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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22738
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dc.contributor.authorSima, Ana Claudia-
dc.contributor.authorMendes de Farias, Tarcisio-
dc.contributor.authorAnisimova, Maria-
dc.contributor.authorDessimoz, Christophe-
dc.contributor.authorRobinson-Rechavi, Marc-
dc.contributor.authorZbinden, Erich-
dc.contributor.authorStockinger, Kurt-
dc.date.accessioned2021-07-01T09:38:03Z-
dc.date.available2021-07-01T09:38:03Z-
dc.date.issued2021-07-
dc.identifier.otherarXiv:2104.13744v4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22738-
dc.description.abstractThe problem of natural language processing over structured data has become a growing research field, both within the relational database and the Semantic Web community, with significant efforts involved in question answering over knowledge graphs (KGQA). However, many of these approaches are either specifically targeted at open-domain question answering using DBpedia, or require large training datasets to translate a natural language question to SPARQL in order to query the knowledge graph. Hence, these approaches often cannot be applied directly to complex scientific datasets where no prior training data is available. In this paper, we focus on the challenges of natural language processing over knowledge graphs of scientific datasets. In particular, we introduce Bio-SODA, a natural language processing engine that does not require training data in the form of question-answer pairs for generating SPARQL queries. Bio-SODA uses a generic graph-based approach for translating user questions to a ranked list of SPARQL candidate queries. Furthermore, Bio-SODA uses a novel ranking algorithm that includes node centrality as a measure of relevance for selecting the best SPARQL candidate query. Our experiments with real-world datasets across several scientific domains, including the official bioinformatics Question Answering over Linked Data (QALD) challenge, show that Bio-SODA outperforms publicly available KGQA systems by an F1-score of least 20% and by an even higher factor on more complex bioinformatics datasets.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computing Machineryde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectDatabasede_CH
dc.subjectQuestion answeringde_CH
dc.subjectGraph databasede_CH
dc.subjectUnsupervised machine learningde_CH
dc.subjectNatural language processingde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleBio-SODA : enabling natural language question answering over knowledge graphs without training datade_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.1145/3468791.3469119de_CH
dc.identifier.doi10.21256/zhaw-22738-
zhaw.conference.detailsInternational Conference on Scientific and Statistical Database Management (SSDBM), Online, 6-7 July 2021de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end72de_CH
zhaw.pages.start61de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 33rd SSDBMde_CH
zhaw.funding.snf167149de_CH
zhaw.webfeedComputational Genomicsde_CH
zhaw.webfeedData Management & Visualisationde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.funding.zhawBio-SODA – Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data (SNSF NRP 75 "Big Data")de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Sima, A. C., Mendes de Farias, T., Anisimova, M., Dessimoz, C., Robinson-Rechavi, M., Zbinden, E., & Stockinger, K. (2021). Bio-SODA : enabling natural language question answering over knowledge graphs without training data [Conference paper]. Proceedings of the 33rd SSDBM, 61–72. https://doi.org/10.1145/3468791.3469119
Sima, A.C. et al. (2021) ‘Bio-SODA : enabling natural language question answering over knowledge graphs without training data’, in Proceedings of the 33rd SSDBM. Association for Computing Machinery, pp. 61–72. Available at: https://doi.org/10.1145/3468791.3469119.
A. C. Sima et al., “Bio-SODA : enabling natural language question answering over knowledge graphs without training data,” in Proceedings of the 33rd SSDBM, Jul. 2021, pp. 61–72. doi: 10.1145/3468791.3469119.
SIMA, Ana Claudia, Tarcisio MENDES DE FARIAS, Maria ANISIMOVA, Christophe DESSIMOZ, Marc ROBINSON-RECHAVI, Erich ZBINDEN und Kurt STOCKINGER, 2021. Bio-SODA : enabling natural language question answering over knowledge graphs without training data. In: Proceedings of the 33rd SSDBM. Conference paper. Association for Computing Machinery. Juli 2021. S. 61–72
Sima, Ana Claudia, Tarcisio Mendes de Farias, Maria Anisimova, Christophe Dessimoz, Marc Robinson-Rechavi, Erich Zbinden, and Kurt Stockinger. 2021. “Bio-SODA : Enabling Natural Language Question Answering over Knowledge Graphs without Training Data.” Conference paper. In Proceedings of the 33rd SSDBM, 61–72. Association for Computing Machinery. https://doi.org/10.1145/3468791.3469119.
Sima, Ana Claudia, et al. “Bio-SODA : Enabling Natural Language Question Answering over Knowledge Graphs without Training Data.” Proceedings of the 33rd SSDBM, Association for Computing Machinery, 2021, pp. 61–72, https://doi.org/10.1145/3468791.3469119.


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