{"id":"https://openalex.org/W7138969391","doi":"https://doi.org/10.1109/globecom59602.2025.11432447","title":"Analysis of Semantic Communication for Logic-based Hypothesis Deduction","display_name":"Analysis of Semantic Communication for Logic-based Hypothesis Deduction","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W7138969391","doi":"https://doi.org/10.1109/globecom59602.2025.11432447"},"language":null,"primary_location":{"id":"doi:10.1109/globecom59602.2025.11432447","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom59602.2025.11432447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043518756","display_name":"Ahmet Faruk Saz","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ahmet Faruk Saz","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037205304","display_name":"Siheng Xiong","orcid":"https://orcid.org/0000-0002-5274-9457"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siheng Xiong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5083854532","display_name":"Faramarz Fekri","orcid":"https://orcid.org/0000-0001-5008-8803"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Faramarz Fekri","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043518756"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88144846,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5701","last_page":"5707"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11010","display_name":"Logic, Reasoning, and Knowledge","score":0.2759000062942505,"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/T11010","display_name":"Logic, Reasoning, and Knowledge","score":0.2759000062942505,"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":0.12430000305175781,"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/T11321","display_name":"Error Correcting Code Techniques","score":0.06279999762773514,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/bayesian-probability","display_name":"Bayesian probability","score":0.5634999871253967},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5497999787330627},{"id":"https://openalex.org/keywords/permutation","display_name":"Permutation (music)","score":0.5063999891281128},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5062000155448914},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5009999871253967},{"id":"https://openalex.org/keywords/transmitter","display_name":"Transmitter","score":0.4927999973297119},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4925999939441681},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.45010000467300415},{"id":"https://openalex.org/keywords/complete-information","display_name":"Complete information","score":0.43880000710487366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6402000188827515},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5634999871253967},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5497999787330627},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.5063999891281128},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5062000155448914},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5009999871253967},{"id":"https://openalex.org/C47798520","wikidata":"https://www.wikidata.org/wiki/Q190157","display_name":"Transmitter","level":3,"score":0.4927999973297119},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.45010000467300415},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3901999890804291},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.37790000438690186},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3734000027179718},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3402000069618225},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.33550000190734863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3310000002384186},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.303600013256073},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2944999933242798},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27880001068115234},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27730000019073486},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom59602.2025.11432447","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom59602.2025.11432447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1525461050","https://openalex.org/W2946889564","https://openalex.org/W3034830866","https://openalex.org/W3036851434","https://openalex.org/W3173805051","https://openalex.org/W3197326271","https://openalex.org/W4250589301","https://openalex.org/W4310902909","https://openalex.org/W4313455370","https://openalex.org/W4388685105","https://openalex.org/W4401717638","https://openalex.org/W4410229183"],"related_works":[],"abstract_inverted_index":{"This":[0],"work":[1],"presents":[2],"an":[3,135],"analysis":[4],"of":[5,11,22,27],"semantic":[6],"communication":[7,149],"in":[8],"the":[9,17,25,28,32,38,45,57,60,69,73,77,80,95,104,131,142,164],"context":[10],"First-Order":[12],"Logic":[13],"(FOL)-based":[14],"deduction.":[15],"Specifically,":[16],"receiver":[18,58],"holds":[19],"a":[20,88,99],"set":[21],"hypotheses":[23],"about":[24,37],"State":[26],"World":[29],"(SotW),":[30],"while":[31],"transmitter":[33,49,78],"has":[34],"incomplete":[35],"evidence":[36],"true":[39,65],"SotW":[40],"but":[41],"lacks":[42],"access":[43],"to":[44,51,55,79,87],"ground":[46],"truth.":[47],"The":[48],"aims":[50],"communicate":[52],"limited":[53],"information":[54],"help":[56],"identify":[59],"hypothesis":[61,138],"most":[62],"consistent":[63],"with":[64],"SotW.":[66],"We":[67,157],"formulate":[68],"objective":[70],"as":[71,134],"approximating":[72],"posterior":[74],"distribution":[75],"at":[76],"receiver.":[81],"Using":[82],"Stirling\u2019s":[83],"approximation,":[84],"this":[85],"reduces":[86],"constrained,":[89],"finite-horizon":[90],"resource":[91],"allocation":[92],"problem.":[93,140],"Applying":[94],"Karush-Kuhn-Tucker":[96],"conditions":[97],"yields":[98],"truncated":[100],"water-filling":[101],"solution.":[102],"Despite":[103],"problem\u2019s":[105],"non-convexity,":[106],"symmetry":[107],"and":[108,125,129,162,175],"permutation":[109],"invariance":[110],"ensure":[111],"global":[112],"optimality.":[113],"Based":[114],"on":[115],"this,":[116],"we":[117],"design":[118],"message":[119],"selection":[120,174],"strategies,":[121],"both":[122],"for":[123],"single-":[124],"multi-":[126],"round":[127],"communication,":[128],"model":[130],"receiver\u2019s":[132],"inference":[133],"m-ary":[136],"Bayesian":[137],"testing":[139],"Under":[141],"Maximum":[143],"A":[144],"Posteriori":[145],"(MAP)":[146],"rule,":[147],"our":[148],"strategy":[150],"achieves":[151],"optimal":[152],"performance":[153],"within":[154],"budget":[155],"constraints.":[156],"further":[158],"analyze":[159],"convergence":[160],"rates":[161],"validate":[163],"theoretical":[165],"findings":[166],"through":[167],"experiments,":[168],"demonstrating":[169],"reduced":[170],"error":[171],"over":[172],"random":[173],"prior":[176],"methods.":[177]},"counts_by_year":[],"updated_date":"2026-03-20T20:54:20.808490","created_date":"2026-03-20T00:00:00"}
