Text2AMR2FRED, converting text into RDF/OWL knowledge graphs via abstract meaning representation

Converting natural language text into structured, logically coherent knowledge graphs (KGs) enhances the ability to retrieve, organize, and analyze vast amounts of information at scale. This paper introduces Text2AMR2FRED, a text-to-KG pipeline that converts multilingual natural language text into logically coherent, interoperable KGs. Designed to support large-scale information retrieval and knowledge extraction, this pipeline addresses key limitations of existing semantic parsers and machine readers, including issues with logical consistency and interoperability. By adhering to Semantic Web standards, Text2AMR2FRED systematically structures text-based information and enhances it through integration with external knowledge sources, delivering enriched, semantically sound KGs ready for diverse applications. We obtain the output KGs by leveraging Abstract Meaning Representation (AMR) as an intermediate semantic parsing formalism, exploiting the progress achieved by text-to-AMR parsers employing pre-trained language models. We produce a manually validated KGs bank created by transforming a dataset of natural language sentences into KGs using Text2AMR2FRED and applying an intrinsic evaluation method that leverages Open Knowledge Extraction motifs.