{"id":"https://openalex.org/W7111285415","doi":"https://doi.org/10.1145/3731443.3771358","title":"Extracting Commonsense Knowledge for Robotic Agents from LLMs","display_name":"Extracting Commonsense Knowledge for Robotic Agents from LLMs","publication_year":2025,"publication_date":"2025-12-09","ids":{"openalex":"https://openalex.org/W7111285415","doi":"https://doi.org/10.1145/3731443.3771358"},"language":"en","primary_location":{"id":"doi:10.1145/3731443.3771358","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3731443.3771358","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Knowledge Capture Conference 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3731443.3771358","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mark Adamik","orcid":"https://orcid.org/0000-0002-7977-3617"},"institutions":[{"id":"https://openalex.org/I865915315","display_name":"Vrije Universiteit Amsterdam","ror":"https://ror.org/008xxew50","country_code":"NL","type":"education","lineage":["https://openalex.org/I865915315"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Mark Adamik","raw_affiliation_strings":["Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-7977-3617","affiliations":[{"raw_affiliation_string":"Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands","institution_ids":["https://openalex.org/I865915315"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ilaria Tiddi","orcid":"https://orcid.org/0000-0001-7116-9338"},"institutions":[{"id":"https://openalex.org/I865915315","display_name":"Vrije Universiteit Amsterdam","ror":"https://ror.org/008xxew50","country_code":"NL","type":"education","lineage":["https://openalex.org/I865915315"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ilaria Tiddi","raw_affiliation_strings":["Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands"],"raw_orcid":"https://orcid.org/0000-0001-7116-9338","affiliations":[{"raw_affiliation_string":"Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands","institution_ids":["https://openalex.org/I865915315"]}]},{"author_position":"last","author":{"id":null,"display_name":"Stefan Schlobach","orcid":"https://orcid.org/0000-0002-3282-1597"},"institutions":[{"id":"https://openalex.org/I865915315","display_name":"Vrije Universiteit Amsterdam","ror":"https://ror.org/008xxew50","country_code":"NL","type":"education","lineage":["https://openalex.org/I865915315"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Stefan Schlobach","raw_affiliation_strings":["Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-3282-1597","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands","institution_ids":["https://openalex.org/I865915315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I865915315"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.58523388,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"131","last_page":"138"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.3393999934196472,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.3393999934196472,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12090","display_name":"Language and cultural evolution","score":0.13510000705718994,"subfield":{"id":"https://openalex.org/subfields/3316","display_name":"Cultural Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.04560000076889992,"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/commonsense-knowledge","display_name":"Commonsense knowledge","score":0.7919999957084656},{"id":"https://openalex.org/keywords/commonsense-reasoning","display_name":"Commonsense reasoning","score":0.5787000060081482},{"id":"https://openalex.org/keywords/ontology","display_name":"Ontology","score":0.504800021648407},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4973999857902527},{"id":"https://openalex.org/keywords/embodied-cognition","display_name":"Embodied cognition","score":0.4535999894142151},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.44200000166893005},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.4334000051021576},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.4293999969959259}],"concepts":[{"id":"https://openalex.org/C30542707","wikidata":"https://www.wikidata.org/wiki/Q1603203","display_name":"Commonsense knowledge","level":3,"score":0.7919999957084656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7077999711036682},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6237000226974487},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.5787000060081482},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.504800021648407},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C100609095","wikidata":"https://www.wikidata.org/wiki/Q1335050","display_name":"Embodied cognition","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.44200000166893005},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.4334000051021576},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4300999939441681},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.4293999969959259},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3978999853134155},{"id":"https://openalex.org/C2777220311","wikidata":"https://www.wikidata.org/wiki/Q6423340","display_name":"Knowledge acquisition","level":2,"score":0.39340001344680786},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3522000014781952},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.3465000092983246},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C234837","wikidata":"https://www.wikidata.org/wiki/Q1420493","display_name":"Conceptual graph","level":3,"score":0.33880001306533813},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C192327766","wikidata":"https://www.wikidata.org/wiki/Q1038799","display_name":"Cognitive robotics","level":3,"score":0.2906000018119812},{"id":"https://openalex.org/C101230327","wikidata":"https://www.wikidata.org/wiki/Q826165","display_name":"Web Ontology Language","level":3,"score":0.2700999975204468},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2687000036239624},{"id":"https://openalex.org/C157170001","wikidata":"https://www.wikidata.org/wiki/Q4781507","display_name":"Applications