Knowledge representation, machine learning, inference and GNN

Knowledge representation, reasoning and GNN

Knowledge representation, as described in ‘Knowledge Information Processing Techniques’, and inference, as described in ‘Inference Techniques’, are important areas for structuring information and facilitating semantic understanding, whereas machine learning methods dedicated to the processing of graph-structured data, as described in ‘Graph Neural Networks’ The application of graph neural networks (GNNs) is one that allows for a more efficient and effective approach to the tasks of knowledge representation and inference. They are described below.

1. knowledge representation: knowledge representation is the process of formalising information and knowledge so that it can be understood by computers, and graph structures are widely used to represent relationships between real-world objects and concepts.

• Characteristics of knowledge representation using GNNs: GNNs learn to represent knowledge as nodes (entities) and edges (relations). This allows complex relationships to be handled naturally, e.g. users, products and services can be represented as nodes and relationships such as purchase, evaluation and recommendation can be represented as edges. Furthermore, GNNs are suitable for integrating knowledge from different sources, e.g. linking different data types, such as text, images, sensor data, etc., to nodes to build an overall knowledge graph.

2. inference: inference is the process of deriving new knowledge and conclusions from given knowledge, and GNNs are a particularly effective approach for reasoning on knowledge graphs.

• Features of inference using GNNs: a central feature of GNNs is a message-passing mechanism that transmits information between nodes, enabling them to aggregate information from surrounding nodes, update their features and develop a semantic understanding of them. For example, information about other entities related to an entity can be gathered to infer relationships and attributes. GNNs can also learn the attributes of edges and relationships between nodes, which enables them to infer relationships between unknown entities and find associations.

Specific applications of knowledge representation and inference using GNNs include

• Enhancing knowledge graphs: when adding new nodes or edges to an existing knowledge graph, GNNs can be used to predict undetermined relationships and extend the graph. This enables a richer knowledge base to be built.
• Question answering systems: question answering systems based on knowledge graphs use GNNs to search for relevant entities and relations for a user’s question and derive an accurate answer. For example, nodes related to the question can be identified and inferences can be made based on them.
• Recommendation systems: systems can be built using GNNs to recommend relevant items based on the user’s preferences and behaviours; the relationships between users and items can be represented as a graph and new recommendations can be generated using GNNs.
• Medical diagnosis: a knowledge graph of medical data can be constructed, and GNNs can be used to infer appropriate treatments and related diseases based on patient symptoms and diagnoses.

Advantages and challenges of GNNs include.

• Advantages
  – Relational understanding: GNNs have an excellent ability to learn complex relationships, making them highly suitable for knowledge graph inference tasks.
  – Scalability: GNNs can be applied to large datasets and graphs, enabling efficient learning and inference.
• Challenges
  – Data imbalance: an imbalanced distribution of nodes and edges in a knowledge graph can affect the performance of GNNs.
  – Interpretability: the internal mechanisms of GNNs are complex and results can be difficult to interpret, making it important to ensure transparency of inference results.

Graph neural networks (GNNs) are very powerful tools in knowledge representation and inference, representing knowledge through nodes and edges and inference through message passing, enabling them to understand complex relationships and derive new knowledge. This has potential applications in various fields, such as knowledge graph enhancement, question answering, recommendation and medical diagnosis.

implementation example

Consider an example implementation that combines Knowledge Representation and Reasoning (KRR) and Graph Neural Networks (GNN). Specifically, the following frameworks can be used.

A basic example implementation using Python and key libraries is given below.

Overview of knowledge representation and reasoning

• Knowledge representation: knowledge is represented as a graph structure (nodes and edges). Nodes represent entities (e.g. people, places, concepts, etc.) and edges represent relations (e.g. ‘parents’, ‘friends’, ‘lives’, etc.).
• Inference: the process of deriving new knowledge from existing knowledge.

Example of use: consider the task of predicting relationships between entities (link prediction) using a knowledge graph.

Libraries required:

• NetworkX: knowledge graph creation.
• PyTorch Geometric (PyG): implementation of GNN.
• Scikit-learn: for evaluation.

import networkx as nx
import torch
from torch_geometric.data import Data
from torch_geometric.nn import GCNConv
import torch.nn.functional as F
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 1. knowledge graph creation
def create_knowledge_graph():
    G = nx.Graph()
    # Adding nodes (entities)
    G.add_nodes_from([
        (0, {"name": "Alice", "type": "Person"}),
        (1, {"name": "Bob", "type": "Person"}),
        (2, {"name": "Charlie", "type": "Person"}),
        (3, {"name": "Paris", "type": "City"}),
        (4, {"name": "London", "type": "City"}),
    ])
    # Adding edges (relationship)
    G.add_edges_from([
        (0, 1, {"relation": "friend"}),
        (0, 3, {"relation": "lives_in"}),
        (1, 4, {"relation": "lives_in"}),
        (2, 3, {"relation": "lives_in"}),
    ])
    return G

