{"id":"https://openalex.org/W4320507227","doi":"https://doi.org/10.1145/3579051.3579062","title":"Extracting ISA Relations of Concepts from Books via Weakly Supervised Learning","display_name":"Extracting ISA Relations of Concepts from Books via Weakly Supervised Learning","publication_year":2022,"publication_date":"2022-10-27","ids":{"openalex":"https://openalex.org/W4320507227","doi":"https://doi.org/10.1145/3579051.3579062"},"language":"en","primary_location":{"id":"doi:10.1145/3579051.3579062","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3579051.3579062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th International Joint Conference on Knowledge Graphs","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/A5041902575","display_name":"Haonian Wang","orcid":"https://orcid.org/0000-0003-3748-7142"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haonian Wang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, China"],"raw_orcid":"https://orcid.org/0000-0003-3748-7142","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020356333","display_name":"Xinyu Tang","orcid":"https://orcid.org/0000-0002-1882-5284"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Tang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, China"],"raw_orcid":"https://orcid.org/0000-0002-1882-5284","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079767418","display_name":"Yurou Liu","orcid":"https://orcid.org/0000-0001-8275-2254"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yurou Liu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, China"],"raw_orcid":"https://orcid.org/0000-0001-8275-2254","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080268112","display_name":"Zhichun Wang","orcid":"https://orcid.org/0000-0001-8797-7065"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhichun Wang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, China"],"raw_orcid":"https://orcid.org/0000-0001-8797-7065","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041902575"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17306146,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"91","last_page":"98"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9994999766349792,"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/T10028","display_name":"Topic Modeling","score":0.9994999766349792,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9976000189781189,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9912999868392944,"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/relationship-extraction","display_name":"Relationship extraction","score":0.865769624710083},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.770925760269165},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7544243335723877},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6718964576721191},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.640686571598053},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6145381331443787},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6124196648597717},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.4337858557701111},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.4251779317855835},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3923729956150055},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.3273143768310547},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.20429539680480957},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0757477879524231}],"concepts":[{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.865769624710083},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.770925760269165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7544243335723877},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6718964576721191},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.640686571598053},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6145381331443787},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6124196648597717},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4337858557701111},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.4251779317855835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3923729956150055},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.3273143768310547},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.20429539680480957},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0757477879524231},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3579051.3579062","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3579051.3579062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th International Joint Conference on Knowledge Graphs","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4000000059604645,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2896457183","https://openalex.org/W2919115771","https://openalex.org/W2964022985","https://openalex.org/W2998386822","https://openalex.org/W3121309507","https://openalex.org/W3167136668","https://openalex.org/W3209316667","https://openalex.org/W6600109629","https://openalex.org/W6600903635","https://openalex.org/W6601055912","https://openalex.org/W6601546630","https://openalex.org/W6788864986"],"related_works":["https://openalex.org/W2805262146","https://openalex.org/W4379517534","https://openalex.org/W842810586","https://openalex.org/W4319940250","https://openalex.org/W2352298027","https://openalex.org/W2092919065","https://openalex.org/W3138801416","https://openalex.org/W4236762297","https://openalex.org/W2444550338","https://openalex.org/W2369351710"],"abstract_inverted_index":{"Relation":[0],"extraction":[1,19,62,80],"is":[2,33,44,54],"an":[3],"important":[4],"task":[5],"in":[6,68,95,109],"natural":[7],"language":[8],"processing.":[9],"In":[10,70],"recent":[11],"years,":[12],"researchers":[13],"have":[14],"gradually":[15],"discovered":[16],"that":[17,36,123],"relation":[18,35,61,79],"can":[20],"be":[21],"used":[22],"for":[23,38,48,66],"teaching":[24],"and":[25,43,86,102],"learning":[26,97],"assessment.":[27],"Among":[28],"various":[29],"dependencies,":[30],"the":[31,59,91,100,107,110,115,118,124,131],"ISA":[32,60],"a":[34,39,75],"accounts":[37],"relatively":[40],"large":[41],"proportion":[42],"of":[45,117],"great":[46],"significance":[47],"many":[49],"related":[50],"tasks.":[51],"However,":[52],"there":[53],"currently":[55],"little":[56],"research":[57],"on":[58,83],"between":[63],"cross-chapter":[64],"concepts":[65],"few-shot":[67,78],"textbooks.":[69],"this":[71],"paper,":[72],"we":[73,126],"propose":[74],"distant":[76],"supervised":[77],"model":[81,125],"based":[82],"example":[84,92],"construction":[85,93],"self-attention":[87,103],"optimization.":[88],"We":[89],"use":[90],"method":[94],"contrastive":[96],"to":[98,105,113],"expand":[99],"samples":[101],"mechanism":[104],"weight":[106],"sentences":[108],"sentence":[111],"bag":[112],"extract":[114],"relations":[116],"concepts.":[119],"Experimental":[120],"results":[121],"show":[122],"constructed":[127],"performs":[128],"better":[129],"than":[130],"previous":[132],"methods.":[133]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}