KBGAT (Knowledge-based GAT) overview and implementation examples

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KBGAT (Knowledge-based GAT)

KBGAT (Knowledge-based Graph Attention Network) is a type of graph neural network (GNN) specifically designed to deal with knowledge graphs (Knowledge Graphs), especially for efficiently modelling the relationships between nodes of a knowledge graph. It is a method.

KBGAT is based on the traditional Graph Attention Network (GAT) and is designed to utilise the special structure of knowledge graphs, with the following characteristics

Exploitation of Relational Information: in a knowledge graph, labelled edges (relations) exist between nodes; KBGAT integrates this relational information into the Attention Mechanism and learns different weights for different types of edges.
Adaptation of the Attention Mechanism: applying the GAT mechanism, the importance between nodes is calculated dynamically according to the relationship.
Improved inference performance: by considering not just structural information but also relational data when computing node embeddings, the performance of the knowledge graph completion and link prediction tasks is improved.

The architecture of KBGAT has the following flow.

1. input data
– Node features (entity information)
– Edge information (relation types)

2. computation of an attention score per relation: for each relation type ( r ), an edge-based attention score is computed. Specifically, the importance of neighbouring nodes is calculated dynamically using the learnable weights for each relation type.

\[
\alpha_{ij} = \text{softmax} \left( f(h_i, h_j, r_{ij}) \right)
\] – \( h_i, h_j \): feature vector of a node
– \( r_{ij} \): relation type

3. feature aggregation: weighted average of neighbouring node features based on attention scores to generate new node features.

\[
h_i’ = \sigma \left( \sum_{j \in \mathcal{N}(i)} \alpha_{ij} \cdot W_r h_j \right)
\] – \( \mathcal{N}(i) \): Set of neighbouring nodes of node \(i \)
– \( W_r \): Learnable weights per relation.

4. outputs: node embedding and relationship prediction scores are generated and utilised according to the task.

Implementation example

Below is a simple example of a KBGAT (Knowledge-based Graph Attention Network) implementation using PyTorch and PyTorch Geometric. This example is intended for knowledge graph completion (link prediction).

1. install the necessary libraries: install the necessary libraries using the following commands.

pip install torch torch-geometric

2. implementation of KBGAT

(1) Implementation of the model: the following code implements the core part of KBGAT.

import torch
import torch.nn as nn
from torch_geometric.nn import GATConv

class KBGAT(torch.nn.Module):
    def __init__(self, in_channels, out_channels, num_relations, num_heads=1):
        super(KBGAT, self).__init__()
        self.num_relations = num_relations
        self.gat_layers = nn.ModuleList([
            GATConv(in_channels, out_channels, heads=num_heads, concat=True)
            for _ in range(num_relations)
        ])
        self.final_layer = nn.Linear(out_channels * num_heads, out_channels)

    def forward(self, x, edge_index, edge_type):
        # Aggregate information for each relation type
        out = torch.zeros_like(x)
        for rel_type in range(self.num_relations):
            mask = edge_type == rel_type
            rel_edge_index = edge_index[:, mask]
            if rel_edge_index.size(1) > 0:  # Avoid empty edge cases
                out += self.gat_layers[rel_type](x, rel_edge_index)

        # Final projection
        out = self.final_layer(out)
        return out

(2) Data preparation: prepare the data (node features, edge lists, relation types) representing the knowledge graph.

from torch_geometric.data import Data

# Node feature vector (randomly initialised)
num_nodes = 10
in_channels = 16
x = torch.rand((num_nodes, in_channels))

# Edge list (defines starting and ending points of edges)
edge_index = torch.tensor([
    [0, 1, 2, 3, 4, 5, 6],
    [1, 2, 3, 4, 5, 6, 0]
], dtype=torch.long)  # (2, num_edges)

# Edge relationship type (0, 1 or 2)
edge_type = torch.tensor([0, 1, 2, 0, 1, 2, 0], dtype=torch.long)

# Summarise in PyTorch Geometric data format.
data = Data(x=x, edge_index=edge_index, edge_attr=edge_type)

(3) Model training and inference: initialise the model and set up the loss function and optimiser.

# hyperparameter
out_channels = 32
num_relations = 3
num_heads = 2
epochs = 50
learning_rate = 0.01

# Initialisation of the model
model = KBGAT(in_channels, out_channels, num_relations, num_heads)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.MSELoss()

# Label (e.g. random target value)
labels = torch.rand((num_nodes, out_channels))

# training loop
for epoch in range(epochs):
    model.train()
    optimizer.zero_grad()
    
    # forward propagation
    output = model(data.x, data.edge_index, data.edge_attr)
    
    # Loss Calculation
    loss = criterion(output, labels)
    loss.backward()
    optimizer.step()
    
    print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item()}")

3. confirmation: after training, use the model to obtain node embeddings or calculate edge scores.

# inductive mode
model.eval()
with torch.no_grad():
    embeddings = model(data.x, data.edge_index, data.edge_attr)
    print("Node embeddings:")
    print(embeddings)

4. applications: KBGAT can be further developed and applied to the following tasks

Link prediction.
- Calculating edge scores using embeddings to complement existing edges.
- Example: compute edge scores using inner products or bilinear functions.
Entity classification
- Predicting the class labels of nodes using node embeddings.
Visualisation of node embedding.
- Visualise the distribution of nodes by projecting the embedding to a lower dimension using t-SNE or UMAP.

5. suggested improvements: the model can be further advanced by adding the following elements

Integration of relational embeddings: learn embeddings per relation and utilise them for attention score calculation.
Introduction of multilayers: learn deeper feature representations by stacking multiple GAT layers.
Edge weighting: add scoring based on edge weights.

Specific application examples

KBGAT (Knowledge-based Graph Attention Network) has been applied as a highly expressive model in tasks that utilise knowledge graphs. Specific applications are described below.

