Datasets that include alignments between natural language and Knowledge Graphs are fundamental to a wide variety of Natural Language Processing and Generation tasks. Current state-of-the-art aligned datasets, though, are significantly impacted by reduced size and scarcity of covered domains, and their quality is difficult to evaluate. To compensate for these issues, we introduce SEALIon, a tool for extracting RDF triples from natural language textual corpora based on a human-in-the-loop approach. We present our first results of SEALIon’s approach, paving the way for further researches in the field of human-in-the-loop triple extraction.
Amianto Barbato, J., Cremaschi, M., Rula, A., Maurino, A. (2024). Toward a Human-in-the-Loop Approach to Create Training Datasets for RDF Lexicalisation. In Intelligent Systems and Applications Proceedings of the 2023 Intelligent Systems Conference (IntelliSys) Volume 1 (pp.84-101). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-47721-8_6].
Toward a Human-in-the-Loop Approach to Create Training Datasets for RDF Lexicalisation
Amianto Barbato, J
;Cremaschi, M;Rula, A;Maurino, A
2024
Abstract
Datasets that include alignments between natural language and Knowledge Graphs are fundamental to a wide variety of Natural Language Processing and Generation tasks. Current state-of-the-art aligned datasets, though, are significantly impacted by reduced size and scarcity of covered domains, and their quality is difficult to evaluate. To compensate for these issues, we introduce SEALIon, a tool for extracting RDF triples from natural language textual corpora based on a human-in-the-loop approach. We present our first results of SEALIon’s approach, paving the way for further researches in the field of human-in-the-loop triple extraction.
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