QART: A Framework to Transform Natural Language Questions and Answers into RDF Triples

Regino, André Gomes; Caus, Rodrigo; Hochgreb, Victor; Reis, J. C. dos

doi:10.5220/0011529200003335

Cited by 3 publications

(1 citation statement)

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“…Also the divide and conquer technique can be similarly applied on RDF graphs as edge based representation. The core structure of the RDF is a set of triples, each consisting of a subject, a predicate and an object [30]. This set, as such, constitutes an RDF graph, that can be visualized as a node and directed-arc diagram, in which each triple is represented as a node-arc-node link [4] [3].…”

Section: Resource Description Framework (Rdf) : (First Phase)mentioning

confidence: 99%

A Two-Phase Bootstrap Approach to Facilitate Conversion from Text to Semantic Graph

Abd-Elrahem,

El-gamal,

Abd-Elfattah

et al. 2024

Preprint

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This paper presents a two-phase un-learnable approach to convert a text to its semantic graph. In the first phase: divide and conquer, the input text is divided into small pieces manageable by the available text to graph conversion tool (e.g.: Senna). This phase as such yields a collection of small subgraphs that don’t represent the entire text as a whole, however each individual small subgraph represents a corresponding piece of input text. In the second phase: focus attention, the underlying subgraphs are appended together by making use of a bootstrap algorithm to provide a strongly connected single graph that represents the entire input text. In the two phases, both SRL and RDF are considered and thoroughly explained. Accordingly, the corresponding two algorithms on divide and conquer and focus attention are bootstrapped (for both SRL and RDF), are evaluated and compared. The implementation of the such algorithms has indicated that this approach can be used with advantages of being simple, fast, straightforward and practical, which makes it attractive for those NLP researchers who are interested in converting texts to semantic graphs.

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Section: Resource Description Framework (Rdf) : (First Phase)mentioning

confidence: 99%

A Two-Phase Bootstrap Approach to Facilitate Conversion from Text to Semantic Graph

Abd-Elrahem,

El-gamal,

Abd-Elfattah

et al. 2024

Preprint

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From Natural Language Texts to RDF Triples: A Novel Approach to Generating e-Commerce Knowledge Graphs

Regino,

Caus,

Hochgreb

et al. 2023

Communications in Computer and Information Science

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Semantic Structuring of E-commerce Texts: The QART Framework

Regino,

dos Reis

2024

Anais Estendidos Do XXXIX Simpósio Brasileiro De Banco De Dados (SBBD Estendido 2024)

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The challenge of transforming natural language texts into structured knowledge representations is important to enhance data integration in e-commerce. We developed the QART framework to address this challenge of converting e-commerce questions and answers into RDF triples for integration into existing Knowledge Graphs (KGs). The QART framework consists of four main steps: field selection and pre-processing, text-to-text conversion, text triplifying, and RDF triple curation. These steps aim to manage the volume and complexity of e-commerce data while ensuring semantic correctness and consistency with predefined ontologies. Our evaluations demonstrated that intermediary steps, such as text summarization, produce competitive results and can improve the quality of the resulting triples.

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QART: A Framework to Transform Natural Language Questions and Answers into RDF Triples

Cited by 3 publications

References 0 publications

A Two-Phase Bootstrap Approach to Facilitate Conversion from Text to Semantic Graph

A Two-Phase Bootstrap Approach to Facilitate Conversion from Text to Semantic Graph

From Natural Language Texts to RDF Triples: A Novel Approach to Generating e-Commerce Knowledge Graphs

Semantic Structuring of E-commerce Texts: The QART Framework

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