RELATE: Relation Extraction in Biomedical Abstracts with LLMs and Ontology Constraints

• URL: http://arxiv.org/abs/2509.19057v1
• Date: Tue, 23 Sep 2025 14:21:46 GMT
• Title: RELATE: Relation Extraction in Biomedical Abstracts with LLMs and Ontology Constraints
• Abstract summary: We introduce RELATE, a three-stage pipeline that maps LLM-extracted relations to standardized KGs.<n>The pipeline includes: (1) preprocessing with embeddings, (2) similarity-based retrieval enhanced with SapBERT, and (3)-based reranking with explicit negation handling.<n>On the ChemProt benchmark, RELATE achieves 52% exact match and 94% accuracy@10, in 2,400 HEAL Project abstracts.
• Score: 2.844531679990474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Biomedical knowledge graphs (KGs) are vital for drug discovery and clinical decision support but remain incomplete. Large language models (LLMs) excel at extracting biomedical relations, yet their outputs lack standardization and alignment with ontologies, limiting KG integration. We introduce RELATE, a three-stage pipeline that maps LLM-extracted relations to standardized ontology predicates using ChemProt and the Biolink Model. The pipeline includes: (1) ontology preprocessing with predicate embeddings, (2) similarity-based retrieval enhanced with SapBERT, and (3) LLM-based reranking with explicit negation handling. This approach transforms relation extraction from free-text outputs to structured, ontology-constrained representations. On the ChemProt benchmark, RELATE achieves 52% exact match and 94% accuracy@10, and in 2,400 HEAL Project abstracts, it effectively rejects irrelevant associations (0.4%) and identifies negated assertions. RELATE captures nuanced biomedical relationships while ensuring quality for KG augmentation. By combining vector search with contextual LLM reasoning, RELATE provides a scalable, semantically accurate framework for converting unstructured biomedical literature into standardized KGs.

