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arXiv 2603.03097cs.AIcs.DB
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Odin：多信号图智能用于知识图谱中的自主发现

Odin: Multi-Signal Graph Intelligence for Autonomous Discovery in Knowledge Graphs

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Muyukani Kizito, Elizabeth Nyambere

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AI总结：

Odin通过多信号整合解决知识图谱中的自主发现问题，提升模式发现质量和分析师效率。

摘要：

我们介绍了Odin，这是首个在知识图谱中无需先验指定即可自主发现有意义模式的图智能引擎。与基于检索的系统不同，Odin通过COMPASS（复合导向多信号路径评估）得分引导探索，这是一种新的度量标准，结合（1）通过个性化PageRank的结构重要性，（2）通过神经概率逻辑学习（NPLL）作为判别过滤器而非生成模型的语义合理性，（3）通过可配置衰减的时间相关性，以及（4）通过GNN识别的桥接实体和社区间亲和力得分的社区意识引导。这种多信号整合，特别是桥接评分机制，解决了“回音室”问题，即图探索容易被困在密集的本地社区中。我们正式化了自主发现问题，证明了我们评分函数的理论性质，并展示了多信号引导的束搜索在保持高召回率的同时，实现O(b·h)的复杂度，优于穷举探索。据我们所知，Odin是首个在受监管的生产环境中部署的自主发现系统（医疗和保险），展示了在模式发现质量和分析师效率方面的显著改进。我们的方法保持了完整的可追溯性——这对于受监管行业来说至关重要，因为幻觉是不可接受的。

英文摘要：

We present Odin, the first production-deployed graph intelligence engine for autonomous discovery of meaningful patterns in knowledge graphs without prior specification. Unlike retrieval-based systems that answer predefined queries, Odin guides exploration through the COMPASS (Composite Oriented Multi-signal Path Assessment) score, a novel metric that combines (1) structural importance via Personalized PageRank, (2) semantic plausibility through Neural Probabilistic Logic Learning (NPLL) used as a discriminative filter rather than generative model, (3) temporal relevance with configurable decay, and (4) community-aware guidance through GNN-identified bridge entities and inter-community affinity scores. This multi-signal integration, particularly the bridge scoring mechanism, addresses the "echo chamber" problem where graph exploration becomes trapped in dense local communities. We formalize the autonomous discovery problem, prove theoretical properties of our scoring function, and demonstrate that beam search with multi-signal guidance achieves $O(b \cdot h)$ complexity while maintaining high recall compared to exhaustive exploration. To our knowledge, Odin represents the first autonomous discovery system deployed in regulated production environments (healthcare and insurance), demonstrating significant improvements in pattern discovery quality and analyst efficiency. Our approach maintains complete provenance traceability -- a critical requirement for regulated industries where hallucination is unacceptable.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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