Complex Query Augmentation for Question Answering over Knowledge Graphs

Abdelkawi, Abdelrahman; Zafar, Hamid; Maleshkova, Maria; Lehmann, Jens

doi:10.1007/978-3-030-33246-4_36

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…What is more, we use an improved version of the query construction component [9] other than selecting between the currently published QA components. ExSQG extends the former SQG to support more query types [24]. For instance, ExSQG supports ordinal questions such as superlatives, however it still does not consider a constraint that can be expressed within a filter clause.…”

Section: Related Workmentioning

confidence: 99%

Querying knowledge graphs in natural language

Liang¹,

Stockinger

Farias

et al. 2021

J Big Data

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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.

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Section: Related Workmentioning

confidence: 99%

Querying knowledge graphs in natural language

Liang¹,

Stockinger

Farias

et al. 2021

J Big Data

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“…To extend QG to ordinal and filter questions, Abdelkawi et al. [51] add extra constraints to the list of all possible answers.…”

Section: Qa Over Knowledge Bases Solutionsmentioning

confidence: 99%

“…Finally, candidate walks are evaluated against the type of question, and a SPARQL query is created. To extend QG to ordinal and filter questions, Abdelkawi et al [51] add extra constraints to the list of all possible answers.…”

Section: Semantic Parsing Pipelinesmentioning

confidence: 99%

Systematic review of question answering over knowledge bases

2021

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Over the years, a growing number of semantic data repositories have been made available on the web. However, this has created new challenges in exploiting these resources efficiently. Querying services require knowledge beyond the typical user's expertise, which is a critical issue in adopting semantic information solutions. Several proposals to overcome this difficulty have suggested using question answering (QA) systems to provide user-friendly interfaces and allow natural language use. Because question answering over knowledge bases (KBQAs) is a very active research topic, a comprehensive view of the field is essential. The purpose of this study was to conduct a systematic review of methods and systems for KBQAs to identify their main advantages and limitations. The inclusion criteria rationale was English full-text articles published since 2015 on methods and systems for KBQAs. Sixty-six articles were reviewed to describe their underlying reference architectures.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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