1. knowledge graph completion (link prediction)

Abstract: Predicts relationships (edges) between missing entities in the knowledge graph.
Application examples
- Medical field: disease, symptoms and treatment are nodes and their relationships (cause, treatment, relevance) are predicted. Example: identifying unregistered treatments related to the disease ‘influenza’.
- Recommendation system: recommends unpurchased items based on purchase history and categories of interest, with user, item and attribute as nodes. Example: predicts which products User A might be interested in.

2 Entity classification

Abstract: classifies nodes (entities) in the knowledge graph into specific categories.
Example applications.
- Data classification in academic fields: papers are classified into research fields (e.g. AI, physics, medicine, etc.) using papers, authors and keywords as nodes. Example: estimate whether an unclassified article belongs to the ‘machine learning’ field.
- Analysing social networking data: predict categories of interests and activities (e.g. sports, music, movies, etc.), with users as nodes. Example: classifying which interest categories SNS users belong to.

3. knowledge graph-based QA systems

Abstract: Question and answer systems use knowledge graphs and utilise the relationships between nodes to generate accurate responses.
Applications.
- FAQ response: products, functions and problems are used as nodes, and the user’s question is answered precisely based on the edges. Example: ‘How long is the battery life of Smartphone X?’ generates a response to the question ‘What is the battery life of smartphone X?’.
- Question answering in the medical field: answers patients’ questions by exploiting the relationship between diseases, treatments and symptoms. e.g. ‘What is the best medicine for the common cold?’ with a recommended medicine.

4. natural language processing (NLP) using knowledge graphs

Abstract: Using knowledge graphs in natural language processing tasks, taking into account contextual information and relationships between entities.
Examples of applications.
- Information extraction: entities extracted from text (e.g. names of people, places, organisations) are mapped onto a knowledge graph to discover new relations. Example: discovering new partnerships between companies from news articles.
- Document summarisation: generate knowledge graphs from longer texts and create summaries based on important entities and their relationships. Example: summarising meeting minutes to extract key agendas and decisions.

5. medical data analysis

Abstract: analyses relationships between patient data, diseases, treatments and drugs to discover new treatments and disease correlations.
Applications
- Drug discovery support: discover new drug candidates by using proteins, drugs and diseases as nodes. Example: prediction of potential drugs for unknown diseases.
- Pattern analysis of patient data: predict diseases and suggest treatments based on patient symptoms and diagnostic data. Example: systems to aid diagnosis of rare diseases.

6. cyber security

Abstract: Modelling the relationship between attackers, attack patterns and vulnerabilities using knowledge graphs.
Applications.
- Threat detection: represent attack patterns on knowledge graphs and detect new attack scenarios. Example: detection of unknown phishing attacks.
- Vulnerability management: represent system configuration and vulnerability data as a knowledge graph and prioritise fixes. Example: identify vulnerabilities that urgently need to be fixed.

7. utilisation of multimodal knowledge graphs

Abstract: integrates and analyses different forms of data, e.g. images, text, sensor data, etc., into a knowledge graph.
Application examples
- Anomaly detection in the manufacturing industry: integrating sensor data and maintenance records from a factory into a knowledge graph to predict anomaly patterns. Example: proactive detection of production line stoppage risks.
- Automated driving: safe operation planning by modelling the relationships between road signs, vehicles and obstacles in a knowledge graph. Example: suggests safe paths at complex intersections.

reference book

Reference books on the application of KBGAT (Knowledge-based Graph Attention Network), knowledge graphs and graph neural networks (GNN) are listed below.

1. reference books on knowledge graphs
– ‘Knowledge Graphs: Fundamentals, Techniques, and Applications’.
– Authors: Mayank Kejriwal, Craig A. Knoblock, Pedro Szekely
– Abstract: This book provides a broad overview of knowledge graphs, from basic theory to applications.

– ‘Differentiable Reasoning about Knowledge Graphs with Region-based Graph Neural Networks’.

2. reference books on graph neural networks (GNNs)
– ‘Graph Representation Learning’.
– Author: William L. Hamilton
– Abstract: Systematically explains the fundamentals of Graph Representation Learning (GNN).

– ‘Deep Learning on Graphs’.
– Authors: Yao Ma, Jiliang Tang
– Abstract: Fundamentals and applications of deep learning techniques on graphs.
– In particular, there are extensive chapters on implementation examples such as GAT (Graph Attention Network).

3. natural language processing (NLP) and knowledge graphs
– “An Introduction to Knowledge Graphs”

– ‘Deep Learning for Natural Language Processing’.
– Authors: Palash Goyal, Sumit Pandey, Karan Jain
– Abstract: Addresses the role of GNNs and knowledge graphs in NLP tasks.

4. practical resources.
– ‘Hands-On Graph Neural Networks with Python’.
– Authors: Max Pumperla, Michael Sigirci
– Abstract: Hands-on with practical Graph Neural Network (GNN) implementation examples.
– Libraries used: e.g. PyTorch Geometric, DGL.

– “Deep Learning For Knowledge Graph Completion With XLNET”

5. articles and online resources
– ‘Attention is All You Need’.
– Author(s): Vaswani et al.
– Abstract: Important as the basis for Transformers and as the basis for GAT and KBGAT.

– ‘KGAT: Knowledge Graph Attention Network for Recommendation’ byWang et al.
– An application example of GAT with a special focus on knowledge graph-based recommendation systems.

– ‘Modelling Relational Data with Graph Convolutional Networks’ by Schlichtkrull et al.
– This paper on R-GCNs (Relational GCNs) helps to understand the basic concepts of KBGAT.

– Official website of the library.
– [PyTorch Geometric] – [DGL (Deep Graph Library)]