Jul 2026· Annual International Computer Software and Applications Conference· pp. 1967-1972· 0 citations· 11 references
Computer Science
Abstract
The exponential growth of biomedical regulatory documentation has created an urgent need for automated, scalable knowledge structuring mechanisms that do not rely on rigid, expert-curated ontologies. This paper presents the system implementation and empirical analysis of the Ontology-Free Biomedical Knowledge Graph Induction (OF-Bio-KG) framework, focusing on three dimensions: (i) the end-to-end computational pipeline, (ii) the structural and statistical properties of the induced knowledge graph, and (iii) a proposed crowd-sourced expert evaluation strategy intended for future deployment. The OF-Bio-KG framework operates on a corpus of 1,068 European Medicines Agency (EMA) pharmaceutical product leaflets totalling over 131 million tokens. Using the nomic-embed-text model for long-context semantic encoding, a Balanced K-Means Constrained clustering algorithm for thematic organization, and LLaMA-3 8B for ontology-free relational inference, the system produces a multi-level knowledge graph comprising 256,148 leaf nodes, 3,742 cluster-level concept nodes, over 1.6 million intra-cluster edges, and 147,000 inter-cluster edges, all persisted in Neo4j. A central challenge in this work is the absence of a groundtruth biomedical knowledge graph against which the induced graph can be evaluated using standard metrics. To address this, we propose a human-in-the-loop validation strategy planned for future deployment: a purpose-built web application will be made available to Master's students at ENSIT, who will act as domain-proficient annotators to explore and validate graph components. This paper describes the current system, its empirical results, and the design of this forthcoming evaluation platform.
Assessment of five small, open-source LLMs in identifying semantic relationships between biomedical concepts confirms that direct fine-tuning effectively exceeds the reasoning bottlenecks of smaller LLMs, providing an accurate, automated methodology for the construction and evolution of specialised biomedical ontologies.
Tanay Aggarwal, Angelo Salatino, Francesco Osborne et al.· arXiv.org· 0 citations
A layered reliability framework is defined in which graph-based inference addresses knowledge incompleteness, retrieval-augmented prompt control mitigates instability, and ontology grounding reduces semantic ambiguity, providing a foundation for more reliable biomedical AI systems.
Hierarchical Knowledge graph (KG)-based retrieval augmented generation (RAG) has emerged as a powerful approach for supporting large language models with structured knowledge. However, there are primary challenges: (i) the lack of methods for automatic KG construction using ontology expansion for low-resource languages such as Vietnamese, (ii) the absence of systematic evaluation for knowledge retrieval strategies leveraging the hierarchical structures. In this paper, we propose an end-to-end pipeline for KG construction and retrieval strategies evaluation. In the KG construction, we employ a three-phase hybrid relation extraction pipeline: intra-batch deduplication via Union-Find, approximate cross-batch search, and LLM extraction with a centroid filter that reduces prompts combined with a five-step dual-LLM validator to prevent bloated ontology. A two-tier architecture consists of unmergeable structural nodes to preserve the document structure and mergeable content nodes. The retrieval evaluation consists of three graph traversal strategies: Top-Down, Horizontal, and Bottom-Up, which are evaluated on a synthetically generated benchmark of 1,210 Vietnamese queries from 109 subgraphs, categorized by five query directions. In this paper, we construct the tree knowledge graph from Vietnamese high school History textbooks (nearly 400 pages) to produce 750 nodes and 4,341 semantic edges with controlled ontology growth from 40 to 41 types. Among experimental graph traversal strategies, the Top-Down strategy with structure surpasses the vector baseline by 4.7 percentage points in NDCG@10. As a result, tree-structural information provides valuable information beyond flat cosine similarity but degrades performance when the query does not require structural context.
In order to enable natural language interaction with structured knowledge bases based on ontologies, widely used to represent knowledge in multiple complex domains, Knowledge Graph Question Answering (KGQA) systems are required. Large Language Models (LLMs) can play a pivotal role in this context, however they suffer from factual inaccuracies, hallucinations, and difficulties in navigating complex semantic schemas. To address these limitations, this paper proposes a novel neuro-symbolic Agentic AI framework for KGQA on real-world knowledge graph. By integrating neural reasoning capabilities of LLMs with symbolic tools within a recursive flow orchestrated by LangGraph, the system is able to identify relevant terms, validate them based on specific ontology, and generate SPARQL queries for data retrieval. To demonstrate effectiveness of this approach an experimental evaluation using a real-world smart city knowledge base, implemented in the Snap4City platform, is carried out. Results indicate that the neuro-symbolic workflows improves the accuracy of information retrieval for non-expert users compared to standalone LLM solutions.
Zahra Fereidooni, M. Fanfani, G. Pantaleo et al.· International Conference on...· 0 citations
A two-stage LLM-assisted workflow for French maintenance regulations is presented: ontology engineering from a SEMLEG-based core ontology, followed by construction of an ontology-grounded French legal knowledge graph.
Génesis Montenegro, M. Billami, Catherine Faron et al.· arXiv.org· 0 citations
LLMBDC (Large Language Model for Biological Domains Oriented Clustering of Gene Ontology) provides a scalable, reproducible, and interpretable route to context-aware, system-level interpretation of GO enrichment results while preserving biological specificity.
Ximing Ran, Jie Xu, Peng Jin et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
