Querying knowledge graphs in natural language.

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State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_673D6C6C570E
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Querying knowledge graphs in natural language.
Journal
Journal of big data
Author(s)
Liang S., Stockinger K., de Farias T.M., Anisimova M., Gil M.
ISSN
2196-1115 (Print)
ISSN-L
2196-1115
Publication state
Published
Issued date
2021
Peer-reviewed
Oui
Volume
8
Number
3
Pages
1-23
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
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.
Keywords
Knowledge graphs, Natural language processing, Query processing, SPARQL
Pubmed
Web of science
Open Access
Yes
Create date
19/02/2021 11:48
Last modification date
30/03/2021 6:34
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