Abstract
Linked knowledge graphs build the backbone of many data-driven applications such as search engines, conversational agents and e-commerce solutions. Declarative link discovery frameworks use complex link specifications to express the conditions under which a link between two resources can be deemed to exist. However, understanding such complex link specifications is a challenging task for non-expert users of link discovery frameworks. In this paper, we address this drawback by devising NMV-LS, a language model-based verbalization approach for translating complex link specifications into natural language. NMV-LS relies on the results of rule-based link specification verbalization to apply continuous training on T5, a large language model based on the Transformer architecture. We evaluated NMV-LS on English and German datasets using well-known machine translation metrics such as BLUE, METEOR, ChrF++ and TER. Our results suggest that our approach achieves a verbalization performance close to that of humans and outperforms state of the art approaches. Our source code and datasets are publicly available at https://github.com/dice-group/NMV-LS.
Abdullah Fathi Ahmed and Asep Fajar Firmansyah contributed equally to this research.
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Acknowledgements
We acknowledge the support of the German Federal Ministry for Economic Affairs and Climate Action (BMWK) within project SPEAKER (01MK20011U), the German Federal Ministry of Education and Research (BMBF) within the EuroStars project PORQUE (01QE2056C), the German Research Foundation (DFG) within the project INGRID (NG 105/7-3), the Ministry of Culture and Science of North Rhine-Westphalia (MKW NRW) within the project SAIL (NW21-059D), the European Union’s Horizon Europe research and innovation programme within project ENEXA (101070305), and Mora Scholarship from the Ministry of Religious Affairs, Republic of Indonesia.
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Ahmed, A.F., Firmansyah, A.F., Sherif, M.A., Moussallem, D., Ngonga Ngomo, AC. (2023). Explainable Integration of Knowledge Graphs Using Large Language Models. In: Métais, E., Meziane, F., Sugumaran, V., Manning, W., Reiff-Marganiec, S. (eds) Natural Language Processing and Information Systems. NLDB 2023. Lecture Notes in Computer Science, vol 13913. Springer, Cham. https://doi.org/10.1007/978-3-031-35320-8_9
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