Towards a question answering system over the Semantic Web

Diefenbach, Dennis; Both, Andreas; Singh, Kamal Deep; Maret, Pierre

doi:10.3233/sw-190343

Cited by 91 publications

(138 citation statements)

References 43 publications

(40 reference statements)

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“…The task of complex question answering is defined in contrast to simple KGQA and requires matching more than one triple in the KG [45]. Previously proposed approaches to complex KGQA formulate it as a subgraph matching task [1,29,44], which is an NP-hard problem (by reduction to the subgraph isomorphism problem) [53], or attempt to translate a natural language question into template-based SPARQL queries to retrieve the answer from the KG [8], which requires a large number of candidate templates [42].…”

Section: Arxiv:190806917v1 [Cscl] 19 Aug 2019mentioning

confidence: 99%

“…In practice, Singh et al [42] report that the question building components of Frankenstein fail to process 46% questions from a subset of LC-QuAD due to the large number of triple patterns. The reason is that most approaches to query generation are templatebased [8] and complex questions require a large number of candidate templates [42]. For example, WDAqua [8] generates 395 SPARQL queries as possible interpretations for the question "Give me philosophers born in Saint Etienne.…”

Section: Related Workmentioning

confidence: 99%

“…The reason is that most approaches to query generation are templatebased [8] and complex questions require a large number of candidate templates [42]. For example, WDAqua [8] generates 395 SPARQL queries as possible interpretations for the question "Give me philosophers born in Saint Etienne. "…”

Section: Related Workmentioning

confidence: 99%

“…4 Baseline. We use WDAqua [8] as our baseline; to the best of our knowledge, the results produced by WDAqua are the only published results on the end-to-end question answering task for the LC-QuAD benchmark to date. It is a rule-based framework that integrates several KGs in different languages and relies on a handful of SPARQL query patterns to generate SPARQL queries and rank them as likely question interpretations.…”

Section: Evaluation Setupmentioning

confidence: 99%

“…It is a rule-based framework that integrates several KGs in different languages and relies on a handful of SPARQL query patterns to generate SPARQL queries and rank them as likely question interpretations. We rely on the evaluation results reported by the authors [8]. WDAqua results were produced for the full LC-QuAD dataset, while other datasets were used for tuning the approach.…”

Section: Evaluation Setupmentioning

confidence: 99%

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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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Section: Arxiv:190806917v1 [Cscl] 19 Aug 2019mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Evaluation Setupmentioning

confidence: 99%

Section: Evaluation Setupmentioning

confidence: 99%

See 3 more Smart Citations

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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Introduction to neural network‐based question answering over knowledge graphs

Chakraborty

Lukovnikov²,

Maheshwari

et al. 2021

WIREs Data Min & Knowl

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Question answering has emerged as an intuitive way of querying structured data sources and has attracted significant advancements over the years. A large body of recent work on question answering over knowledge graphs (KGQA) employs neural network‐based systems. In this article, we provide an overview of these neural network‐based methods for KGQA. We introduce readers to the formalism and the challenges of the task, different paradigms and approaches, discuss notable advancements, and outline the emerging trends in the field. Through this article, we aim to provide newcomers to the field with a suitable entry point to semantic parsing for KGQA, and ease their process of making informed decisions while creating their own QA systems. This article is categorized under: Technologies > Machine Learning Technologies > Prediction Technologies > Artificial Intelligence

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QaldGen: Towards Microbenchmarking of Question Answering Systems over Knowledge Graphs

Singh¹,

Saleem

Nadgeri³

et al. 2019

Lecture Notes in Computer Science

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Towards a question answering system over the Semantic Web

Cited by 91 publications

References 43 publications

Message Passing for Complex Question Answering over Knowledge Graphs

Message Passing for Complex Question Answering over Knowledge Graphs

Introduction to neural network‐based question answering over knowledge graphs

QaldGen: Towards Microbenchmarking of Question Answering Systems over Knowledge Graphs

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