NER/RE strategies for biological graph completion
Natural Language Processing (NLP), a subset of artificial intelligence, facili- tates computer understanding of human language, with techniques like Named Entity Recognition (NER) and Relation Extraction (RE) essential for trans- forming unstructured text into structured, actionable data. In the bio...
| Autor: | |
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/419447 |
| Acceso en línea: | https://hdl.handle.net/2117/419447 |
| Access Level: | acceso abierto |
| Palabra clave: | Natural language processing (Computer science) Processament del Llenguatge Natural Reconeixement d'Entitats Nombrades Extracció de Relacions grafs de coneixement biològic extracció d'entitats extracció de relacions resums de PubMed ChemProt BioRED BioBERT SciBERT incrustació de gràfics de coneixement ComplEx RotatE terminologia biomèdica. Natural Language Processing Named Entity Recognition Relation Extraction biological knowledge graphs entity extraction relationship extraction PubMed abstracts knowledge graph embedding biomedical terminology. Tractament del llenguatge natural (Informàtica) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural |
| Sumario: | Natural Language Processing (NLP), a subset of artificial intelligence, facili- tates computer understanding of human language, with techniques like Named Entity Recognition (NER) and Relation Extraction (RE) essential for trans- forming unstructured text into structured, actionable data. In the biomedi- cal domain, NER identifies critical biological entities such as genes, proteins, and diseases while RE discovers relationships like gene-disease associations and protein-protein interactions. These methods are particularly valuable for cre- ating biological knowledge graphs, which model complex interactions among biological entities, supporting research in areas like drug discovery and disease mechanisms. However, challenges arise due to domain-specific language and the intricate nature of biological relationships, posing a need for more accurate, scalable extraction methods. This thesis aims to address these challenges by developing NER and RE strate- gies for biological texts, using both rule-based and pretrained models on PubMed abstracts. The study uses biomedical datasets, including ChemProt and BioRED, to evaluate performance in entity and relationship extraction, while focusing on improving precision and scalability for biological knowledge graph comple- tion. Specifically, pretrained models like BioBERT and SciBERT, designed for biomedical NLP, were applied, achieving high accuracy in identifying domain- specific entities and relationships. Additionally, these extracted entities and relationships were integrated into a biological knowledge graph, utilizing knowl- edge graph embedding techniques, such as ComplEx and RotatE, to represent the complex associations within biological systems. Results indicate that pretrained models excel in capturing biomedical terminol- ogy, with BioBERT showing particular strength in disease-related entity extrac- tion. Integration of text-mined data into knowledge graph embeddings enhances accuracy in relationship predictions, emphasizing the potential of combining structured and unstructured data for dynamic graph completion. By improv- ing both NER and RE techniques, this research contributes new methodologies for automating biological knowledge graph construction, supporting future ad- vancements in fields like genomics, drug discovery, and systems biology. |
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