A GRAPH FRAMEWORK FOR METAPHOR CREATION INTEGRATING KNOWLEDGE GRAPH CONSTRUCTION AND GENERATION

Abstract

Metaphorical language enables humans to express abstract concepts through concrete experiences, making it an essential facet of natural language. Despite its prevalence, computational understanding and generation of metaphor remain fundamentally challenging due to their conceptual complexity and semantic ambiguity. This study presents a novel large-scale metaphor Knowledge Graph (KG) designed to explicitly encode the structure of metaphorical expressions and enable their computational interpretation and generation. The proposed approach integrates multiple layers of semantic information, such as frames, roles, domains, concepts, lexical units, and paraphrases, into a unified KG framework. The construction pipeline employs FrameNet, semantic role labeling (SRL), and Abstract Meaning Representation (AMR) parsing to extract conceptual and relational information from a curated corpus of metaphoric and literal sentence pairs. The resulting KG comprises over 108,000 triples, 12 node types and 13,544 role mappings. Intrinsic evaluations reveal high coverage and consistency in capturing metaphorical structures, while extrinsic evaluations demonstrate the effectiveness of KG in improving metaphor generation tasks. When used as a guide for metaphor generation, the KG improves performance across standard evaluation metrics and receives superior human ratings for fluency, creativity, and Faithfulness compared to baseline neural systems. These results indicate that a structurally grounded representation of metaphor, encoded as a knowledge graph, provides a scalable and interpretable pathway toward metaphor-aware natural language understanding and generation.