Knowledge Graph Question Answering using Graph-Pattern Isomorphism

Vollmers, Daniel; Jalota, Rricha; Moussallem, Diego; Topiwala, Hardik; Ngomo, Axel-Cyrille Ngonga; Usbeck, Ricardo

doi:10.48550/arxiv.2103.06752

Cited by 3 publications

(4 citation statements)

References 25 publications

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“…Early work usually exploits a slotfilling method by using a set of pre-defined SPARQL templates with some slots that have to be filled. These models often design and summarize some common simple SPARQL templates for a specific dataset, such as TeBaQA [32], Template-based QA over RNN [33], and SubQG [34]. However, the templates used by these methods are not guaranteed to be adapted to other datasets [11].…”

Section: Semantic Parsing-based Methodsmentioning

confidence: 99%

Enhancing SPARQL Query Generation for Knowledge Base Question Answering Systems by Learning to Correct Triplets

Qi,

Su,

Guo

et al. 2024

Applied Sciences

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Generating SPARQL queries from natural language questions is challenging in Knowledge Base Question Answering (KBQA) systems. The current state-of-the-art models heavily rely on fine-tuning pretrained models such as T5. However, these methods still encounter critical issues such as triple-flip errors (e.g., (subject, relation, object) is predicted as (object, relation, subject)). To address this limitation, we introduce TSET (Triplet Structure Enhanced T5), a model with a novel pretraining stage positioned between the initial T5 pretraining and the fine-tuning for the Text-to-SPARQL task. In this intermediary stage, we introduce a new objective called Triplet Structure Correction (TSC) to train the model on a SPARQL corpus derived from Wikidata. This objective aims to deepen the model’s understanding of the order of triplets. After this specialized pretraining, the model undergoes fine-tuning for SPARQL query generation, augmenting its query-generation capabilities. We also propose a method named “semantic transformation” to fortify the model’s grasp of SPARQL syntax and semantics without compromising the pre-trained weights of T5. Experimental results demonstrate that our proposed TSET outperforms existing methods on three well-established KBQA datasets: LC-QuAD 2.0, QALD-9 plus, and QALD-10, establishing a new state-of-the-art performance (95.0% F1 and 93.1% QM on LC-QuAD 2.0, 75.85% F1 and 61.76% QM on QALD-9 plus, 51.37% F1 and 40.05% QM on QALD-10).

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Section: Semantic Parsing-based Methodsmentioning

confidence: 99%

Enhancing SPARQL Query Generation for Knowledge Base Question Answering Systems by Learning to Correct Triplets

Qi,

Su,

Guo

et al. 2024

Applied Sciences

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“…In a different work, Unger et al [29] introduced a method to generate SPARQL query templates, utilizing the semantic structure of the question. More recently, a classification based approach was proposed by Vollmers et al [10], where semantically similar types of questions are classified to obtain a query template. Nevertheless, the query generation task remains limited due to the fixed number of schema.…”

Section: Related Workmentioning

confidence: 99%

“…Several approaches for SPARQL query generation have been presented recently [6]- [10]. The widely adopted ap-FIGURE 1.…”

Section: Introductionmentioning

confidence: 99%

SGPT: A Generative Approach for SPARQL Query Generation From Natural Language Questions

et al. 2022

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SPARQL query generation from natural language questions is complex because it requires an understanding of both the question and the underlying knowledge graph (KG) patterns. Most SPARQL query generation approaches are template-based, tailored to a specific knowledge graph and require pipelines with multiple steps, including entity and relation linking. Template-based approaches are also difficult to adapt for new KGs and require manual efforts from domain experts to construct query templates. To overcome this hurdle, we propose a new approach, dubbed SGPT, that combines the benefits of end-to-end and modular systems and leverages recent advances in large-scale language models. Specifically, we devise a novel embedding technique that can encode linguistic features from the question which enables the system to learn complex question patterns. In addition, we propose training techniques that allow the system to implicitly employ the graph-specific information (i.e., entities and relations) into the language model's parameters and generate SPARQL queries accurately. Finally, we introduce a strategy to adapt standard automatic metrics for evaluating SPARQL query generation. A comprehensive evaluation demonstrates the effectiveness of SGPT over state-of-the-art methods across several benchmark datasets.INDEX TERMS Knowledge based systems, knowledge graph, information retrieval, query generation, language models.

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“…Vollmers [135] proposes TeBaQA, an isomorphic graph-based approach. Basic graph patterns of some SPARQL queries are maintained in the repository.…”

mentioning

confidence: 99%

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

Zope

Mishra

Shaw

et al. 2022

BDCC

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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.

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Knowledge Graph Question Answering using Graph-Pattern Isomorphism

Cited by 3 publications

References 25 publications

Enhancing SPARQL Query Generation for Knowledge Base Question Answering Systems by Learning to Correct Triplets

Enhancing SPARQL Query Generation for Knowledge Base Question Answering Systems by Learning to Correct Triplets

SGPT: A Generative Approach for SPARQL Query Generation From Natural Language Questions

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

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