为知识图谱中本体选择提供可量化的评估方法。
WiseOWL: A Methodology for Evaluating Ontological Descriptiveness and Semantic Correctness for Ontology Reuse and Ontology Recommendations
- 构建四维评分体系：描述完整性、定义准确性、结构连通性与层级广度。
- 在6个本体上验证，得分0-10分，支持交互式可视化分析。
- 适合需要科学选型本体的研究者或系统开发者。
语义网通过标准化概念含义，使内容对机器可操作且解释一致，促进高级分析。复用本体可加速开发并保证一致性，但选择最优本体仍具挑战，因作者缺乏系统性筛选标准，常依赖难以论证的直觉，限制了复用。为此，本文提出WiseOWL方法，一种用于本体复用与推荐的评估框架。该方法基于四个指标进行评分：(i) 描述充分性，衡量文档覆盖程度；(ii) 定义严谨性，采用前沿嵌入技术评估标签与定义的一致性；(iii) 连接性，反映结构上的互联程度；(iv) 层级广度，体现层级平衡性。输出归一化0-10分及可操作反馈。以Streamlit实现，支持OWL格式输入，转换为RDF Turtle，提供交互式可视化。在植物本体（PO）、基因本体（GO）、语义科学集成本体（SIO）、食品本体（FoodON）、都柏林核心（DC）和GoodRelations共6个本体上评估，结果表明该方法具有显著有效性。
原文摘要 · Abstract (English)
The Semantic Web standardizes concept meaning for humans and machines, enabling machine-operable content and consistent interpretation that improves advanced analytics. Reusing ontologies speeds development and enforces consistency, yet selecting the optimal choice is challenging because authors lack systematic selection criteria and often rely on intuition that is difficult to justify, limiting reuse. To solve this, WiseOWL is proposed, a methodology with scoring and guidance to select ontologies for reuse. It scores four metrics: (i) Well-Described, measuring documentation coverage; (ii) Well-Defined, using state-of-the-art embeddings to assess label-definition alignment; (iii) Connection, capturing structural interconnectedness; and (iv) Hierarchical Breadth, reflecting hierarchical balance. WiseOWL outputs normalized 0-10 scores with actionable feedback. Implemented as a Streamlit app, it ingests OWL format, converts to RDF Turtle, and provides interactive visualizations. Evaluation across six ontologies, including the Plant Ontology (PO), Gene Ontology (GO), Semanticscience Integrated Ontology (SIO), Food Ontology (FoodON), Dublin Core (DC), and GoodRelations, demonstrates promising effectiveness.
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