# 2. preparation of graph features and edge lists
def preprocess_graph(G):
    # Node features (simple type encoding)
    node_features = []
    for _, data in G.nodes(data=True):
        if data["type"] == "Person":
            node_features.append([1, 0])  # Encoding people as [1, 0].
        else:
            node_features.append([0, 1])  # Encoding cities as [0, 1].
    x = torch.tensor(node_features, dtype=torch.float)
    
    # Edge lists and edge attributes (encoding)
    edge_index = torch.tensor(list(G.edges), dtype=torch.long).t().contiguous()
    edge_attr = torch.tensor([0 if d["relation"] == "friend" else 1 for _, _, d in G.edges(data=True)])
    return Data(x=x, edge_index=edge_index, edge_attr=edge_attr)

# 3. definition of the GNN model
class GCN(torch.nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(GCN, self).__init__()
        self.conv1 = GCNConv(input_dim, hidden_dim)
        self.conv2 = GCNConv(hidden_dim, output_dim)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = self.conv2(x, edge_index)
        return F.log_softmax(x, dim=1)

# 4. data preparation
G = create_knowledge_graph()
data = preprocess_graph(G)

# Labels (for link prediction)
labels = torch.tensor([1 if d["relation"] == "friend" else 0 for _, _, d in G.edges(data=True)])
train_idx, test_idx = train_test_split(range(len(labels)), test_size=0.2, random_state=42)

# 5. learning and assessment
model = GCN(input_dim=2, hidden_dim=4, output_dim=2)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

for epoch in range(100):
    model.train()
    optimizer.zero_grad()
    out = model(data)
    loss = F.nll_loss(out[train_idx], labels[train_idx])
    loss.backward()
    optimizer.step()

    if epoch % 10 == 0:
        print(f'Epoch {epoch}, Loss: {loss.item()}')

# test
model.eval()
pred = model(data).argmax(dim=1)
accuracy = accuracy_score(labels[test_idx].numpy(), pred[test_idx].numpy())
print(f'Test Accuracy: {accuracy:.2f}')

Description.

1. Knowledge graph preparation: create simple knowledge graphs in NetworkX.
2. Feature preparation: assign features to nodes and encode edge relationships.
3. GNN models: learn representations of nodes using Graph Convolutional Networks (GCN).
4. Task: link prediction to predict edge relationships (friends or not).

Applications.

• Node classification: inferring node types.
• Graph classification: characterise multiple graphs.
• Edge prediction: predict new edges (relationships).

Specific application examples

Specific examples of knowledge representation and reasoning and the use of graph neural networks (GNNs) are given below.

1. knowledge graph completion

• Application examples
  – Link prediction: estimating missing relations (edges) in the knowledge graph.
  – Example: predicting ‘the city where Einstein lived’ in a Wikipedia-based knowledge graph.
  – Role of GNNs: encode node features (attributes of entities) and estimate possible edges (relations).
  – Model: e.g. R-GCN (Relational Graph Convolutional Network) as described in ‘Overview of R-GCN and examples of algorithms and implementations’.
  Examples: complementing missing edges in knowledge graph datasets such as Freebase and DBpedia.
  Research paper: Schlichtkrull et al, ‘Modelling Relational Data with Graph Convolutional Networks’ (R-GCN).

2. the medical sector: disease prediction and drug repurposing

• Application examples.
  – Disease-drug relationship inference: using knowledge graphs to discover new associations between drugs and diseases.
  – Example: Inference of new drug candidates for the disease ‘diabetes’.
  – Role of GNNs: represent medical data (drugs, diseases, genes) as graphs and model interactions between nodes.
  – Models: GraphSAGE as described in ‘GraphSAGE overview, algorithms and implementation examples’, GAT (Graph Attention Network) as described in ‘GAT (Graph Attention Network) overview, algorithms and implementation examples’. Examples include.
  – Datasets: DrugBank (drug-target information), DisGeNET (disease-gene relationships).
  Examples of results
  – Reduction of new drug development costs.
  – Estimation of disease causation based on gene-gene interaction networks.

3. natural language processing (NLP): knowledge-based QA systems

• Application examples.
  – Question answering systems: generate appropriate answers to user questions using knowledge graphs.
  – Example: ‘How tall is the Eiffel Tower?’ → Extract data from the knowledge graph.
  – Role of GNNs: learn node representations and infer semantic relations between entities.
  – Models: GCN as described in ‘Overview of Graph Convolutional Neural Networks (GCN), algorithms and implementation examples’, and KBGAT (Knowledge-based GAT) as described in ‘Overview of KBGAT and implementation examples’.
  – Datasets: WebQuestions (question-answer pair dataset), WikiData (knowledge base).
  Example outcome.
  – Improved advanced QA capabilities of smart speakers.
  – Enhanced QA performance in specific fields (e.g. legal, medical).

