Domain experts often rely on most recent knowledge for apprehending and disseminating specific biological processes that help them design strategies for developing prevention and therapeutic decision-making in various disease scenarios. A challenging scenarios for artificial intelligence (AI) is using biomedical data (e.g., texts, imaging, omics, and clinical) to provide diagnosis and treatment recommendations for cancerous conditions.~Data and knowledge about biomedical entities like cancer, drugs, genes, proteins, and their mechanism is spread across structured (knowledge bases (KBs)) and unstructured (e.g., scientific articles) sources. A large-scale knowledge graph (KG) can be constructed by integrating and extracting facts about semantically interrelated entities and relations. Such a KG not only allows exploration and question answering (QA) but also enables domain experts to deduce new knowledge. However, exploring and querying large-scale KGs is tedious for non-domain users due to their lack of understanding of the data assets and semantic technologies. In this paper, we develop a domain KG to leverage cancer-specific biomarker discovery and interactive QA. For this, we constructed a domain ontology called OncoNet Ontology (ONO), which enables semantic reasoning for validating gene-disease (different types of cancer) relations. The KG is further enriched by harmonizing the ONO, metadata, controlled vocabularies, and biomedical concepts from scientific articles by employing BioBERT- and SciBERT-based information extractors. Further, since the biomedical domain is evolving, where new findings often replace old ones, without having access to up-to-date scientific findings, there is a high chance an AI system exhibits concept drift while providing diagnosis and treatment. Therefore, we fine-tune the KG using large language models (LLMs) based on more recent articles and KBs.

翻译：领域专家通常依赖最新知识来理解和传播特定生物过程，从而制定不同疾病场景下的预防策略和治疗决策。人工智能面临的一项挑战性任务是利用生物医学数据（如文本、影像、组学和临床数据）为癌症诊断和治疗提供建议。关于癌症、药物、基因、蛋白质等生物实体及其机制的实体知识广泛分布于结构化知识库和非结构化科学文献中。通过整合和抽取语义相关的实体与关系事实，可以构建大规模知识图谱。此类知识图谱不仅能支持探索性查询和问答，还能帮助领域专家推导新知识。然而，非专业用户因缺乏对数据资产和语义技术的理解，在探索和查询大规模知识图谱时存在困难。本文构建了一个面向癌症特异性生物标志物发现与交互式问答的领域知识图谱。为此，我们设计了名为OncoNet本体（ONO）的领域本体，该本体支持基因-疾病（不同类型癌症）关系的语义推理。通过采用基于BioBERT和SciBERT的信息抽取器，整合ONO本体、元数据、受控词汇表及科学文献中的生物医学概念，进一步丰富了知识图谱内容。此外，由于生物医学领域持续发展，新发现常取代旧结论，若无法获取最新科学成果，AI系统在提供诊断和治疗时极易出现概念漂移。因此，我们基于最新文献和知识库，利用大型语言模型对知识图谱进行了微调优化。