SPARQL as a Foreign Language

Soru, Tommaso; Marx, Edgard; Moussallem, Diego; Publio, Gustavo; Valdestilhas, André; Esteves, Diego; Neto, Ciro Baron

doi:10.48550/arxiv.1708.07624

Cited by 5 publications

(7 citation statements)

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“…Recent studies have introduced methodologies that utilize literal values instead of IRI encodings as inputs or outputs for models. For instance, Soru et al [37,38] developed a technique named SPARQL 1:1 encoding, which utilizes character values to represent structural elements, including parentheses and punctuation marks. Diomedi and Hogan [39] applied Neural Machine Translation (NMT) to translate query templates containing placeholders.…”

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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“…Soru et al (2020) and Yin et al (2021) have considered the conversion as a language translation problem where SPARQL is the foreign language. Soru et al (2020) implemented Long Short-Term Memory (LSTM) architecture to build sequence-to-sequence models and train the model over a dataset extracted from DBPedia. Then, Yin et al (2021) extended the work by investigating the use of eight Neural Machine Translation (NMT) methods built using Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer.…”

Section: Introductionmentioning

confidence: 99%

CASBERT: BERT-based retrieval for compositely annotated biosimulation model entities

Munarko

Rampadarath²,

Nickerson³

2023

Front. Bioinform.

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Maximising FAIRness of biosimulation models requires a comprehensive description of model entities such as reactions, variables, and components. The COmputational Modeling in BIology NEtwork (COMBINE) community encourages the use of Resource Description Framework with composite annotations that semantically involve ontologies to ensure completeness and accuracy. These annotations facilitate scientists to find models or detailed information to inform further reuse, such as model composition, reproduction, and curation. SPARQL has been recommended as a key standard to access semantic annotation with RDF, which helps get entities precisely. However, SPARQL is unsuitable for most repository users who explore biosimulation models freely without adequate knowledge of ontologies, RDF structure, and SPARQL syntax. We propose here a text-based information retrieval approach, CASBERT, that is easy to use and can present candidates of relevant entities from models across a repository’s contents. CASBERT adapts Bidirectional Encoder Representations from Transformers (BERT), where each composite annotation about an entity is converted into an entity embedding for subsequent storage in a list of entity embeddings. For entity lookup, a query is transformed to a query embedding and compared to the entity embeddings, and then the entities are displayed in order based on their similarity. The list structure makes it possible to implement CASBERT as an efficient search engine product, with inexpensive addition, modification, and insertion of entity embedding. To demonstrate and test CASBERT, we created a dataset for testing from the Physiome Model Repository and a static export of the BioModels database consisting of query-entities pairs. Measured using Mean Average Precision and Mean Reciprocal Rank, we found that our approach can perform better than the traditional bag-of-words method.

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“…The three main types of SPARQL generation are Semantic Query Graph (SQG) searches [2], [3], [8], template designs [1], and machine learning solutions [7], [9]. Based on SQG generated by a dependency tree structure of a question, Ochieng [8] proposed a framework to translate natural language to SPARQL.…”

Section: Introductionmentioning

confidence: 99%

Efficient SPARQL Queries Generator for Question Answering Systems

Chen

Lin

2022

IEEE Access

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Much like traditional database querying, the question answering process in a Question Answering (QA) system involves converting a user's question input into query grammar, querying the knowledge base through the query grammar, and finally returning the query result (i.e., the answer) to the user. The accuracy of query grammar generation is therefore important in determining whether a Question Answering system can produce a correct answer. Generally speaking, incorrect query grammar will never find the right answer. SPARQL is the most frequently used query language in question answering systems. In the past, SPARQL was generated based on graph structures, such as dependency trees, syntax trees and so on. However, the query cost of generating SPARQL is high, which creates long processing times to answer questions. To reduce the query cost, this work proposes a low-cost SPARQL generator named Light-QAWizard, which integrates multi-label classification into a recurrent neural network (RNN), builds a template classifier, and generates corresponding query grammars based on the results of template classifier. Light-QAWizard reduces query frequency to DBpedia by aggregating multiple outputs into a single output using multilabel classification. In the experimental results, Light-QAWizard's performance on Precision, Recall and F-measure metrics were evaluated on the QALD-7, QALD8 and QALD-9 datasets. Not only did Light-QAWizard outperform all other models, but it also had a lower query cost that was nearly half that of QAWizard. bullet.png INDEX TERMS Question answering system (QA), SPARQL query, Query cost, Recurrent neural network (RNN), Question Answering over Linked Data (QALD).

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SPARQL as a Foreign Language

Cited by 5 publications

References 0 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

CASBERT: BERT-based retrieval for compositely annotated biosimulation model entities

Efficient SPARQL Queries Generator for Question Answering Systems

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