Related papers

• ANCHOR-RE: An Agentic Neuro-Symbolic Framework for Grounded Biomedical Relation Extraction [5.624249017853434]
  ANCHOR-RE is a framework that integrates ontology-guided reasoning, external knowledge grounding, and data-driven verification rules.<n>We evaluated it on three BioRE benchmarks (SemRepGS, DDI, and ChemProt) using both proprietary and open-weight LLMs.<n>Neuro-symbolic reasoning can improve the reliability of LLM-based BioRE without fine-tuning.
  arXiv  Detail & Related papers  (2026-08-04T05:36:19Z)
• Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field [0.8633013637160062]
  We assess the performance of five small, open-source Large Language Models (LLMs) in identifying semantic relationships between biomedical concepts.<n>We analyse three adaptation strategies: standard prompting, Chain-of-Thought, and fine-tuning.<n>These results confirm that direct fine-tuning effectively exceeds the reasoning bottlenecks of smaller LLMs.
  arXiv  Detail & Related papers  (2026-07-20T12:48:41Z)
• Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning? [0.0]
  We investigate few-shot BioRE using prompt-based learning with large language models (LLMs)<n>We compare two task formulations: pairwise classification, which predicts relations for individual entity pairs, and joint generation, which extracts multiple relations in a single model call.<n>Experiments on the BioREDirect dataset reveal a clear precision-recall trade-off. Pairwise classification achieves higher recall, whereas joint generation is more precise and computationally efficient.
  arXiv  Detail & Related papers  (2026-06-13T17:37:50Z)
• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• Unlocking Biological Workflows for Robust Protein-Text Question Answering: A Dual-Dimensional RAG Framework [60.82334952881798]
  Protein-Text Question Answering (QA) is crucial for interpreting biological sequences through natural language.<n>We propose 2D-ProteinRAG, a novel framework that empowers Large Language Models (LLMs) to operate within the gold-standard biological research workflow.<n>We show that 2D-Protein consistently achieves state-of-the-art performance, outperforming fine-tuned baselines and other RAG methods.
  arXiv  Detail & Related papers  (2026-05-17T05:03:24Z)
• Eligibility-Aware Evidence Synthesis: An Agentic Framework for Clinical Trial Meta-Analysis [49.83108591873481]
  EligMeta is an agentic framework that integrates automated trial discovery with eligibility-aware meta-analysis.<n>The framework structures eligibility criteria and computes similarity-based study weights reflecting population alignment between target trials.
  arXiv  Detail & Related papers  (2026-04-03T03:18:50Z)
• A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine [59.78991974851707]
  Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis.<n>Most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems.<n>We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications.
  arXiv  Detail & Related papers  (2026-01-29T18:48:21Z)
• AI Co-Scientist for Knowledge Synthesis in Medical Contexts: A Proof of Concept [0.0]
  We present an AI for scalable and transparent knowledge synthesis based on explicit formalization of Population, Intervention, Comparator, Outcome, and Study design (PICOS)<n>The platform integrates relational storage, vector-based semantic retrieval, and a Neo4j knowledge graph.<n>Results show that PICOS-aware and explainable natural language processing can improve the scalability, transparency, and efficiency of evidence synthesis.
  arXiv  Detail & Related papers  (2026-01-16T23:07:58Z)
• Scalable Construction of a Lung Cancer Knowledge Base: Profiling Semantic Reasoning in LLMs [0.0]
  This study presents a pipeline for developing a lung cancer knowledge base using Open Information Extraction (OpenIE)<n>The resulting triplet sets provide a domain-specific, large-scale, and noise-aware resource for fine-tuning Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2026-01-05T23:40:00Z)
• MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph [57.54231831309079]
  We introduce MedKGent, a framework for constructing temporally evolving medical Knowledge Graphs.<n>We simulate the emergence of biomedical knowledge via a fine-grained daily time series.<n>The resulting KG contains 156,275 entities and 2,971,384 relational triples.
  arXiv  Detail & Related papers  (2025-08-17T15:14:03Z)
• HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis [55.2480439325792]
  HySemRAG is a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG)<n>System addresses limitations in existing RAG architectures through a multi-layered approach.
  arXiv  Detail & Related papers  (2025-08-01T20:30:42Z)
• Zero-Shot Document-Level Biomedical Relation Extraction via Scenario-based Prompt Design in Two-Stage with LLM [6.26004554105527]
  We propose a novel approach to achieve the same results from unannotated full documents using general large language models (LLMs) with lower hardware and labor costs.<n>Our approach combines two major stages: named entity recognition (NER) and relation extraction (RE)<n>To enhance the effectiveness of prompt, we propose a five-part template structure and a scenario-based prompt design principles.
  arXiv  Detail & Related papers  (2025-05-02T07:33:20Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning.<n>Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• Zero-shot Causal Graph Extrapolation from Text via LLMs [50.596179963913045]
  We evaluate the ability of large language models (LLMs) to infer causal relations from natural language. LLMs show competitive performance in a benchmark of pairwise relations without needing (explicit) training samples. We extend our approach to extrapolating causal graphs through iterated pairwise queries.
  arXiv  Detail & Related papers  (2023-12-22T13:14:38Z)
• High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models [1.9665865095034865]
  We formulate the relation extraction task as binary classifications for large language models. We designate the main title as the tail entity and explicitly incorporate it into the context. Longer contents are sliced into text chunks, embedded, and retrieved with additional embedding models.
  arXiv  Detail & Related papers  (2023-12-13T16:43:41Z)
• Biomedical knowledge graph-optimized prompt generation for large language models [1.6658478064349376]
  Large Language Models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains like biomedicine. Here, we introduce a token-optimized and robust Knowledge Graph-based Retrieval Augmented Generation framework.
  arXiv  Detail & Related papers  (2023-11-29T03:07:00Z)
• Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models [59.89454513692417]
  Tabular data is often hidden in text, particularly in medical diagnostic reports. We propose a novel, simple, and effective methodology for extracting structured tabular data from textual medical reports, called TEMED-LLM. We demonstrate that our approach significantly outperforms state-of-the-art text classification models in medical diagnostics.
  arXiv  Detail & Related papers  (2023-06-08T09:12:28Z)
• Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation [0.0]
  This work explores the potential of Large Language Models for dialoguing with biomedical background knowledge. The framework involves of three evaluation steps, each assessing different aspects sequentially: fluency, prompt alignment, semantic coherence, factual knowledge, and specificity of the generated responses. The work provides a systematic assessment on the ability of eleven state-of-the-art models LLMs, including ChatGPT, GPT-4 and Llama 2, in two prompting-based tasks.
  arXiv  Detail & Related papers  (2023-05-28T22:46:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.