# Evaluating Complex Entity Knowledge Propagation for Knowledge Editing in LLMs
## 2 Related Works
### Knowledge Graphs and LLMs  
KGs store structural knowledge, playing an essential role in many real-word applications [42]. For KG-related tasks, LLMs can be of great importance given their generalizability. There are various ways in which KGs and LLMs can be fused based on their application. The most straightforward way is to use LLMs as text encoders for KG-relevant tasks [43]. To retrieve entities and relations from KGs [44], LLMs are used to process the original data. For LLMs to comprehend KGs, some studies have designed a prompting method based on KGs that can effectively convert these comprehensive structural data for LLMs [45]. This makes it easier for LLMs to be directly applied to KG-related tasks such as KG reasoning or completion tasks. Another approach is to use KG embedding-based approaches, which primarily focus on generating embeddings from structured knowledge. TransE [46], TransR [47], and TransH [48] are translation-based paradigms utilized by most common KG embedding methods. Besides this, graph convolutional neural networks have gained massive interest in processing structural information [43; 49; 50]. Pre-trained LLMs have gaining massive interest over time [51] for various tasks. The study conducted by [52] discusses enhancing chatbots' capabilities to explain learning recommendations to students by integrating LLMs with KGs. The proposed approach uses chatbots as intermediaries, leveraging the structured information from KGs to guide the LLM's output, thus ensuring more accurate and relevant explanations. This combination aims to simulate a mentor-like interaction, aiding students in understanding the rationale behind the learning paths suggested to them. Although not replacing human mentors, this system connects students with human guidance when necessary, demonstrating the potential of chatbots in educational settings. The effectiveness of this approach was assessed through a user study, highlighting its possibilities and identifying areas for improvement. A Multi-Modal Process Knowledge Graph (MPKG-WT) is introduced specifically for wind turbines,aiming to consolidate assembly process information from various sources like 3D models, text, and images. This KG, in combination with LLMs, forms the basis of a question-answering system designed to efficiently utilize historical assembly process knowledge. The proposed system demonstrates superior performance over existing Knowledge Base Question Answering (KBQA) methods and LLMs in the context of wind turbine assembly, leading to notable improvements in assembly process design efficiency [53]. Another study details the creation of a KG from unstructured open-source threat intelligence using a large language model (LLM-TIKG) to overcome limitations in current KG construction methods, particularly in handling domain-specific entities and lengthy texts without requiring extensive labeled data [54]. By leveraging GPT's few-shot learning for data annotation and augmentation, fine-tuning a smaller model for topic classification, entity and relationship extraction, and TTP identification, the approach significantly enhances the automated analysis of cyber threats. The resulting threat intelligence knowledge graph demonstrates notable improvements in named entity recognition and TTP classification accuracy. Nonetheless, KG embeddings in previous studies are usually deployed as static artifacts whose behavior is challenging to modify after deployment. To this end, we propose a new task of representing KG-based data as a comprehensive representation of knowledge for KE methods regarding the correction or changes for facts of existing KGs. Embeddings provide a powerful but static representation of knowledge as they are pre-computed, dense representations of knowledge, which is efficient for models to process but requires updates to the embeddings themselves for the model to learn new information. KG-augmented LLMs allow for more dynamic updates and adaptability but might require more computational effort from the LLM to integrate and learn from this data.