{"id":"https://openalex.org/W4414691077","doi":"https://doi.org/10.52825/ocp.v6i.2900","title":"silp_nlp at LLMs4OL 2025 Tasks A, B, C, and D: Clustering-Based Ontology Learning Using LLMs","display_name":"silp_nlp at LLMs4OL 2025 Tasks A, B, C, and D: Clustering-Based Ontology Learning Using LLMs","publication_year":2025,"publication_date":"2025-10-01","ids":{"openalex":"https://openalex.org/W4414691077","doi":"https://doi.org/10.52825/ocp.v6i.2900"},"language":"en","primary_location":{"id":"doi:10.52825/ocp.v6i.2900","is_oa":true,"landing_page_url":"https://doi.org/10.52825/ocp.v6i.2900","pdf_url":"https://www.tib-op.org/ojs/index.php/ocp/article/download/2900/2924","source":{"id":"https://openalex.org/S4220650788","display_name":"Open Conference Proceedings","issn_l":"2749-5841","issn":["2749-5841"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Conference Proceedings","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.tib-op.org/ojs/index.php/ocp/article/download/2900/2924","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107711115","display_name":"Pankaj Kumar Goyal","orcid":null},"institutions":[{"id":"https://openalex.org/I26072440","display_name":"Indian Institute of Information Technology Allahabad","ror":"https://ror.org/03rgjt374","country_code":"IN","type":"education","lineage":["https://openalex.org/I26072440"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pankaj Kumar Goyal","raw_affiliation_strings":["Indian Institute of Information Technology Allahabad"],"raw_orcid":"https://orcid.org/0009-0007-5501-9111","affiliations":[{"raw_affiliation_string":"Indian Institute of Information Technology Allahabad","institution_ids":["https://openalex.org/I26072440"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002585659","display_name":"Sumit Singh","orcid":"https://orcid.org/0000-0002-3292-4131"},"institutions":[{"id":"https://openalex.org/I26072440","display_name":"Indian Institute of Information Technology Allahabad","ror":"https://ror.org/03rgjt374","country_code":"IN","type":"education","lineage":["https://openalex.org/I26072440"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sumit Singh","raw_affiliation_strings":["Indian Institute of Information Technology Allahabad"],"raw_orcid":"https://orcid.org/0000-0002-3292-4131","affiliations":[{"raw_affiliation_string":"Indian Institute of Information Technology Allahabad","institution_ids":["https://openalex.org/I26072440"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060052491","display_name":"Uma Shanker Tiwary","orcid":"https://orcid.org/0000-0001-7206-9013"},"institutions":[{"id":"https://openalex.org/I26072440","display_name":"Indian Institute of Information Technology Allahabad","ror":"https://ror.org/03rgjt374","country_code":"IN","type":"education","lineage":["https://openalex.org/I26072440"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Uma Shanker Tiwary","raw_affiliation_strings":["Indian Institute of Information Technology Allahabad"],"raw_orcid":"https://orcid.org/0000-0001-7206-9013","affiliations":[{"raw_affiliation_string":"Indian Institute of Information Technology Allahabad","institution_ids":["https://openalex.org/I26072440"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I26072440"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3768315,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9987000226974487,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9987000226974487,"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":0.9980999827384949,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9976999759674072,"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/ontology","display_name":"Ontology","score":0.6383000016212463},{"id":"https://openalex.org/keywords/taxonomy","display_name":"Taxonomy (biology)","score":0.538100004196167},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.4634000062942505},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.436599999666214},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4341000020503998},{"id":"https://openalex.org/keywords/ontology-learning","display_name":"Ontology learning","score":0.4124000072479248},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.37540000677108765},{"id":"https://openalex.org/keywords/ontology-based-data-integration","display_name":"Ontology-based data integration","score":0.34139999747276306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7379000186920166},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.6383000016212463},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.538100004196167},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.4634000062942505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4569000005722046},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.436599999666214},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4341000020503998},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4334999918937683},{"id":"https://openalex.org/C2777002027","wikidata":"https://www.wikidata.org/wiki/Q3620938","display_name":"Ontology