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https://doi.org/10.21256/zhaw-21263| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Querying knowledge graphs in natural language |
| Authors: | Liang, Shiqi Stockinger, Kurt de Farias, Tarcisio Mendes Anisimova, Maria Gil, Manuel |
| et. al: | No |
| DOI: | 10.1186/s40537-020-00383-w 10.21256/zhaw-21263 |
| Published in: | Journal of Big Data |
| Volume(Issue): | 8 |
| Issue: | 3 |
| Issue Date: | 6-Jan-2021 |
| Publisher / Ed. Institution: | Springer |
| ISSN: | 2196-1115 |
| Language: | English |
| Subjects: | Natural language processing; Knowledge graphs; Query processing; SPARQL |
| Subject (DDC): | 006: Special computer methods 410.285: Computational linguistics |
| Abstract: | Knowledge graphs are a powerful concept for querying large amounts of data. These knowledge graphs are typically enormous and are often not easily accessible to end-users because they require specialized knowledge in query languages such as SPARQL. Moreover, end-users need a deep understanding of the structure of the underlying data models often based on the Resource Description Framework (RDF). This drawback has led to the development of Question-Answering (QA) systems that enable end-users to express their information needs in natural language. While existing systems simplify user access, there is still room for improvement in the accuracy of these systems. In this paper we propose a new QA system for translating natural language questions into SPARQL queries. The key idea is to break up the translation process into 5 smaller, more manageable sub-tasks and use ensemble machine learning methods as well as Tree-LSTM-based neural network models to automatically learn and translate a natural language question into a SPARQL query. The performance of our proposed QA system is empirically evaluated using the two renowned benchmarks-the 7th Question Answering over Linked Data Challenge (QALD-7) and the Large-Scale Complex Question Answering Dataset (LC-QuAD). Experimental results show that our QA system outperforms the state-of-art systems by 15% on the QALD-7 dataset and by 48% on the LC-QuAD dataset, respectively. In addition, we make our source code available. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/21263 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | Life Sciences and Facility Management School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) Institute of Computational Life Sciences (ICLS) |
| Published as part of the ZHAW project: | SNF NRP 75 "Big Data": Bio-SODA - Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data |
| Appears in collections: | Publikationen School of Engineering |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 2021_Liang-Stockinger_etal_Querying-knowledge-graphs_Big-Data.pdf | 2.05 MB | Adobe PDF |  View/Open |
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Liang, S., Stockinger, K., de Farias, T. M., Anisimova, M., & Gil, M. (2021). Querying knowledge graphs in natural language. Journal of Big Data, 8(3). https://doi.org/10.1186/s40537-020-00383-w
Liang, S. et al. (2021) ‘Querying knowledge graphs in natural language’, Journal of Big Data, 8(3). Available at: https://doi.org/10.1186/s40537-020-00383-w.
S. Liang, K. Stockinger, T. M. de Farias, M. Anisimova, and M. Gil, “Querying knowledge graphs in natural language,” Journal of Big Data, vol. 8, no. 3, Jan. 2021, doi: 10.1186/s40537-020-00383-w.
LIANG, Shiqi, Kurt STOCKINGER, Tarcisio Mendes DE FARIAS, Maria ANISIMOVA und Manuel GIL, 2021. Querying knowledge graphs in natural language. Journal of Big Data. 6 Januar 2021. Bd. 8, Nr. 3. DOI 10.1186/s40537-020-00383-w
Liang, Shiqi, Kurt Stockinger, Tarcisio Mendes de Farias, Maria Anisimova, and Manuel Gil. 2021. “Querying Knowledge Graphs in Natural Language.” Journal of Big Data 8 (3). https://doi.org/10.1186/s40537-020-00383-w.
Liang, Shiqi, et al. “Querying Knowledge Graphs in Natural Language.” Journal of Big Data, vol. 8, no. 3, Jan. 2021, https://doi.org/10.1186/s40537-020-00383-w.
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