@inbook{46516, 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 Transformerarchitecture. 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.}}, author = {{Ahmed, Abdullah Fathi Ahmed and Firmansyah, Asep Fajar and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Natural Language Processing and Information Systems}}, isbn = {{9783031353192}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Explainable Integration of Knowledge Graphs Using Large Language Models}}}, doi = {{10.1007/978-3-031-35320-8_9}}, year = {{2023}}, } @inproceedings{29486, author = {{Firmansyah, Asep Fajar and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the 11th on Knowledge Capture Conference}}, isbn = {{978-1-4503-8457-5}}, pages = {{73–80}}, publisher = {{ACM}}, title = {{{GATES: Using Graph Attention Networks for Entity Summarization}}}, doi = {{10.1145/3460210.3493574}}, year = {{2021}}, }