用逻辑图结构高效扩展实体集合，避免全量构建图。
Navigating Taxonomic Expansions of Entity Sets Driven by Knowledge Bases
- 构建基于逻辑公式的有向无环扩张图，支持语义泛化。
- 在实体描述受限条件下，可高效判断节点间关系。
- 适合需增量扩展知识库的场景，无需预生成完整图。
识别实体间的相似性是人类认知与计算智能的核心。实体集扩展任务旨在从初始实体集出发，发现具有相关语义属性的新增实体，并可迭代扩展为更广集合。传统线性方法忽略知识资源中的丰富层级结构。近期提出的逻辑框架引入扩张图：一种根节点有向无环图，每个节点由逻辑公式标记，表示语义泛化，边表示严格语义包含。该结构支持基于知识库的分类式扩展。然而，实际应用中图规模过大，全量生成不现实。为此，我们形式化了判断两元组是否可比、不可比或同属一节点的推理任务。结果表明，在合理假设下（如限制输入大小或实体描述长度），这些任务可高效实现。这支持局部、增量式导航扩张图，无需预先构造完整图，适用于实际应用场景。
原文摘要 · Abstract (English)
Recognizing similarities among entities is central to both human cognition and computational intelligence. Within this broader landscape, Entity Set Expansion is one prominent task aimed at taking an initial set of (tuples of) entities and identifying additional ones that share relevant semantic properties with the former -- potentially repeating the process to form increasingly broader sets. However, this ``linear'' approach does not unveil the richer ``taxonomic'' structures present in knowledge resources. A recent logic-based framework introduces the notion of an expansion graph: a rooted directed acyclic graph where each node represents a semantic generalization labeled by a logical formula, and edges encode strict semantic inclusion. This structure supports taxonomic expansions of entity sets driven by knowledge bases. Yet, the potentially large size of such graphs may make full materialization impractical in real-world scenarios. To overcome this, we formalize reasoning tasks that check whether two tuples belong to comparable, incomparable, or the same nodes in the graph. Our results show that, under realistic assumptions -- such as bounding the input or limiting entity descriptions -- these tasks can be implemented efficiently. This enables local, incremental navigation of expansion graphs, supporting practical applications without requiring full graph construction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。