A Systematic Literature Review on RDF Triple Generation From Natural Language Texts

We live in a big data era of unstructured data expressed as natural language (NL) texts. As the volume of text-based information grows, effective methods for encoding and extracting meaningful knowledge from this corpus are of paramount relevance. A challenging task concerns transforming NL texts into structured and semantically rich data. Semantic web technologies have revolutionized how we represent and access structured knowledge. Resource description framework (RDF) triples serve as a fundamental building block for this purpose, enabling the integration of diverse data sources. This investigation examines methods for RDF triple generation and knowledge graphs (KGs) enhancement from NL texts. This study area presents wide-ranging applications encompassing knowledge representation, data integration, NL understanding, and information retrieval. Our systematic literature review addresses the understanding, characterization, and identification of challenges and limitations in existing approaches to RDF triple generation from NL texts and their inclusion into an existing KG. We retrieved, categorized, and analyzed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>150</mml:mn> </mml:math> articles from several scientific databases. We provide a comprehensive overview of the field, identify research gaps, and provide directions for future research. We found the most commonly available study categories, especially considering the domain, target language, the public availability of datasets, and real-world applications. Our results reveal a growing trend in this field in the last few years related to the use of transformer-based machine learning methods for triple generation. Our study also drives innovation by highlighting open research questions and providing a road map for future investigations.