提出构建语义网络的新框架，用推理机制提升知识图谱质量。
Semantic Networks as Clues: A Theoretical Foundation and Process Optimization for Semantic Network Construction
- 将语义网络构建视为过程优化问题，整合三阶段流程
- 通过可解释性标准筛选候选网络，提升结果科学性
- 适合自然语言处理与知识发现研究者参考
本文聚焦两类任务：一是厘清一种用于表征文本非命题知识的语义网络（SN）的理论基础，指出该类网络作为现实线索而非替代品，难以定义金标准；二是提出名为ClueNetwork的框架，用于对多种语义网络构建（SNC）过程生成的候选网络进行排序。基于其作为推论工具的科学合理性，本文回顾了SNC的三个核心阶段：自动关键词提取（AKE）、边权重分配（EW）与社区检测（CD），并明确各阶段目标与操作。随后，定义并论证了三项评估标准（两项既有，一项重构），并基于此开展示例实验。最终，将SNC重构为过程优化问题（POP），建立融合局部标准的全局目标函数，由此提出完整框架ClueNetwork。
原文摘要 · Abstract (English)
The subject matter of this paper is twofold. One is to review the theoretical foundation of a specific type of Semantic Networks (SNs) representing textual non-propositional knowledge. The other involves proposing a framework (ClueNetwork) for ranking candidate SNs generated through various Semantic Network Construction (SNC) processes for the type. In the first fold, it is clarified that the type serves as clues, not surrogates, of reality, making gold standards elusive. Then, it is discussed why this type nevertheless holds scientific legitimacy in terms of abduction. Grounded in this legitimacy, the three main stages of SNC, comprising Automatic Keyphrase Extraction (AKE), Edge Weighting (EW), and Community Detection (CD), are reviewed alongside their objectives and operations. In the second fold, evaluation criteria (comprising two established and one reformulated) for achieving the objectives are first defined and justified, followed by illustrative experiments based on the criteria. Thereafter, SNC is reformulated as a Process Optimization Problem (POP), and its global objective function that integrates the local criteria is defined and justified. Based on these, ClueNetwork is ultimately proposed.
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。