of artificial intelligence","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2653000056743622}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3731443.3771358","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3731443.3771358","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Knowledge Capture Conference 2025","raw_type":"proceedings-article"},{"id":"pmh:oai:research.vu.nl:publications/5f859c14-a83c-4c89-933f-45a72ddbc6b8","is_oa":true,"landing_page_url":"https://research.vu.nl/en/publications/5f859c14-a83c-4c89-933f-45a72ddbc6b8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401107","display_name":"VU Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I865915315","host_organization_name":"Vrije Universiteit Amsterdam","host_organization_lineage":["https://openalex.org/I865915315"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adamik, M, Tiddi, I & Schlobach, S 2025, Extracting Commonsense Knowledge for Robotic Agents from LLMs. in C Shimizu, S Ferrada & L Kagal (eds), K-CAP 2025 - Proceedings of the 13th Knowledge Capture Conference 2025 : [Proceedings]. Association for Computing Machinery, Inc, pp. 131-138, 13th International Conference on Knowledge Capture, K-CAP 2025, Dayton, United States, 10/12/25. https://doi.org/10.1145/3731443.3771358","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:research.vu.nl:openaire_cris_publications/5f859c14-a83c-4c89-933f-45a72ddbc6b8","is_oa":true,"landing_page_url":"https://hdl.handle.net/1871.1/5f859c14-a83c-4c89-933f-45a72ddbc6b8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401107","display_name":"VU Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I865915315","host_organization_name":"Vrije Universiteit Amsterdam","host_organization_lineage":["https://openalex.org/I865915315"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Adamik, M, Tiddi, I & Schlobach, S 2025, Extracting Commonsense Knowledge for Robotic Agents from LLMs. in C Shimizu, S Ferrada & L Kagal (eds), K-CAP 2025 - Proceedings of the 13th Knowledge Capture Conference 2025 : [Proceedings]. Association for Computing Machinery, Inc, pp. 131-138, 13th International Conference on Knowledge Capture, K-CAP 2025, Dayton, United States, 10/12/25. https://doi.org/10.1145/3731443.3771358","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1145/3731443.3771358","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3731443.3771358","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Knowledge Capture Conference 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1791197240","https://openalex.org/W2080133951","https://openalex.org/W2081580037","https://openalex.org/W2104583100","https://openalex.org/W2107658650","https://openalex.org/W2263785794","https://openalex.org/W2574205862","https://openalex.org/W2619545562","https://openalex.org/W2740432174","https://openalex.org/W3207166518","https://openalex.org/W4224912544","https://openalex.org/W4229038710","https://openalex.org/W4240791294","https://openalex.org/W4286903249","https://openalex.org/W4287590700","https://openalex.org/W4287642521","https://openalex.org/W4296414573","https://openalex.org/W4383180571","https://openalex.org/W4389820524","https://openalex.org/W4392822529","https://openalex.org/W4394591548","https://openalex.org/W4396601131","https://openalex.org/W4396650728","https://openalex.org/W4398233570","https://openalex.org/W4401754921","https://openalex.org/W4403074774","https://openalex.org/W4408267896","https://openalex.org/W4408555470","https://openalex.org/W6967072037"],"related_works":[],"abstract_inverted_index":{"The":[0],"acquisition":[1],"of":[2,73,104,136],"commonsense":[3,48,167],"knowledge":[4,49,58],"remains":[5],"a":[6,43,56,101,160],"core":[7],"challenge":[8],"in":[9,33,70,169],"both":[10],"robotics":[11,170],"and":[12,26,52,87,108,123,139,147],"artificial":[13],"intelligence.":[14],"While":[15],"Large":[16],"Language":[17],"Models":[18],"(LLMs)":[19],"encode":[20],"rich":[21],"latent":[22],"knowledge,":[23],"their":[24,30],"unstructured":[25],"probabilistic":[27],"nature":[28],"limits":[29],"direct":[31],"use":[32,148],"safety-critical":[34],"domains":[35],"like":[36],"robotics.":[37],"In":[38],"this":[39],"paper,":[40],"we":[41],"present":[42],"pipeline":[44,99],"for":[45,64,164],"extracting":[46],"structured":[47,166],"from":[50],"LLMs":[51,156],"integrating":[53],"it":[54],"into":[55],"symbolic":[57],"graph":[59],"based":[60],"on":[61],"the":[62,71,98,134],"Ontology":[63],"Robotic":[65],"Knowledge":[66],"Acquisition":[67],"(ORKA).":[68],"Grounded":[69],"theory":[72],"conceptual":[74],"spaces,":[75],"our":[76],"method":[77],"targets":[78],"physical":[79],"object":[80],"properties\u2014both":[81],"categorical":[82],"(e.g.,":[83,89],"shape,":[84],"material,":[85],"location)":[86],"quantifiable":[88],"size,":[90],"weight,":[91],"temperature)\u2014relevant":[92],"to":[93,115],"embodied":[94],"reasoning.":[95],"We":[96,131],"evaluate":[97],"across":[100],"diverse":[102],"set":[103],"LLMs,":[105],"model":[106,145],"sizes,":[107],"quantization":[109],"levels,":[110],"using":[111],"human-annotated":[112],"ground":[113],"truth":[114],"assess":[116],"accuracy.":[117],"Results":[118],"show":[119],"that":[120,153],"even":[121,154],"compact":[122],"quantized":[124],"models":[125],"can":[126,157],"produce":[127],"reliable,":[128],"interpretable":[129],"knowledge.":[130],"also":[132],"analyze":[133],"influence":[135],"measurement":[137],"units":[138],"contextual":[140],"relevance,":[141],"highlighting":[142],"trade-offs":[143],"between":[144],"types":[146],"cases.":[149],"These":[150],"results":[151],"indicate":[152],"smaller":[155],"serve":[158],"as":[159],"useful":[161],"intermediate":[162],"source":[163],"deriving":[165],"representations":[168],"contexts.":[171]},"counts_by_year":[],"updated_date":"2025-12-10T02:49:46.989445","created_date":"2025-12-10T00:00:00"}