The proposed TKGCon framework for constructing theme-specific knowledge graphs may face several challenges and limitations when dealing with highly specialized or rapidly evolving themes:
Limited Training Data: Highly specialized themes may have limited training data available, making it challenging to extract a comprehensive set of entities and relations for the knowledge graph construction.
Ambiguity and Noise: Rapidly evolving themes may introduce ambiguity and noise in the extracted entities and relations, leading to inaccuracies in the constructed knowledge graph.
Dynamic Nature of Themes: Themes that evolve quickly may require frequent updates to the knowledge graph to ensure its relevance and accuracy, posing a challenge in maintaining the timeliness of the information.
Hallucinations and Errors: In highly specialized themes, large language models may still generate hallucinations or errors when prompted with theme-specific queries, impacting the quality of the constructed knowledge graph.
Scalability: Constructing theme-specific knowledge graphs for a wide range of highly specialized themes may require significant computational resources and expertise, limiting the scalability of the framework.
Ontology Construction: Building accurate and comprehensive theme ontologies from existing sources like Wikipedia may be challenging, especially for niche or emerging themes.
Addressing these challenges and limitations would be crucial for the successful construction of theme-specific knowledge graphs for highly specialized or rapidly evolving themes.