Combining LLMs and GNNs to Construct and Analyze Advanced Knowledge Graphs

Large language models (LLMs) have dominated the machine learning landscape since the release of ChatGPT, revolutionizing text generation and reasoning tasks. In parallel, graph neural networks (GNNs) have made significant strides in processing structured data, but they have not gained the same level of public attention. This article explores how to integrate LLMs and GNNs to construct next-generation knowledge graphs, bridging the gap between unstructured text and structured data. Transforming Text into Knowledge Graphs 1. Text to Semantic Graph via LLM The process begins with converting text into semantic graphs. Traditional NLP tools like the Natural Language Toolkit (NLTK) and SpaCy can break down sentences and documents into term dependency graphs. For large volumes of text, advanced LLMs can capture higher-level, non-obvious connections. 2. Custom Prompting and Structured Output To achieve this, we can use custom prompting to instruct LLMs to extract and structure knowledge graph elements. Nodes represent entities (e.g., concepts, people, organizations) and edges represent relationships (e.g., is_subfield_of, works_at). For example, the LLM might generate: Nodes: - Machine learning (Concept) - Artificial intelligence (Concept) - Geoffrey Hinton (Person) Edges: - Machine learning is_subfield_of Artificial intelligence - Geoffrey Hinton works_at Google 3. Utilizing LangChain Tools LangChain offers an experimental feature, LLMGraphTransformer, which simplifies this process. It extracts nodes and edges from textual content, encapsulating them in a GraphDocument. Here’s a basic implementation: ```python from langchain import LLMGraphTransformer from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model_name = "gpt-4.1-mini" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) transformer = LLMGraphTransformer(llm=model, tokenizer=tokenizer) graph_doc = transformer.convert_to_graph_documents([text_input]) ``` Formatting the Knowledge Graph for GNNs 1. Numerical Representations To make the graph compatible with GNNs, nodes and edges must be converted into numerical attributes. One-hot encoding is a simple method, assigning a unique vector to each node: ```python import torch from torch_geometric.data import HeteroData Example nodes and edges nodes = graph_doc[0].nodes edges = graph_doc[0].relationships Create id mappings for each node type node_id_mappings = {} for node in nodes: node_type = node.type.lower() if node_type not in node_id_mappings: node_id_mappings[node_type] = {} node_id_mappings[node_type][node.id] = len(node_id_mappings[node_type]) Create edge_index tensors edge_indices_by_type = {} for edge in edges: source_type = edge.source.type.lower() target_type = edge.target.type.lower() edge_type = edge.type edge_key = (source_type, edge_type, target_type) if edge_key not in edge_indices_by_type: edge_indices_by_type[edge_key] = [] # Get numeric IDs source_id = node_id_mappings[source_type][edge.source.id] target_id = node_id_mappings[target_type][edge.target.id] edge_indices_by_type[edge_key].append([source_id, target_id]) Convert edge lists to tensors for edge_key in edge_indices_by_type: edge_indices_by_type[edge_key] = torch.tensor(edge_indices_by_type[edge_key], dtype=torch.long).t() Create node features using one-hot encoding x_dict = {} for node_type, node_ids in node_id_mappings.items(): num_nodes = len(node_ids) one_hot = torch.eye(num_nodes) x_dict[node_type] = one_hot Create the heterogeneous graph hetero_data = HeteroData() for node_type, features in x_dict.items(): hetero_data[node_type].x = features for (src_type, edge_type, dst_type), edge_index in edge_indices_by_type.items(): hetero_data[src_type, edge_type, dst_type].edge_index = edge_index ``` GNN Training and Evaluation 1. Graph Splitting The next step involves splitting the graph into training, validation, and testing subsets. Negative sampling is used to introduce fake connections, helping the model distinguish true relationships. ```python from torch_geometric.transforms import ToUndirected from torch_geometric.loader import DataLoader from torch_geometric.utils import train_test_split_edges Convert graph to undirected format hetero_g_undir = ToUndirected()(hetero_data) Split graph for link prediction train_g, val_g, test_g = train_test_split_edges(hetero_g_undir, val_ratio=0.1, test_ratio=0.1) ``` 