Please use this identifier to cite or link to this item:
Publication type: Conference paper
Type of review: Peer review (publication)
Title: Bio-SODA : enabling natural language question answering over knowledge graphs without training data
Authors: 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
Proceedings: Proceedings of the 33rd SSDBM
Page(s): 61
Pages to: 72
Conference details: International Conference on Scientific and Statistical Database Management (SSDBM), Online, 6-7 July 2021
Issue Date: Jul-2021
Publisher / Ed. Institution: ACM
Other identifiers: arXiv:2104.13744v4
Language: English
Subjects: Database; Question answering; Graph database; Unsupervised machine learning; Natural language processing
Subject (DDC): 005: Computer programming, programs and data
006: Special computer methods
Abstract: 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.
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project: Bio-SODA – Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data (SNSF NRP 75 "Big Data")
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2021_Sima-etal_BioSODA_SSDBM.pdfAccepted Version544.45 kBAdobe PDFThumbnail

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