LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs
This paper presents LYRICEL, a framework integrating Knowledge Graph (KG) representation learning, Large Language Models (LLMs), and machine learning for reliable, explainable, and validatable cross-cultural lyric analysis. The core component, Sequential Language Model Integration (SLMI), enhances t...
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| Format: | Article |
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11121171/ |
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| author | Dimitrios P. Panagoulias Evangelia-Aikaterini Tsichrintzi Dionisios N. Sotiropoulos Konstantina Chrysafiadi Evangelos Sakkopoulos George A. Tsihrintzis Maria Virvou |
| author_facet | Dimitrios P. Panagoulias Evangelia-Aikaterini Tsichrintzi Dionisios N. Sotiropoulos Konstantina Chrysafiadi Evangelos Sakkopoulos George A. Tsihrintzis Maria Virvou |
| author_sort | Dimitrios P. Panagoulias |
| collection | DOAJ |
| description | This paper presents LYRICEL, a framework integrating Knowledge Graph (KG) representation learning, Large Language Models (LLMs), and machine learning for reliable, explainable, and validatable cross-cultural lyric analysis. The core component, Sequential Language Model Integration (SLMI), enhances the interpretability and reliability of transformer-based LLMs by addressing explainability and validation challenges through Retrieval-Augmented Generation (RAG), hybrid search, and rule-based evaluation. An important feature of LYRICEL is its use of KG visualizations, which serve as dynamic links to improve interpretability and validatability by structuring data relationships and sources. These visualizations are central to advancements in four areas: KG representation learning, knowledge acquisition, temporal KGs, and knowledge-aware applications. Tested on Greek folk music with models like GPT-4o and BERT, LYRICEL’s trustworthiness is assessed using the VIRTSI model, which quantifies cognitive trust in human-computer interactions. The framework shows strong potential for cross-cultural applications, particularly in languages such as Modern Greek which encompasses a rich cultural heritage spanning centuries of history and traditions resulting in a complex study. The outcomes of GPT-enabled LYRICEL are compared to ChatGPT alone and show a significant improvement in the reliability and efficiency of interactions that can reach a global audience, enhancing the accessibility and understanding of diverse cultural heritages. |
| format | Article |
| id | doaj-art-f51e9d44fc9b4fb582d86361ec1ea064 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f51e9d44fc9b4fb582d86361ec1ea0642025-08-20T03:47:06ZengIEEEIEEE Access2169-35362025-01-011314198514200610.1109/ACCESS.2025.359721311121171LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek SongsDimitrios P. Panagoulias0https://orcid.org/0000-0002-9421-141XEvangelia-Aikaterini Tsichrintzi1https://orcid.org/0009-0009-3821-1333Dionisios N. Sotiropoulos2Konstantina Chrysafiadi3https://orcid.org/0000-0001-8096-1407Evangelos Sakkopoulos4https://orcid.org/0000-0002-6852-384XGeorge A. Tsihrintzis5https://orcid.org/0000-0002-2716-4035Maria Virvou6https://orcid.org/0000-0002-4008-4654Department of Informatics, University of Piraeus, Piraeus, GreeceDepartment of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, GreeceDepartment of Informatics, University of Piraeus, Piraeus, GreeceDepartment of Informatics, University of Piraeus, Piraeus, GreeceDepartment of Informatics, University of Piraeus, Piraeus, GreeceDepartment of Informatics, University of Piraeus, Piraeus, GreeceDepartment of Informatics, University of Piraeus, Piraeus, GreeceThis paper presents LYRICEL, a framework integrating Knowledge Graph (KG) representation learning, Large Language Models (LLMs), and machine learning for reliable, explainable, and validatable cross-cultural lyric analysis. The core component, Sequential Language Model Integration (SLMI), enhances the interpretability and reliability of transformer-based LLMs by addressing explainability and validation challenges through Retrieval-Augmented Generation (RAG), hybrid search, and rule-based evaluation. An important feature of LYRICEL is its use of KG visualizations, which serve as dynamic links to improve interpretability and validatability by structuring data relationships and sources. These visualizations are central to advancements in four areas: KG representation learning, knowledge acquisition, temporal KGs, and knowledge-aware applications. Tested on Greek folk music with models like GPT-4o and BERT, LYRICEL’s trustworthiness is assessed using the VIRTSI model, which quantifies cognitive trust in human-computer interactions. The framework shows strong potential for cross-cultural applications, particularly in languages such as Modern Greek which encompasses a rich cultural heritage spanning centuries of history and traditions resulting in a complex study. The outcomes of GPT-enabled LYRICEL are compared to ChatGPT alone and show a significant improvement in the reliability and efficiency of interactions that can reach a global audience, enhancing the accessibility and understanding of diverse cultural heritages.https://ieeexplore.ieee.org/document/11121171/ChatGPTLLMse-learningmachine learningartificial intelligencecultural heritage |
| spellingShingle | Dimitrios P. Panagoulias Evangelia-Aikaterini Tsichrintzi Dionisios N. Sotiropoulos Konstantina Chrysafiadi Evangelos Sakkopoulos George A. Tsihrintzis Maria Virvou LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs IEEE Access ChatGPT LLMs e-learning machine learning artificial intelligence cultural heritage |
| title | LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs |
| title_full | LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs |
| title_fullStr | LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs |
| title_full_unstemmed | LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs |
| title_short | LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs |
| title_sort | lyricel knowledge graphs combined with large language models and machine learning for cross cultural analysis of lyrics x2014 the case of greek songs |
| topic | ChatGPT LLMs e-learning machine learning artificial intelligence cultural heritage |
| url | https://ieeexplore.ieee.org/document/11121171/ |
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