2. Hybrid Network Construction We construct a hybrid model combining a GNN convolution layer (using the GraphSAGE architecture) and a prediction head for binary classification of edges. ```python from torch_geometric.nn import GraphSAGE, to_hetero from torch.nn import Linear, ReLU from torch_geometric.nn import functional as pyg_fn Define the GNN convolution layer sage = GraphSAGE(in_channels=-1, hidden_channels=16, out_channels=16, num_layers=2, aggr="mean") gnn_conv_hetero = to_hetero(sage, hetero_g_undir.metadata()) Define the link prediction model class LinkPredictor(nn.Module): def init(self, gnn_conv, dim_in=8, hidden_dim=8, relation=target_rel): super().init() self.gnn_conv = gnn_conv self.prediction_head = Linear(dim_in * 2, hidden_dim) self.out = Linear(hidden_dim, 1) self.target_rel = relation self.src_node_type = relation[0] self.dst_node_type = relation[2] def forward(self, hetero_g): node_embed = self.gnn_conv(hetero_g.x_dict, hetero_g.edge_index_dict) src_idx = hetero_g[self.target_rel].edge_label_index[0, :] src_embed = node_embed[self.src_node_type][src_idx] dst_idx = hetero_g[self.target_rel].edge_label_index[1, :] dst_embed = node_embed[self.dst_node_type][dst_idx] x = torch.cat([src_embed, dst_embed], dim=1) x = self.prediction_head(x) x = self.out(x) return x Initialize the model model = LinkPredictor(gnn_conv_hetero) ``` 3. Model Training Training the model involves feeding it the graph data and optimizing it to minimize the loss. For small datasets, mini-batching is unnecessary. 4. Embedding Extraction and Visualization Finally, we extract and visualize the node embeddings to gain insights into the learned relationships. Principal component analysis (PCA) is used to reduce dimensionality for plotting. Case Study: Processing Wikipedia Articles 1. Fetch and Merge Articles We fetch and merge Wikipedia articles on machine learning and neural networks using LangChain’s Wikipedia loader. ```python from langchain.document_loaders.web import WikipediaLoader wiki_docs = WikipediaLoader(query="Machine learning", load_max_docs=2, doc_content_chars_max=50_000).load() merged_doc = "\n".join([doc.page_content for doc in wiki_docs]) ``` 2. Extract Knowledge Graph Elements Using the LLMGraphTransformer, we convert the merged text into a structured knowledge graph. python graph_doc = transformer.convert_to_graph_documents([merged_doc]) 3. Format for GNN We format the graph data for GNN using the methods described earlier. 4. Train and Evaluate the Model The trained model successfully distinguishes between real and negative connections, as shown by the loss diagrams and classification reports. Embedding visualizations reveal clear clusters, such as classic ML techniques on one side and deep learning concepts on the other. Industry Insights and Future Prospects Combining LLMs and GNNs opens new avenues for AI, potentially reducing hallucinations and improving reasoning. Research indicates that this hybrid approach can enhance the expressiveness and robustness of foundation models (Galkin et al., 2023; Huang et al., 2025). Company Profiles Scale AI: Specializes in data labeling and annotation services, crucial for training large language models and other AI systems. The company has recently received a significant investment from Meta, valuing it at $29 billion. Meta: A technology giant investing heavily in AI, particularly in superintelligent systems. The acquisition of a 49% stake in Scale AI demonstrates Meta's commitment to staying competitive in the AI landscape. LangChain: An open-source platform that simplifies the integration of LLMs into various applications, including knowledge graph construction. LangChain’s experimental LLMGraphTransformer is a valuable tool for this process. Neo4j: A popular graph database platform that enables efficient querying and pattern discovery in knowledge graphs. It supports the transition from unstructured text to structured graph data. Conclusion This exercise demonstrates a method to integrate LLMs and GNNs for constructing and training structure-aware knowledge graphs. Despite the complexity, tools like LangChain and HeXtractor simplify the process, making it accessible to researchers and developers. The potential benefits of this approach, such as improved reasoning and reduced hallucinations, are significant and warrant further exploration.