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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | https://arxiv.org/abs/2601.02604 |
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| _version_ | 1866911356332015616 |
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| author | Cisneros, Cesar Felipe Martínez Bautista, Jesús Ulises Quiroz Solano, Claudia Anahí Guzmán Rivera, Bogdan Kaleb García Pacheco, Iván García Martínez, Yalbi Itzel Balderas Adebayoc, Kolawole John Fernández, Ignacio Arroyo |
| author_facet | Cisneros, Cesar Felipe Martínez Bautista, Jesús Ulises Quiroz Solano, Claudia Anahí Guzmán Rivera, Bogdan Kaleb García Pacheco, Iván García Martínez, Yalbi Itzel Balderas Adebayoc, Kolawole John Fernández, Ignacio Arroyo |
| contents | The integration of Large Language Models (LLMs) into biomedical research offers new opportunities for domainspecific reasoning and knowledge representation. However, their performance depends heavily on the semantic quality of training data. In oncology, where precision and interpretability are vital, scalable methods for constructing structured knowledge bases are essential for effective fine-tuning. This study presents a pipeline for developing a lung cancer knowledge base using Open Information Extraction (OpenIE). The process includes: (1) identifying medical concepts with the MeSH thesaurus; (2) filtering open-access PubMed literature with permissive licenses (CC0); (3) extracting (subject, relation, object) triplets using OpenIE method; and (4) enriching triplet sets with Named Entity Recognition (NER) to ensure biomedical relevance. The resulting triplet sets provide a domain-specific, large-scale, and noise-aware resource for fine-tuning LLMs. We evaluated T5 models finetuned on this dataset through Supervised Semantic Fine-Tuning. Comparative assessments with ROUGE and BERTScore show significantly improved performance and semantic coherence, demonstrating the potential of OpenIE-derived resources as scalable, low-cost solutions for enhancing biomedical NLP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02604 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable Construction of a Lung Cancer Knowledge Base: Profiling Semantic Reasoning in LLMs Cisneros, Cesar Felipe Martínez Bautista, Jesús Ulises Quiroz Solano, Claudia Anahí Guzmán Rivera, Bogdan Kaleb García Pacheco, Iván García Martínez, Yalbi Itzel Balderas Adebayoc, Kolawole John Fernández, Ignacio Arroyo Computation and Language The integration of Large Language Models (LLMs) into biomedical research offers new opportunities for domainspecific reasoning and knowledge representation. However, their performance depends heavily on the semantic quality of training data. In oncology, where precision and interpretability are vital, scalable methods for constructing structured knowledge bases are essential for effective fine-tuning. This study presents a pipeline for developing a lung cancer knowledge base using Open Information Extraction (OpenIE). The process includes: (1) identifying medical concepts with the MeSH thesaurus; (2) filtering open-access PubMed literature with permissive licenses (CC0); (3) extracting (subject, relation, object) triplets using OpenIE method; and (4) enriching triplet sets with Named Entity Recognition (NER) to ensure biomedical relevance. The resulting triplet sets provide a domain-specific, large-scale, and noise-aware resource for fine-tuning LLMs. We evaluated T5 models finetuned on this dataset through Supervised Semantic Fine-Tuning. Comparative assessments with ROUGE and BERTScore show significantly improved performance and semantic coherence, demonstrating the potential of OpenIE-derived resources as scalable, low-cost solutions for enhancing biomedical NLP. |
| title | Scalable Construction of a Lung Cancer Knowledge Base: Profiling Semantic Reasoning in LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.02604 |