AI 中文总结
研究人工智能知识系统中实体重要性表示，提出受众评估与结构权威双信号框架，经电影实体实验验证，表明两维度是不可冗余信号，贡献是最小知识表示框架及维度必要性实证测试，支持任务感知AI知识系统。
AI 中文摘要
人工智能知识系统在检索、推荐、证据选择和知识密集型推理中需要实体重要性的表示。然而，重要性通常被简化为从人类反应或图结构得出的单一分数。本研究引入了一种可解释的双信号表示，其中每个实体由受众评估维度和结构权威维度来表征。该框架以电影实体作为实证验证领域进行评估，利用IMDb非商业数据集提供基于评分的受众排名，Wikidata支持实体对齐，英文维基百科超链接形成知识网络并用PageRank估计结构权威。实验表明两个维度之间存在统计学上显著但较弱的关联，结果显示受众评估和结构权威是不可冗余的信号，不应自动合并为单一的重要性标量概念。贡献在于一个最小知识表示框架及其维度必要性的实证测试，支持在应用特定上下文选择或聚合之前保留不同重要性信号的任务感知人工智能知识系统。
英文摘要
AI knowledge systems require representations of entity importance for retrieval, recommendation, evidence selection, and knowledge-intensive reasoning. Yet importance is often reduced to a single score derived from either human response or graph structure. Such compression may discard distinctions that matter when an AI system must choose among entities for different tasks. This study introduces an interpretable dual-signal representation in which each entity is characterized by an audience-evaluation dimension and a structural-authority dimension. The framework is evaluated using movie entities as an empirical validation domain. IMDb non-commercial datasets provide a rating-based audience ranking, Wikidata supports entity alignment, and English Wikipedia hyperlinks form the knowledge network on which PageRank estimates structural authority. Experiments on 482 entities and 13,690 directed relationships reveal a statistically significant but weak association between the two dimensions (Spearman rho = 0.2275, p < 0.001). Their overlap is only 10% in the top 10 and 34% in the top 100, while entity-level divergence occurs in both directions. The results show that audience evaluation and structural authority are non-redundant signals and should not automatically be collapsed into a single scalar notion of importance. The contribution is not a new ranking algorithm or learned embedding, but a minimal knowledge-representation framework and an empirical test of its dimensional necessity. The findings support task-aware AI knowledge systems that preserve distinct importance signals before applying context-specific selection or aggregation.
Comments12 pages, 3 figures, 4 tables
