1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20229
Full metadata record
DC FieldValueLanguage
dc.contributor.authorEslahi, Yasamin-
dc.contributor.authorStockinger, Kurt-
dc.contributor.authorBhardwaj, Akansha-
dc.contributor.authorCudré-Mauroux, Philippe-
dc.contributor.authorRosso, Paolo-
dc.date.accessioned2020-07-03T20:29:52Z-
dc.date.available2020-07-03T20:29:52Z-
dc.date.issued2020-06-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20229-
dc.descriptionBest Paper Award ​© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.de_CH
dc.description.abstractThe Web has a collection of over 150 million tables, which as a whole represents an invaluable source of semi-structured knowledge. Such tables are commonly referred to as Web tables, and are considerably easier to leverage in automated processes than completely unstructured, free-format text. Understanding the semantics of Web tables is important since they are used in various applications like knowledge base augmentation, information retrieval or natural language interfaces for databases. The task of understanding the semantics of a given Web table is known as Web table annotation. In recent years, it has been tackled through methods where the table is enriched using existing knowledge bases containing valuable information on the domain at hand, its entities and their mutual relationships. In this paper, we present two novel and unsupervised Web table annotation methods, which leverage the context of the tables to better capture their semantics. Our first method is lookup-based and exploits text similarity to find reference entities in the knowledge base. The second method uses distributional vector representations – a.k.a. embeddings – of the Web tables to elicit their context and disambiguate their semantics. Experiments show that our proposed approach outperforms the state of the art in Web table annotation by up to 18%. Another contribution of this work is a manually corrected version of one of the popular gold standard datasets, Limaye, with annotations from DBpedia. Our dataset and code are publicly available.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectWeb table annotationde_CH
dc.subjectKnowledge basede_CH
dc.subjectEmbeddingsde_CH
dc.subjectData integrationde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleAnnotating web tables through knowledge bases : a context-based approach (Best Paper Award)de_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1109/SDS49233.2020.00013de_CH
dc.identifier.doi10.21256/zhaw-20229-
zhaw.conference.details7th Swiss Conference on Data Science, Lucerne, Switzerland, 26 June 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 7th SDSde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2020_Eslahi_Annotating_web_tables_throughknowledge_bases_SDS_2020.pdfAccepted Version322.46 kBAdobe PDFThumbnail
View/Open
Show simple item record
Eslahi, Y., Stockinger, K., Bhardwaj, A., Cudré-Mauroux, P., & Rosso, P. (2020, June). Annotating web tables through knowledge bases : a context-based approach (Best Paper Award). Proceedings of the 7th SDS. https://doi.org/10.1109/SDS49233.2020.00013
Eslahi, Y. et al. (2020) ‘Annotating web tables through knowledge bases : a context-based approach (Best Paper Award)’, in Proceedings of the 7th SDS. IEEE. Available at: https://doi.org/10.1109/SDS49233.2020.00013.
Y. Eslahi, K. Stockinger, A. Bhardwaj, P. Cudré-Mauroux, and P. Rosso, “Annotating web tables through knowledge bases : a context-based approach (Best Paper Award),” in Proceedings of the 7th SDS, Jun. 2020. doi: 10.1109/SDS49233.2020.00013.
ESLAHI, Yasamin, Kurt STOCKINGER, Akansha BHARDWAJ, Philippe CUDRÉ-MAUROUX und Paolo ROSSO, 2020. Annotating web tables through knowledge bases : a context-based approach (Best Paper Award). In: Proceedings of the 7th SDS. Conference paper. IEEE. Juni 2020
Eslahi, Yasamin, Kurt Stockinger, Akansha Bhardwaj, Philippe Cudré-Mauroux, and Paolo Rosso. 2020. “Annotating Web Tables through Knowledge Bases : A Context-Based Approach (Best Paper Award).” Conference paper. In Proceedings of the 7th SDS. IEEE. https://doi.org/10.1109/SDS49233.2020.00013.
Eslahi, Yasamin, et al. “Annotating Web Tables through Knowledge Bases : A Context-Based Approach (Best Paper Award).” Proceedings of the 7th SDS, IEEE, 2020, https://doi.org/10.1109/SDS49233.2020.00013.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.