates":[106],"into":[107],"NBFNet-style":[108],"KG":[109],"while":[112],"preserving":[113],"structural":[114],"transferability.":[115],"Existing":[116],"TKG":[117],"benchmarks":[118,154],"evaluate":[119],"within":[120],"shared":[121],"train/test":[122],"vocabularies":[123],"cannot":[125],"directly":[126],"test":[127],"cross-dataset":[128],"transfer;":[130],"we":[131],"therefore":[132],"construct":[133],"GDELTIndT":[134],"WIKIIndT,":[136],"benchmark":[139],"suites":[140],"spanning":[147],"both":[148],"interpolation":[149],"extrapolation.":[151],"Across":[152],"these":[153],"held-out":[156],"forecasting":[157],"datasets,":[158],"single":[160],"jointly":[161],"pretrained":[162],"checkpoint":[164],"improves":[165],"over":[166],"static":[168],"base":[169],"model":[170],"in":[171],"most":[172],"settings.":[173]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-15T00:00:00"}