{"id":"https://openalex.org/W3205027972","doi":"https://doi.org/10.1109/icra48506.2021.9561952","title":"Embedding Symbolic Temporal Knowledge into Deep Sequential Models","display_name":"Embedding Symbolic Temporal Knowledge into Deep Sequential Models","publication_year":2021,"publication_date":"2021-05-30","ids":{"openalex":"https://openalex.org/W3205027972","doi":"https://doi.org/10.1109/icra48506.2021.9561952","mag":"3205027972"},"language":"en","primary_location":{"id":"doi:10.1109/icra48506.2021.9561952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48506.2021.9561952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","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/A5032012609","display_name":"Yaqi Xie","orcid":"https://orcid.org/0009-0001-5703-2500"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Yaqi Xie","raw_affiliation_strings":["Dept. of Computer Science, National University of Singapore"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077610457","display_name":"Fan Zhou","orcid":"https://orcid.org/0000-0003-1736-2641"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Fan Zhou","raw_affiliation_strings":["Dept. of Computer Science, National University of Singapore"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066073375","display_name":"Harold Soh","orcid":"https://orcid.org/0000-0002-3278-0035"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Harold Soh","raw_affiliation_strings":["Dept. of Computer Science, National University of Singapore"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5032012609"],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":1.3597,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.84718361,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"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":0.9958000183105469,"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":0.9958000183105469,"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":0.994700014591217,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7835574150085449},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7485070824623108},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.6972519159317017},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6468141078948975},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6438568830490112},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5033630728721619},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4883377254009247},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4844384491443634},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34063661098480225}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7835574150085449},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7485070824623108},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.6972519159317017},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6468141078948975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6438568830490112},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5033630728721619},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4883377254009247},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4844384491443634},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34063661098480225}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra48506.2021.9561952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48506.2021.9561952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","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":72,"referenced_works":["https://openalex.org/W58948312","https://openalex.org/W125598877","https://openalex.org/W1522301498","https://openalex.org/W1848921964","https://openalex.org/W1879966306","https://openalex.org/W1938755728","https://openalex.org/W1965024948","https://openalex.org/W1978892650","https://openalex.org/W1980088782","https://openalex.org/W2006754637","https://openalex.org/W2023808162","https://openalex.org/W2064675550","https://openalex.org/W2071797744","https://openalex.org/W2099217059","https://openalex.org/W2124623089","https://openalex.org/W2141088850","https://openalex.org/W2158592639","https://openalex.org/W2163941698","https://openalex.org/W2194775991","https://openalex.org/W2250539671","https://openalex.org/W2555308822","https://openalex.org/W2567705466","https://openalex.org/W2604825101","https://openalex.org/W2605514557","https://openalex.org/W2612872092","https://openalex.org/W2741248519","https://openalex.org/W2752344401","https://openalex.org/W2766447205","https://openalex.org/W2784931892","https://openalex.org/W2792764867","https://openalex.org/W2808386811","https://openalex.org/W2810178509","https://openalex.org/W2905224888","https://openalex.org/W2907492528","https://openalex.org/W2919115771","https://openalex.org/W2951559648","https://openalex.org/W2953232951","https://openalex.org/W2960930698","https://openalex.org/W2963149920","https://openalex.org/W2963277051","https://openalex.org/W2963355447","https://openalex.org/W2963403868","https://openalex.org/W2963737801","https://openalex.org/W2963938535","https://openalex.org/W2964015378","https://openalex.org/W2964121744","https://openalex.org/W2964654516","https://openalex.org/W2969622979","https://openalex.org/W2976115197","https://openalex.org/W2981635073","https://openalex.org/W2991085750","https://openalex.org/W2995238154","https://openalex.org/W2997319416","https://openalex.org/W3039865636","https://openalex.org/W3101073843","https://openalex.org/W4210257598","https://openalex.org/W4288118250","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6639220752","https://openalex.org/W6639301165","https://openalex.org/W6718092244","https://openalex.org/W6726873649","https://openalex.org/W6730164271","https://openalex.org/W6735993290","https://openalex.org/W6739901393","https://openalex.org/W6747063171","https://openalex.org/W6749825310","https://openalex.org/W6757374366","https://openalex.org/W6762837429","https://openalex.org/W6768627553","https://openalex.org/W6769311773"],"related_works":["https://openalex.org/W2604454537","https://openalex.org/W4206028705","https://openalex.org/W2808284704","https://openalex.org/W2883748392","https://openalex.org/W2897702399","https://openalex.org/W2757431232","https://openalex.org/W2954554213","https://openalex.org/W3200431764","https://openalex.org/W4377865163","https://openalex.org/W4288286922"],"abstract_inverted_index":{"Sequences":[0],"and":[1,12,36,74,112,158],"time-series":[2],"often":[3],"arise":[4],"in":[5,9,60],"robot":[6,151],"tasks,":[7],"e.g.,":[8],"activity":[10],"recognition":[11,157],"imitation":[13,159],"learning.":[14,160],"In":[15,83],"recent":[16],"years,":[17],"deep":[18,121],"neural":[19],"networks":[20],"(DNNs)":[21],"have":[22],"emerged":[23],"as":[24,47,107,154],"an":[25],"effective":[26],"data-driven":[27],"methodology":[28],"for":[29],"processing":[30],"sequences":[31],"given":[32],"sufficient":[33],"training":[34,119],"data":[35,41],"compute":[37],"resources.":[38],"However,":[39,63],"when":[40,54,97],"is":[42,58,101],"limited,":[43],"simpler":[44],"models":[45,68],"such":[46,153],"logic/rule-based":[48],"methods":[49],"work":[50,77],"surprisingly":[51],"well,":[52],"especially":[53],"relevant":[55],"prior":[56,94],"knowledge":[57,96,105],"applied":[59],"their":[61],"construction.":[62],"unlike":[64],"DNNs,":[65,91],"these":[66,114,142],"\"structured\"":[67],"can":[69,145],"be":[70],"difficult":[71],"to":[72,88,102,116,147],"extend,":[73],"do":[75],"not":[76],"well":[78],"with":[79],"raw":[80],"unstructured":[81],"data.":[82],"this":[84],"work,":[85],"we":[86,124],"seek":[87],"learn":[89],"flexible":[90],"yet":[92],"leverage":[93],"temporal":[95,109],"available.":[98],"Our":[99],"approach":[100],"embed":[103],"symbolic":[104],"expressed":[106],"linear":[108],"logic":[110],"(LTL)":[111],"use":[113],"embeddings":[115,127,144],"guide":[117],"the":[118],"of":[120,128],"models.":[122],"Specifically,":[123],"construct":[125],"semantic-based":[126],"automata":[129],"generated":[130],"from":[131],"LTL":[132],"formula":[133],"via":[134],"a":[135],"Graph":[136],"Neural":[137],"Network.":[138],"Experiments":[139],"show":[140],"that":[141],"learnt":[143],"lead":[146],"improvements":[148],"on":[149],"downstream":[150],"tasks":[152],"sequential":[155],"action":[156]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}