OntoDoc：多智能体框架自动生成本体资源的自然语言描述
OntoDoc: Automatically Generating Ontology Resource Descriptions
做知识图谱的朋友可以看看这篇：OntoDoc 用四个智能体给本体术语自动写描述，完整性比模板方法最高多 2.8 分。
OntoDoc 是一个多智能体框架，可为 SPARQL 端点或 RDF 文件中的任意 IRI 自动生成自然语言描述。框架采用四阶段流水线：Database Context 智能体通过 SPARQL 采集语义三元组，Web Context 智能体检索 Wikipedia 和开放网络背景知识，Assumption Generation 智能体汇总中间假设，最后由 Description Generation 智能体生成描述。在生物医学、农业和跨领域三个知识图谱上，用 LLM 自动评分和 11 名标注者的人工评估进行测试。结果显示其完整性比 naive LLM 基线平均提升 12%，在 5 分制上比模板化本体 verbalizer 最高提升 2.8 分。
OntoDoc: Automatically Generating Ontology Resource Descriptions
Knowledge graphs and ontologies form the backbone of structured, machine-readable knowledge representation, yet ontology terms often lack natural language descriptions, limiting their accessibility and reuse. Existing ontology verbalization approaches tend to rely on rule-based or template-driven techniques that operate on strict, formal schema axioms, resulting in rigid verbalizations that fail to convey richer contextual semantics. We present OntoDoc, a multi-agent framework for automatically generating natural language descriptions for any Internationalized Resource Identifier (IRI) within a SPARQL endpoint or RDF file. OntoDoc operates through a four-stage pipeline: (1) a Database Context agent that adaptively explores the knowledge graph via SPARQL to collect semantically informative triples about the target IRI; (2) a Web Context agent that retrieves and re-ranks encyclopedic background knowledge from Wikipedia and the open web; (3) an Assumption Generation agent that synthesizes both contexts into a structured, evidence-grounded list of intermediate assumptions; and (4) a Description Generation agent that produces the final natural language description from the grounded assumptions. We evaluate OntoDoc on three knowledge graphs spanning biomedical, agricultural, and cross-domain settings, using both automatic scoring via Large Language Models (LLMs) and human evaluation with 11 annotators. OntoDoc consistently matches or exceeds a naive LLM baseline and a template-based ontology verbalizer in accuracy. Furthermore, it vastly outperforms both baselines in the completeness of the generated descriptions, delivering an average 12% improvement in completeness over the naive baseline across all domains, and improving completeness scores by as much as 2.8 points on a 5-point scale relative to the ontology verbalizer.