{"id":"https://openalex.org/W7164044374","doi":"https://doi.org/10.48550/arxiv.2606.08362","title":"EmpiriGraph-Psy: A Dataset and LLM Pipeline for Extracting Empirical Relation Graphs from Psychology Abstracts","display_name":"EmpiriGraph-Psy: A Dataset and LLM Pipeline for Extracting Empirical Relation Graphs from Psychology Abstracts","publication_year":2026,"publication_date":"2026-06-06","ids":{"openalex":"https://openalex.org/W7164044374","doi":"https://doi.org/10.48550/arxiv.2606.08362"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.08362","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08362","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":null,"license_id":null,"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.2606.08362","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133787729","display_name":"Danqin Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zhao, Danqin","raw_affiliation_strings":["Department of Psychology, University of Warwick"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Psychology, University of Warwick","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053247799","display_name":"Y Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]},{"id":"https://openalex.org/I2802631561","display_name":"Australian Mathematical Sciences Institute","ror":"https://ror.org/00yn60108","country_code":"AU","type":"other","lineage":["https://openalex.org/I2802631561"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Liu, Yicun","raw_affiliation_strings":["Mathematical Sciences Institute, The Australian National University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mathematical Sciences Institute, The Australian National University","institution_ids":["https://openalex.org/I118347636","https://openalex.org/I2802631561"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085079234","display_name":"Xingwei Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tan, Xingwei","raw_affiliation_strings":["School of Computer Science, University of Sheffield"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Sheffield","institution_ids":["https://openalex.org/I91136226"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5137195276","display_name":"Thomas T. Hills","orcid":null},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hills, Thomas T.","raw_affiliation_strings":["Department of Psychology, University of Warwick"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Psychology, University of Warwick","institution_ids":["https://openalex.org/I39555362"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"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":0.33500000834465027,"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":0.33500000834465027,"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":0.2671999931335449,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.08089999854564667,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5827999711036682},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5546000003814697},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5358999967575073},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.527400016784668},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.46639999747276306},{"id":"https://openalex.org/keywords/hierarchy","display_name":"Hierarchy","score":0.4410000145435333},{"id":"https://openalex.org/keywords/moderation","display_name":"Moderation","score":0.42559999227523804},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.41440001130104065}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6970999836921692},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5827999711036682},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5546000003814697},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5358999967575073},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.527400016784668},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.46639999747276306},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45660001039505005},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.4410000145435333},{"id":"https://openalex.org/C93225998","wikidata":"https://www.wikidata.org/wiki/Q1941972","display_name":"Moderation","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.41440001130104065},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.41040000319480896},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.38029998540878296},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3569999933242798},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.34060001373291016},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3379000127315521},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.33640000224113464},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C90734943","wikidata":"https://www.wikidata.org/wiki/Q17008777","display_name":"Conceptualization","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2806999981403351},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.2687000036239624}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.08362","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08362","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.08362","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08362","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Existing":[0],"scientific":[1,54,172],"relation":[2,95,124],"extraction":[3,109],"benchmarks":[4],"mainly":[5],"target":[6],"domains":[7],"such":[8,29],"as":[9,30,36],"computer":[10],"science,":[11],"where":[12,32],"entities":[13],"are":[14,34,61],"tasks,":[15],"methods,":[16],"datasets,":[17],"materials,":[18],"or":[19],"metrics.":[20],"This":[21],"leaves":[22],"a":[23,79,111,141],"gap":[24],"in":[25],"variable-oriented":[26],"empirical":[27,47,68,94,165],"fields":[28],"psychology,":[31],"findings":[33],"expressed":[35],"relations":[37,150],"among":[38],"constructs,":[39],"measurements,":[40],"interventions,":[41],"and":[42,64,69,97,103,110,126,151,167],"outcomes.":[43],"We":[44,100],"introduce":[45],"variable-centered":[46],"graph":[48],"extraction,":[49,118,125,135],"the":[50,137,155,160],"task":[51],"of":[52,81,143,162],"mapping":[53],"abstracts":[55,84],"to":[56],"typed":[57],"graphs":[58],"whose":[59,65],"nodes":[60],"normalized":[62,90],"variables":[63],"edges":[66],"represent":[67],"hierarchical":[70],"relations.":[71],"To":[72],"support":[73],"this":[74],"task,":[75],"we":[76],"construct":[77],"EmpiriGraph-Psy,":[78],"benchmark":[80],"210":[82],"psychology":[83],"annotated":[85],"by":[86],"domain-trained":[87],"annotators":[88],"with":[89,136],"variables,":[91],"concept":[92,152],"hierarchies,":[93],"types,":[96],"validation":[98],"states.":[99],"evaluate":[101],"frontier":[102],"open-weight":[104],"LLMs":[105],"using":[106],"both":[107],"direct":[108,134],"staged":[112,130],"graph-construction":[113],"pipeline":[114,131],"that":[115,148],"separates":[116],"variable":[117],"normalization,":[119],"hierarchy":[120],"construction,":[121],"evidence":[122],"selection,":[123],"edge":[127],"validation.":[128],"The":[129],"substantially":[132],"outperforms":[133],"best":[138],"configuration":[139],"achieving":[140],"macro-F1":[142],"0.74.":[144],"Error":[145],"analysis":[146],"shows":[147],"moderation":[149],"hierarchies":[153],"remain":[154],"most":[156],"challenging":[157],"cases,":[158],"highlighting":[159],"difficulty":[161],"extracting":[163],"higher-order":[164],"claims":[166],"implicit":[168],"abstraction":[169],"structure":[170],"from":[171],"abstracts.":[173]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-10T00:00:00"}