区分观众评价与结构权威，让AI更准确判断实体重要性。
Representing Entity Importance in AI Knowledge Systems: A Dual-Signal Framework of Audience Evaluation and Structural Authority
- 用观众评分和结构权威双信号表示实体重要性。
- 两者相关性弱（斯皮尔曼ρ=0.2275），重叠率仅10%-34%。
- 适合需要精准选源的AI系统，如推荐与推理任务。
AI知识系统需对实体重要性进行建模，以支持检索、推荐、证据选择及知识密集型推理。然而，重要性常被简化为单一得分，仅来自人类反馈或图结构。这种压缩可能丢失关键差异，影响AI在不同任务中对实体的选择。本研究提出一种可解释的双信号表示框架，每个实体由观众评价维度与结构权威维度共同刻画。以电影实体为验证场景，使用IMDb非商业数据集获取评分型观众排名，Wikidata实现实体对齐，英文维基百科超链接构建知识网络并用PageRank估计结构权威。基于482个实体和13,690条有向关系的实验显示，两维度间存在统计显著但微弱的相关性（斯皮尔曼ρ = 0.2275，p < 0.001），其前10名重合率仅10%，前100名重合率34%，且实体层面存在双向偏离。结果表明，观众评价与结构权威是非冗余信号，不应自动合并为单一重要性标量。贡献不在于新排序算法或学习嵌入，而是一个最小化知识表示框架及其维度必要性的实证检验。研究支持任务感知的AI知识系统，在上下文特定选择或聚合前保留独立的重要性信号。
原文摘要 · Abstract (English)
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.
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