Recognizing biomedical concepts in the text is vital for ontology refinement, knowledge graph construction, and concept relationship discovery. However, traditional concept recognition methods, relying on explicit mention identification, often fail to capture complex concepts not explicitly stated in the text. To overcome this limitation, we introduce MA-COIR, a framework that reformulates concept recognition as an indexing-recognition task. By assigning semantic search indexes (ssIDs) to concepts, MA-COIR resolves ambiguities in ontology entries and enhances recognition efficiency. Using a pretrained BART-based model fine-tuned on small datasets, our approach reduces computational requirements to facilitate adoption by domain experts. Furthermore, we incorporate large language model (LLM)-generated queries and synthetic data to improve recognition in low-resource settings. Experimental results on three scenarios (CDR, HPO, and HOIP) highlight the effectiveness of MA-COIR in recognizing both explicit and implicit concepts without the need for mention-level annotations during inference, advancing ontology-driven concept recognition in biomedical domain applications. Our code and constructed data are available at https://github.com/sl-633/macoir-master.

Automatic biomedical annotation is essential for advancing medical research, diagnosis, and treatment. However, it presents significant challenges, especially when entities are not explicitly mentioned in the text, leading to difficulties in extraction of relevant information. These challenges are intensified by unclear terminology, implicit background knowledge, and the lack of labeled training data. Annotating with a specific ontology adds another layer of complexity, as it requires aligning text with a predefined set of concepts and relationships. Manual annotation is time-consuming and expensive, highlighting the need for automated systems to handle large volumes of biomedical data efficiently. In this paper, we propose an entailment-based zero-shot text classification approach to annotate biomedical text passages using the Homeostasis Imbalance Process (HOIP) ontology. Our method reformulates the annotation task as a multi-class, multi-label classification problem and uses natural language inference to classify text into related HOIP processes. Experimental results show promising performance, especially when processes are not explicitly mentioned, highlighting the effectiveness of our approach for ontological annotation of biomedical literature.

Entity alignment (EA) involves identifying and linking equivalent entities across different knowledge graphs (KGs). While knowledge graphs provide structured information about real-world entities, only a small fraction of these entities are aligned. The entity alignment process is challenging due to heterogeneity in KGs, such as differences in structure, terminology, and attribute details. Traditional EA methods use multi-aspect entity embeddings to align entities. Although these methods perform well in certain scenarios, their effective- ness is often constrained by sparse or incomplete data in knowledge graphs and the limitations of embedding techniques. We propose ProLEA ( Profile Generation and Reasoning with LLMs for Entity Alignment) an entity alignment method that combines large language models (LLMs) with entity embed- dings. LLMs generate contextual profiles for entities based on their properties. Candidate entities identified by entity embedding techniques are then re-evaluated by the LLMs, using its background knowledge and the generated profile. A thresholding mechanism is introduced to resolve conflicts between LLMs predictions and embedding-based alignments. This method enhances alignment accuracy, robustness, and explainability, particularly for complex, het- erogeneous knowledge graphs. Furthermore, ProLEA is a generalized framework. Its profile generation and LLM-enhanced entity align- ment components can improve the performance of existing entity alignment models.

Biomedical information extraction is crucial for advancing research, enhancing healthcare, and discovering treatments by efficiently analyzing extensive data. Given the extensive amount of biomedical data available, automated information extraction methods are necessary due to manual extraction’s labor-intensive, expertise-dependent, and costly nature. In this paper, we propose a novel two-stage system for information extraction where we annotate biomedical articles based on a specific ontology (HOIP). The major challenge is annotating relation between biomedical processes often not explicitly mentioned in text articles. Here, we first predict the candidate processes and then determine the relationships between these processes. The experimental results show promising outcomes in mention-agnostic process identification using Large Language Models (LLMs). In relation classification, BERT-based supervised models still outperform LLMs significantly. The end-to-end evaluation results suggest the difficulty of this task and room for improvement in both process identification and relation classification.