SPARQL Queries over Ontologies Under the Fixed-Domain Semantics

Rudolph, Sebastian; Schweizer, Lukas; Yao, Zhihao

doi:10.1007/978-3-030-29908-8_39

Cited by 1 publication

(2 citation statements)

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“…SPARQL is a query language used to express queries across diverse data sources, whether the data is stored natively as RDF or viewed as RDF via middleware. In recent years, the conversion of natural language questions (NLQs) to SPARQL queries gained further popularity to the growing number of graph-based applications [1]- [3]. Automatic query generation from NLQ is a long-standing research challenge with several factors contributing to its difficulty, including but not limited to understanding the complex aspects of syntax and semantics of the natural language question (i.e., ellipsis, ambiguity, lexical gap), error propagation in NLP pipelines, and skewed distribution of question types in training datasets.…”

Section: Introductionmentioning

confidence: 99%

“…(1) SPARQL templates are usually created manually or semiautomatically by domain experts, which is both time consuming and cost intensive, (2) The query templates are tailored to a particular KG, which results in potentially changing of the whole template set when the underlying graph is changed, (3) The extension of template sets to handle new question types is performed manually or semi-automatically, and (4) In pipeline-based approaches, the SPARQL generation module is dependent on the performance of the preceding modules (i.e., entity and relation linkers as well as ranking algorithms) and, thus, suffer from error propagation.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%