发表机构
Soongsil University（崇实大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对一类语义网络，梳理其理论基础，提出ClueNetwork框架，将语义网络构建重构为流程优化问题并定义全局目标函数，通过实验验证相关评估准则。
AI 中文摘要
本文研究内容分为两部分：其一，综述一类表示文本非命题知识的语义网络（Semantic Networks, SNs）的理论基础，明确该类语义网络是现实的线索而非替代物，因此难以获取黄金标准，并从溯因推理角度论证其科学合法性；其二，提出用于对该类语义网络构建（Semantic Network Construction, SNC）流程生成的候选语义网络进行排序的框架（ClueNetwork）。基于上述合法性，综述语义网络构建的三个主要阶段：自动关键词提取（Automatic Keyphrase Extraction, AKE）、边加权（Edge Weighting, EW）与社区检测（Community Detection, CD），及其目标与操作。第二部分首先定义并论证实现各阶段目标的评估准则（含两项现有准则与一项重构准则），并基于该准则开展示例实验；随后将语义网络构建重构为流程优化问题（Process Optimization Problem, POP），定义并论证整合局部准则的全局目标函数，最终提出ClueNetwork框架。
英文摘要
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.
