Formal Query Generation for Question Answering over Knowledge Bases

Zafar, Hamid; Napolitano, Giulio; Lehmann, Jens

doi:10.1007/978-3-319-93417-4_46

Cited by 48 publications

(61 citation statements)

References 21 publications

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“…Both SINA and NLIWOD did not employ a query ranking mechanism, i.e., their accuracy and 4 https://github.com/dice-group/NLIWOD (Shekarpour et al, 2015) 0.24 † 0.39 † NLIWOD 4 0.48 † 0.49 † SQG (Zafar et al, 2018) 0.75 † -CompQA (Luo et al, 2018) 0.772 ±0.014 0.511 ±0.043 SubQG (our approach) 0.846 ±0.016 0.624 ±0.030 † indicates results taken from Singh et al (2018) and SQG. coverage are limited by the rules and templates.…”

Section: End-to-end Resultsmentioning

confidence: 99%

“…Datasets We employed the same datasets as Singh et al(2018) and Zafar et al(2018): (i) the large-scale complex question answering dataset (LC-QuAD) (Trivedi et al, 2017), containing 3,253 questions with non-empty results on DBpedia (2016-04), and (ii) the fifth edition of question answering over linked data (QALD-5) dataset (Unger et al, 2015), containing 311 questions with non-empty results on DBpedia (2015-10). Both datasets are widely used in KBQA studies (Zou et al, 2014;Dubey et al, 2018), and have become benchmarks for some annual KBQA competitions 23 .…”

Section: Methodsmentioning

confidence: 99%

“…Generally speaking, formal query generation is expected to include but not be limited to have the capabilities of (i) recognizing and paraphrasing different kinds of constraints, including triple-level constraints (e.g., "movies" corresponds to a typing constraint for the target variable) and higher level constraints (e.g., subgraphs). For instance, "the same ... as" * Corresponding authors represents a complex structure shown in the middle of Figure 1; (ii) recognizing and paraphrasing aggregations (e.g., "how many" corresponds to COUNT); and (iii) organizing all the above to generate an executable query (Singh et al, 2018;Zafar et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering

Ding¹,

Hu²,

Xu³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Formal query generation aims to generate correct executable queries for question answering over knowledge bases (KBs), given entity and relation linking results. Current approaches build universal paraphrasing or ranking models for the whole questions, which are likely to fail in generating queries for complex, long-tail questions. In this paper, we propose SubQG, a new query generation approach based on frequent query substructures, which helps rank the existing (but nonsignificant) query structures or build new query structures. Our experiments on two benchmark datasets show that our approach significantly outperforms the existing ones, especially for complex questions. Also, it achieves promising performance with limited training data and noisy entity/relation linking results.

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Section: End-to-end Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering

Ding¹,

Hu²,

Xu³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

View full text Add to dashboard Cite

show abstract

“…Query building approaches involve query generation and ranking steps [29,52]. These approaches essentially consider KGQA as a subgraph matching task [1,29,44], which is an NP-hard problem (by reduction to the subgraph isomorphism problem) [53].…”

Section: Related Workmentioning

confidence: 99%

Message Passing for Complex Question Answering over Knowledge Graphs

Vakulenko

García

Polleres

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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Question answering over knowledge graphs (KGQA) has evolved from simple single-fact questions to complex questions that require graph traversal and aggregation. We propose a novel approach for complex KGQA that uses unsupervised message passing, which propagates confidence scores obtained by parsing an input question and matching terms in the knowledge graph to a set of possible answers. First, we identify entity, relationship, and class names mentioned in a natural language question, and map these to their counterparts in the graph. Then, the confidence scores of these mappings propagate through the graph structure to locate the answer entities. Finally, these are aggregated depending on the identified question type.This approach can be efficiently implemented as a series of sparse matrix multiplications mimicking joins over small local subgraphs. Our evaluation results show that the proposed approach outperforms the state-of-the-art on the LC-QuAD benchmark. Moreover, we show that the performance of the approach depends only on the quality of the question interpretation results, i.e., given a correct relevance score distribution, our approach always produces a correct answer ranking. Our error analysis reveals correct answers missing from the benchmark dataset and inconsistencies in the DBpedia knowledge graph. Finally, we provide a comprehensive evaluation of the proposed approach accompanied with an ablation study and an error analysis, which showcase the pitfalls for each of the question answering components in more detail.

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“…Finally, the QG-component needs to generate a SPARQL query by taking into account the structure of the knowledge graphs. However, most implementations of QA systems over knowledge graphs are not subdivided into such independent components [9]. Recently, truly modular QA systems have been introduced so that components can be reused and exchanged.…”

Section: Introductionmentioning

confidence: 99%

Querying Knowledge Graphs in Natural Language

Liang¹,

Stockinger

Farias

et al. 2020

Preprint

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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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Formal Query Generation for Question Answering over Knowledge Bases

Cited by 48 publications

References 21 publications

Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering

Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering

Message Passing for Complex Question Answering over Knowledge Graphs

Querying Knowledge Graphs in Natural Language

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