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Publikationstyp: Konferenz: Paper
Art der Begutachtung: Peer review (Publikation)
Titel: Bio-SODA : enabling natural language question answering over knowledge graphs without training data
Autor/-in: Sima, Ana Claudia
Mendes de Farias, Tarcisio
Anisimova, Maria
Dessimoz, Christophe
Robinson-Rechavi, Marc
Zbinden, Erich
Stockinger, Kurt
et. al: No
DOI: 10.1145/3468791.3469119
10.21256/zhaw-22738
Tagungsband: Proceedings of the 33rd SSDBM
Seite(n): 61
Seiten bis: 72
Angaben zur Konferenz: International Conference on Scientific and Statistical Database Management (SSDBM), Online, 6-7 July 2021
Erscheinungsdatum: Jul-2021
Verlag / Hrsg. Institution: ACM
Andere Identifier: arXiv:2104.13744v4
Sprache: Englisch
Schlagwörter: Database; Question answering; Graph database; Unsupervised machine learning; Natural language processing
Fachgebiet (DDC): 005: Computerprogrammierung, Programme und Daten
006: Spezielle Computerverfahren
Zusammenfassung: The 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/22738
Volltext Version: Akzeptierte Version
Lizenz (gemäss Verlagsvertrag): Lizenz gemäss Verlagsvertrag
Departement: Life Sciences und Facility Management
School of Engineering
Organisationseinheit: Institut für Angewandte Informationstechnologie (InIT)
Institut für Computational Life Sciences (ICLS)
Publiziert im Rahmen des ZHAW-Projekts: Bio-SODA – Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data (SNSF NRP 75 "Big Data")
Enthalten in den Sammlungen:Publikationen School of Engineering

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