learning","level":5,"score":0.4124000072479248},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.37540000677108765},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3580000102519989},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3483999967575073},{"id":"https://openalex.org/C22550185","wikidata":"https://www.wikidata.org/wiki/Q7095047","display_name":"Ontology-based data integration","level":3,"score":0.34139999747276306},{"id":"https://openalex.org/C78726541","wikidata":"https://www.wikidata.org/wiki/Q3882785","display_name":"Upper ontology","level":3,"score":0.3285999894142151},{"id":"https://openalex.org/C2988080768","wikidata":"https://www.wikidata.org/wiki/Q7095057","display_name":"Semantic relation","level":3,"score":0.3102000057697296},{"id":"https://openalex.org/C37926939","wikidata":"https://www.wikidata.org/wiki/Q7449061","display_name":"Semantic equivalence","level":4,"score":0.30379998683929443},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.2757999897003174},{"id":"https://openalex.org/C101230327","wikidata":"https://www.wikidata.org/wiki/Q826165","display_name":"Web Ontology Language","level":3,"score":0.2750000059604645},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.27379998564720154},{"id":"https://openalex.org/C50382505","wikidata":"https://www.wikidata.org/wiki/Q2553356","display_name":"OWL-S","level":4,"score":0.2623000144958496}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.52825/ocp.v6i.2900","is_oa":true,"landing_page_url":"https://doi.org/10.52825/ocp.v6i.2900","pdf_url":"https://www.tib-op.org/ojs/index.php/ocp/article/download/2900/2924","source":{"id":"https://openalex.org/S4220650788","display_name":"Open Conference Proceedings","issn_l":"2749-5841","issn":["2749-5841"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Conference Proceedings","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.52825/ocp.v6i.2900","is_oa":true,"landing_page_url":"https://doi.org/10.52825/ocp.v6i.2900","pdf_url":"https://www.tib-op.org/ojs/index.php/ocp/article/download/2900/2924","source":{"id":"https://openalex.org/S4220650788","display_name":"Open Conference Proceedings","issn_l":"2749-5841","issn":["2749-5841"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Conference Proceedings","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414691077.pdf","grobid_xml":"https://content.openalex.org/works/W4414691077.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W2911489562","https://openalex.org/W3094447026","https://openalex.org/W4362640271","https://openalex.org/W4403071384","https://openalex.org/W4403071392","https://openalex.org/W4403071419","https://openalex.org/W4403071423","https://openalex.org/W4403071677","https://openalex.org/W4403071727","https://openalex.org/W4403071840","https://openalex.org/W4403071966","https://openalex.org/W4403072078"],"related_works":[],"abstract_inverted_index":{"This":[0,140],"paper":[1],"presents":[2],"the":[3,6,10,45,103,130],"participation":[4],"of":[5,105,132],"silp\\_nlp":[7],"team":[8],"in":[9,20,54,109,121],"LLMs4OL":[11],"2025":[12],"Challenge,":[13],"where":[14],"we":[15,48],"addressed":[16],"four":[17],"core":[18],"tasks":[19],"ontology":[21,152],"learning:":[22],"Text2Onto":[23],"(Task":[24,28,32,38],"A),":[25],"Term":[26],"Typing":[27],"B),":[29],"Taxonomy":[30],"Discovery":[31],"C),":[33],"and":[34,67,76,85,93,111,124,150],"Non-Taxonomic":[35],"Relation":[36],"Extraction":[37],"D).":[39],"Building":[40],"on":[41],"our":[42,106,115],"experience":[43],"from":[44,70],"first":[46],"edition,":[47],"proposed":[49],"a":[50],"clustering-enhanced":[51],"methodology":[52],"grounded":[53],"large":[55],"language":[56],"models":[57,62],"(LLMs),":[58],"integrating":[59],"domain-adapted":[60],"transformer":[61],"such":[63],"as":[64],"pranav-s/MaterialsBERT,":[65],"dmis-lab/biobert-v1.1,":[66],"proprietary":[68],"LLMs":[69],"Grok.":[71],"Our":[72],"framework":[73],"combined":[74],"lexical":[75],"semantic":[77,88],"clustering":[78,133],"with":[79,136],"adaptive":[80],"prompting":[81],"to":[82],"tackle":[83],"entity":[84],"type":[86],"extraction,":[87],"classification,":[89],"hierarchical":[90],"structure":[91],"discovery,":[92],"complex":[94],"relation":[95,126],"modeling.":[96],"Experimental":[97],"results":[98],"across":[99,154],"18":[100],"subtasks":[101],"highlight":[102],"strength":[104],"approach,":[107],"particularly":[108],"blind":[110],"zero-shot":[112],"scenarios.":[113],"Notably,":[114],"model":[116],"achieved":[117],"multiple":[118],"first-rank":[119],"scores":[120],"taxonomy":[122],"discovery":[123],"non-taxonomic":[125],"extraction":[127],"subtasks,":[128],"validating":[129],"efficacy":[131],"when":[134],"coupled":[135],"semantically":[137],"specialized":[138],"LLMs.":[139],"work":[141],"demonstrates":[142],"that":[143],"clustering-driven,":[144],"LLM-based":[145],"approaches":[146],"can":[147],"advance":[148],"robust":[149],"scalable":[151],"learning":[153],"diverse":[155],"domains.":[156]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
