{"id":"https://openalex.org/W2598891312","doi":"https://doi.org/10.1109/taai.2016.7880164","title":"AFIS: Aligning detail-pages for full schema induction","display_name":"AFIS: Aligning detail-pages for full schema induction","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2598891312","doi":"https://doi.org/10.1109/taai.2016.7880164","mag":"2598891312"},"language":"en","primary_location":{"id":"doi:10.1109/taai.2016.7880164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taai.2016.7880164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","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/A5053181885","display_name":"Oviliani Yenty Yuliana","orcid":"https://orcid.org/0000-0002-2446-0402"},"institutions":[{"id":"https://openalex.org/I22265921","display_name":"National Central University","ror":"https://ror.org/00944ve71","country_code":"TW","type":"education","lineage":["https://openalex.org/I22265921"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Oviliani Yenty Yuliana","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I22265921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078524542","display_name":"Chia\u2010Hui Chang","orcid":"https://orcid.org/0000-0002-1101-6337"},"institutions":[{"id":"https://openalex.org/I22265921","display_name":"National Central University","ror":"https://ror.org/00944ve71","country_code":"TW","type":"education","lineage":["https://openalex.org/I22265921"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chia-Hui Chang","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I22265921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5053181885"],"corresponding_institution_ids":["https://openalex.org/I22265921"],"apc_list":null,"apc_paid":null,"fwci":0.6635,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.82215009,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"18","issue":null,"first_page":"220","last_page":"227"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12016","display_name":"Web Data Mining and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11478","display_name":"Caching and Content Delivery","score":0.9733999967575073,"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"}},{"id":"https://openalex.org/T13976","display_name":"Web visibility and informetrics","score":0.9516000151634216,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/computer-science","display_name":"Computer science","score":0.7856012582778931},{"id":"https://openalex.org/keywords/schema","display_name":"Schema (genetic algorithms)","score":0.6153799891471863},{"id":"https://openalex.org/keywords/web-page","display_name":"Web page","score":0.5652668476104736},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5109381079673767},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4724987745285034},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44949105381965637},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.44625669717788696},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.2671896517276764},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26261812448501587}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7856012582778931},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.6153799891471863},{"id":"https://openalex.org/C21959979","wikidata":"https://www.wikidata.org/wiki/Q36774","display_name":"Web page","level":2,"score":0.5652668476104736},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5109381079673767},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4724987745285034},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44949105381965637},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.44625669717788696},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.2671896517276764},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26261812448501587}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taai.2016.7880164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taai.2016.7880164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W107766491","https://openalex.org/W1553019137","https://openalex.org/W1968053850","https://openalex.org/W1981220436","https://openalex.org/W1986318292","https://openalex.org/W1999361961","https://openalex.org/W2019577381","https://openalex.org/W2024791376","https://openalex.org/W2040757233","https://openalex.org/W2049488566","https://openalex.org/W2119577990","https://openalex.org/W2134150392","https://openalex.org/W2139599797","https://openalex.org/W2150721933","https://openalex.org/W2154444297","https://openalex.org/W2158051716","https://openalex.org/W2161861392","https://openalex.org/W2201534957","https://openalex.org/W2913389685","https://openalex.org/W6633154970","https://openalex.org/W6647013195","https://openalex.org/W6680060327","https://openalex.org/W6680582021"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W2377979023","https://openalex.org/W2218034408","https://openalex.org/W2392921965","https://openalex.org/W2358755282","https://openalex.org/W2625833328","https://openalex.org/W1533177136","https://openalex.org/W2251519152","https://openalex.org/W4380994516"],"abstract_inverted_index":{"Web":[0],"data":[1,9,15,42,56],"extraction":[2,16,57],"is":[3,48],"an":[4],"essential":[5],"task":[6],"for":[7,75],"web":[8],"integration.":[10],"Most":[11],"researches":[12],"focus":[13],"on":[14],"from":[17,64,93],"list-pages":[18],"by":[19],"detecting":[20],"data-rich":[21],"section":[22],"and":[23,84,103,130],"record":[24],"boundary":[25],"segmentation.":[26],"However,":[27],"in":[28,35,78,107],"detail-pages":[29],"which":[30],"contain":[31],"all-inclusive":[32],"product":[33],"information":[34],"each":[36],"page,":[37],"so":[38],"the":[39,121],"number":[40],"of":[41,61,72,109],"attributes":[43],"need":[44],"to":[45,90],"be":[46],"aligned":[47],"much":[49],"larger.":[50],"In":[51],"this":[52,79],"paper,":[53],"we":[54],"formulate":[55],"problem":[58],"as":[59],"alignment":[60],"leaf":[62],"nodes":[63],"DOM":[65],"Trees.":[66],"We":[67],"propose":[68],"AFIS,":[69],"Annotation-Free":[70],"Induction":[71],"Full":[73],"Schema":[74],"detail":[76],"pages":[77],"paper.":[80],"AFIS":[81,99,118],"applies":[82],"Divide-and-Conquer":[83],"Longest":[85],"Increasing":[86],"Sequence":[87],"(LIS)":[88],"algorithms":[89],"mine":[91],"landmarks":[92],"input.":[94],"The":[95],"experiments":[96],"show":[97],"that":[98],"outperforms":[100],"RoadRunner,":[101],"FivaTech":[102],"TEX":[104,129],"(F1":[105,125],"0.990)":[106],"terms":[108],"selected":[110],"data.":[111],"For":[112],"full":[113],"schema":[114],"evaluation":[115],"(all":[116],"data),":[117],"also":[119],"represents":[120],"highest":[122],"average":[123],"performance":[124],"0.937)":[126],"compared":[127],"with":[128],"RoadRunner.":[131]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
