Exploring Sequence-to-Sequence Models for SPARQL Pattern Composition

Panchbhai, Anand; Soru, Tommaso; Marx, Edgard

doi:10.1007/978-3-030-65384-2_12

Cited by 6 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In scenarios where training data is limited, overfitting compromises network performance. To tackle this problem, instead of using a bidirectional long shortterm memory (LSTM) network to create the language representation model, Lukovnikov et al [53], Luo et al [54], and Panchbhai et al [55] independently evaluated the use of Bidirectional Encoder Representations from Transformers (BERT) [57], the current most performant solution for NL understanding tasks.…”

Section: Kbqa Based On Information Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Systematic review of question answering over knowledge bases

2021

View full text Add to dashboard Cite

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.

show abstract

Section: Kbqa Based On Information Extractionmentioning

confidence: 99%

“…[54], and Panchbhai et al. [55] independently evaluated the use of Bidirectional Encoder Representations from Transformers (BERT) [57], the current most performant solution for NL understanding tasks.…”

Section: Qa Over Knowledge Bases Solutionsmentioning

confidence: 99%

Systematic review of question answering over knowledge bases

2021

View full text Add to dashboard Cite

show abstract

“…Due to limited data availability, these LSTM-based approaches tend to overfit. Hence use of Bidirectional Encoder Representations from Transformers (BERT) is explored in Luo et al [124], Lukovnikov et al [147], Panchbhai et al [148]. One can also use five BERT models for five different tasks: question expected answer type (Q-EAT), answer type (AT) classification models, and Question Answering Model, as suggested by Day and Kuo [149].…”

mentioning

confidence: 99%

Question Answer System: A State-of-Art Representation of Quantitative and Qualitative Analysis

Zope

Mishra

Shaw

et al. 2022

BDCC

View full text Add to dashboard Cite

Question Answer System (QAS) automatically answers the question asked in natural language. Due to the varying dimensions and approaches that are available, QAS has a very diverse solution space, and a proper bibliometric study is required to paint the entire domain space. This work presents a bibliometric and literature analysis of QAS. Scopus and Web of Science are two well-known research databases used for the study. A systematic analytical study comprising performance analysis and science mapping is performed. Recent research trends, seminal work, and influential authors are identified in performance analysis using statistical tools on research constituents. On the other hand, science mapping is performed using network analysis on a citation and co-citation network graph. Through this analysis, the domain’s conceptual evolution and intellectual structure are shown. We have divided the literature into four important architecture types and have provided the literature analysis of Knowledge Base (KB)-based and GNN-based approaches for QAS.

show abstract