GNN for Knowledge Graph Reasoning

GNN Development for Knowledge Graph Reasoning

A Knowledge Graph (KG) is a graph of entities and relationships: (Company A) → [owns] → (Company B), (Drug X) → [treats] → (Disease Y). Standard ML methods work with tabular data and cannot exploit graph structure. Graph Neural Networks (GNN) solve KG tasks: predicting missing links, classifying nodes, inferring new facts—tasks that would otherwise require manual rules or SPARQL queries in classical approaches.

Types of Knowledge Graph Tasks

Link Prediction — the most common task. Given: (Protein A) → [interacts with] → (?). Predict which other proteins interact with A. Applications: drug discovery, recommendation systems, fraud detection (who is connected to a fraudster?).

Entity Classification — classifying nodes based on their connections in the graph. Example: determining the type of legal entity (individual / company / sole proprietor) based on financial transaction patterns.

Reasoning / Multi-hop Inference — inference through a chain: (A works in B) + (B is a subsidiary of C) → infer that A is indirectly connected to C. Used in compliance systems and knowledge base completion.

Architecture of GNN for KG Reasoning

For link prediction we use R-GCN (Relational GCN) — an extension of Graph Convolutional Network for graphs with typed edges:

import torch
import torch.nn as nn
from torch_geometric.nn import RGCNConv

class KnowledgeGraphRGCN(nn.Module):
    def __init__(self, num_entities: int, num_relations: int,
                 embedding_dim: int = 200, num_layers: int = 3):
        super().__init__()
        self.entity_emb = nn.Embedding(num_entities, embedding_dim)
        self.convs = nn.ModuleList([
            RGCNConv(embedding_dim, embedding_dim, num_relations)
            for _ in range(num_layers)
        ])
        self.dropout = nn.Dropout(0.2)

    def forward(self, edge_index, edge_type):
        x = self.entity_emb.weight
        for conv in self.convs:
            x = torch.relu(conv(x, edge_index, edge_type))
            x = self.dropout(x)
        return x

    def score_triple(self, head_emb, tail_emb, relation_id):
        # DistMult scoring function
        rel = self.relation_emb(relation_id)
        return (head_emb * rel * tail_emb).sum(dim=-1)

For more complex multi-hop reasoning chains we use CompGCN or NBFNet (Neural Bellman-Ford Networks) — the latter shows superior performance on FB15k-237 and WN18RR benchmarks.

Scalability: Working with Large Graphs

Real-scale KGs: Wikidata contains 100M+ nodes, 1B+ edges. Full GNN training on such a graph is impossible in naive mode. We apply:

• Mini-batch sampling: GraphSAGE-style neighborhood sampling — each mini-batch contains k-hop neighborhoods of selected nodes
• Negative sampling: for link prediction training we need negative examples; we use self-adversarial negative sampling from RotatE
• Mixed CPU/GPU training: storing embeddings on CPU, computations on GPU via PyG + DGL

# Example with DGL for scalable training
from dgl.dataloading import MultiLayerNeighborSampler, EdgeDataLoader

sampler = MultiLayerNeighborSampler([15, 10, 5])  # fanout per layer
dataloader = EdgeDataLoader(
    graph, train_eids,
    sampler,
    batch_size=1024,
    shuffle=True,
    num_workers=4
)

Applications in Real Domains

Biomedicine — predicting drug-target interactions. Graph: proteins, genes, diseases, drugs, side effects. MRR (Mean Reciprocal Rank) on DRKG: 0.32–0.38 for R-GCN vs 0.41–0.47 for NBFNet.

Financial Systems — graph of transactions, companies, directors, addresses. Task: detecting hidden links for AML compliance. F1 on detecting suspicious connections: 0.78–0.84.

E-commerce — KG of products, categories, attributes, brands. Link prediction → item-to-item recommendations. NDCG@10 exceeds collaborative filtering baseline by 8–12%.

Building KG from Unstructured Data

If the client doesn't have a ready-made KG, the first stage is its construction: NER (Named Entity Recognition) for entity extraction from texts, RE (Relation Extraction) for relationship extraction. We use SpanBERT or REBEL (a model combining NER and RE in one pass).

Development Stages

Data analysis: structure, size, quality of existing graph or sources for its construction. Selecting GNN architecture for the task. Building or cleaning KG, normalizing entities (entity linking). Model training, hyperparameter tuning, evaluation on held-out test set. Developing API for inference, integration into product.