HTEKG: A Human-Trait-Enhanced Literary Knowledge Graph with language model evaluation

Abstract

Knowledge Graphs (KGs) are a crucial component of Artificial Intelligence (AI) systems, enhancing AI’s capabilities in literary analysis. However, traditional KG designs in this field have focused more on events, often ignoring character information. To tackle this issue, we created a comprehensive Human-Trait-Enhanced Knowledge Graph, HTEKG, which combines past event-centered KGs with general human traits. The HTEKG enhances query capabilities by mapping the complex relationships and traits of literary characters, thereby providing more accurate and context-relevant information. We tested our HTEKG on three typical literary comprehension methods: traditional Cypher query, integration with a BERT classifier, and integration with GPT-4, demonstrating its effectiveness in literary analysis and its adaptability to different language models.