Semantic Networks as Clues: A Theoretical Foundation and Process Optimization for Semantic Network Construction

2026-08-03

AI Summary

This study addresses the lack of a unified evaluation framework for semantic network construction and the difficulty in validating such networks as genuine abductive "clues" rather than mere substitutes for reality. Positioning semantic networks explicitly as clues within abductive reasoning, this work reformulates evaluation criteria accordingly and formalizes the construction process as a global objective-driven optimization problem that integrates automatic keyword extraction, edge weight computation, and community detection. The proposed ClueNetwork framework enables systematic ranking and refinement of candidate networks generated by diverse construction methods. Experimental results demonstrate the validity of the new evaluation criteria, underscoring the theoretical novelty and practical utility of this approach.

Abstract

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.

Problem

• Semantic Networks
• Non-propositional Knowledge
• Process Optimization
• Clue-based Representation
• Network Construction

Innovation

• Semantic Network Construction
• Process Optimization Problem
• Abduction
• ClueNetwork
• Non-propositional Knowledge