4. cyber security: detection of malicious activities.

• Application examples
  – Malware detection: anomalies are detected by representing relationships between files and communication logs as a knowledge graph.
  – Example: infer suspicious malware from file dependencies.
  – Role of GNNs: score the anomaly degree of a node in an anomaly detection task.
  – Models: Deep Graph Infomax, described in ‘Overview and implementation examples of Deep Graph Infomax’; Variational Graph Auto-Encoders (VGAE), described in ‘Overview, algorithms and implementation examples of GraphAutoEncoder.
  – Dataset: network traffic logs.
  Example results.
  – Early detection of malware.
  – Detection of advanced attack techniques (e.g. APT attacks).

5. automated driving: situation awareness with knowledge graphs

• Application examples.
  – Road situation understanding and reasoning: vehicles, traffic lights, pedestrians, etc. are represented as nodes and relationships as edges.
  – Example: violation detection when a vehicle crosses a red light stop line.
  – Role of GNNs: analysing dynamic graphs (real-time road conditions).
  – Model: Temporal Graph Neural Network as described in ‘Temporal Graph Neural Network overview and implementation examples’.
  – Dataset: real-time data from sensors.
  Example results.
  – Improved decision-making performance of self-driving cars.
  – Real-time accident avoidance behaviour.

6. supply chain management

• Application examples
  – Demand forecasting and optimisation: representation of factories, warehouses and logistics networks as knowledge graphs.
  – Example: optimising the relationship between product demand and inventory.
  – Role of GNNs: demand forecasting considering supply network relationships.
  – Model: GCN or Edge-GNN as described in ‘Overview and implementation examples of Edge-GNN’.
  Examples of results
  – Reduction of stock management costs.
  – Improved delivery efficiency.

reference book

Reference books on knowledge representation, inference and graph neural networks (GNNs) are summarised below.

1. knowledge representation and reasoning
Basic books.
1. ‘Knowledge Representation and Reasoning’
– Author(s): Ronald Brachman, Hector Levesque
– Contents: basic concepts of knowledge representation, logic-based reasoning methods, frame theory, etc. Suitable for AI theoretical background.

2. ‘Artificial Intelligence: A Guide to Intelligent Systems’.
– Author: Michael Negnevitsky
– Description: a practical textbook that teaches the fundamentals of knowledge representation and reasoning, along with general AI concepts.

Applied books.
3. ‘Ontology Engineering’
– Author(s): Asunción Gómez-Pérez, Mariano Fernández-López, Oscar Corcho
– Description: an applied perspective on knowledge representation and reasoning, with a focus on ontology construction.

4. ‘Semantic Web for the Working Ontologist’.
– Author(s): Dean Allemang, James Hendler
– Description: a book for learning Semantic Web technologies such as knowledge graphs and RDF/OWL.

2. graph neural networks (GNNs)
Basic books.
1. ‘Graph Representation Learning’
– Author: William L. Hamilton
– Description: a clear explanation of the fundamentals and applications of GNNs. Suitable for beginners to intermediate users.

2. ‘Deep Learning on Graphs’.
– Author(s): Yao Ma, Jiliang Tang
– Description: Covers GNN algorithms and applications. Also includes a wealth of code examples.

Applied books.
3. ‘Graphs Theory and Applications: With Exercises and Problems’

4. ‘Graph Neural Networks: Foundations, Frontiers, and Applications’.
– Author(s): Lingfei Wu, Peng Cui, Jian Pei
– Description: a comprehensive description of GNNs based on the latest research results.

3. useful books for implementation
1. ‘Programming PyTorch for Deep Learning’.
– Author: Ian Pointer
– Description: An introduction to implementation using PyTorch, applicable to GNN implementations.

2. ‘Hands-On Graph Neural Networks with PyTorch and DGL’.
– Author: Max Pumperla
– Description: details the implementation of GNNs using PyTorch Geometric and DGL.

3. ‘Graph Machine Learning’.
– Author(s): Claudio Stamile, Aldo Marzullo
– Description: a practical guide to utilising graph data in machine learning.

4. related papers and resources
– Papers:.
– Kipf & Welling, ‘Semi-Supervised Classification with Graph Convolutional Networks’ (basic GCN paper).
– Thomas N. Kipf, Max Welling.

– Implementation resources:.
– Deep Graph Library (DGL): [official website](https://www.dgl.ai/)
– PyTorch Geometric: [official website](https://pytorch-geometric.readthedocs.io/)

OpenKE: tool for knowledge graph embedding.