How to use LLMs for semantic parsing and knowledge graph construction

Posted on by Panther

How to Use Language Model to Construct Knowledge Graphs and Perform Semantic Parsing

In this tutorial, we will explore how to use Language Models (LMs) for semantic parsing and knowledge graph construction. Semantic parsing is the process of converting natural language into a structured representation that can be understood by machines. A knowledge graph is a way to represent information as a graph database, where entities are represented as nodes and relationships between entities are represented as edges.

Language Models have revolutionized natural language processing tasks by learning contextual representations of words and sentences. We will use the Language Model called “Longformer” in this tutorial. The “Longformer” is a state-of-the-art Language Model that can handle long-range dependencies and has been pre-trained on a large corpus of text data.

By the end of this tutorial, you will be able to use the Longformer Language Model for semantic parsing and knowledge graph construction. So, let’s get started!

Prerequisites

To follow along with this tutorial, you will need the following:

  • Python 3.6 or above
  • torch library installed (pip install torch)
  • transformers library installed (pip install transformers)
  • networkx library installed (pip install networkx)
  • matplotlib library installed (pip install matplotlib)

Step 1: Installing the Required Libraries

First, we need to install the necessary libraries. Open your terminal or command prompt and execute the following commands to install the required libraries:

pip install torch transformers networkx matplotlib

This will install the torch library, which provides a way to work with tensors and deep learning models, and the transformers library, which provides access to pre-trained Language Models like the Longformer. We also install the networkx and matplotlib libraries, which will be used for visualizing the knowledge graph.

Step 2: Importing the Required Libraries

Next, let’s import the required libraries into our Python script. Open your preferred Python IDE or text editor and create a new file, for example, semantic_parsing.py. Add the following imports at the beginning of the file:

import torch
from transformers import LongformerTokenizer, LongformerModel
import networkx as nx
import matplotlib.pyplot as plt

We import the torch library, the LongformerTokenizer and LongformerModel classes from the transformers library, and the networkx and matplotlib.pyplot libraries for visualizing the knowledge graph.

Step 3: Setting up the Longformer

Now, let’s set up the Longformer Language Model. Add the following code to your semantic_parsing.py file:

# Load the pre-trained Longformer model and tokenizer
model_name = "allenai/longformer-base-4096"
tokenizer = LongformerTokenizer.from_pretrained(model_name)
model = LongformerModel.from_pretrained(model_name)

# Set the device to GPU if available, otherwise use CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Set the model in evaluation mode
model.eval()

In this code, we load the pre-trained Longformer model and tokenizer using the longformer-base-4096 model. We also set the device to GPU if available, otherwise, we use the CPU. Finally, we set the model in evaluation mode.

Step 4: Parsing Input Text

Now, let’s define a helper function to parse the input text using the Longformer model. Add the following code to your semantic_parsing.py file:

def parse_text(input_text):
    # Tokenize the input text
    input_tokens = tokenizer.encode(input_text, add_special_tokens=False, return_tensors="pt")
    input_tokens = input_tokens.to(device)

    # Generate the output embeddings
    with torch.no_grad():
        output = model(input_tokens)[0]

    # Convert the output tensor to numpy array
    output = output.detach().cpu().numpy()

    return output

In this code, we tokenize the input text using the Longformer tokenizer. We then convert the tokenized input into tensors and move it to the device (GPU or CPU) where the Longformer model is located. We generate the output embeddings by passing the input tokens through the Longformer model and extract the output tensor. Finally, we convert the output tensor to a numpy array and return it.

Step 5: Constructing the Knowledge Graph

Let’s now define a function to construct the knowledge graph from the output of the Longformer model. Add the following code to your semantic_parsing.py file:

def construct_graph(output):
    # Create an empty graph
    graph = nx.Graph()

    # Add nodes to the graph
    for i, embedding in enumerate(output):
        graph.add_node(i, embedding=embedding)

    # Calculate the similarity between embeddings
    for i in range(len(output)):
        for j in range(i + 1, len(output)):
            similarity = cosine_similarity(output[i], output[j])
            graph.add_edge(i, j, weight=similarity)

    return graph

In this code, we create an empty graph using the nx.Graph() function from the Networkx library. We then iterate over the output embeddings and add nodes to the graph using the graph.add_node() function. Next, we calculate the similarity between the embeddings using a similarity metric such as cosine similarity. Finally, we add edges to the graph with the similarity as the weight using the graph.add_edge() function.

Step 6: Visualizing the Knowledge Graph

Finally, let’s define a function to visualize the constructed knowledge graph. Add the following code to your semantic_parsing.py file:

def visualize_graph(graph):
    # Draw the graph with node labels and edge weights
    pos = nx.spring_layout(graph)
    labels = nx.get_edge_attributes(graph, "weight")
    nx.draw(graph, pos, with_labels=True)
    nx.draw_networkx_edge_labels(graph, pos, edge_labels=labels)
    plt.show()

In this code, we use the nx.spring_layout() function to calculate the positions of the graph nodes. We use the nx.draw() function to draw the nodes and edges of the graph. We also use the nx.get_edge_attributes() function to get the edge weights and pass them to the nx.draw_networkx_edge_labels() function to label the edges with their weights. Finally, we use the plt.show() function to display the graph.

Step 7: Putting it All Together

Now, let’s put all the previously defined functions together and test our semantic parsing and knowledge graph construction pipeline. Add the following code to your semantic_parsing.py file:

def main():
    # Example input text
    input_text = "Apple Inc. is an American multinational technology company headquartered in Cupertino, California. It designs, manufactures, and markets consumer electronics, computer software, and online services."

    # Parse the input text
    output = parse_text(input_text)

    # Construct the knowledge graph
    graph = construct_graph(output)

    # Visualize the knowledge graph
    visualize_graph(graph)


if __name__ == "__main__":
    main()

In this code, we define the main() function, which is the entry point of our script. We provide an example input text and pass it to the parse_text() function to obtain the output embeddings. We then pass the output embeddings to the construct_graph() function to construct the knowledge graph. Finally, we visualize the knowledge graph using the visualize_graph() function.

Step 8: Running the Script

To run the script, open your terminal or command prompt, navigate to the directory where your semantic_parsing.py file is located, and execute the following command:

python semantic_parsing.py

This will execute the script and display the knowledge graph in a separate window.

Conclusion

In this tutorial, we have explored how to use Language Models (LMs) for semantic parsing and knowledge graph construction. We used the Longformer Language Model and the Networkx library to parse input text, construct a knowledge graph, and visualize the results. You can now apply this knowledge to various natural language processing tasks that require semantic parsing and knowledge graph construction.

Remember to experiment with different input texts, change the hyperparameters, and explore other pre-trained Language Models to improve the performance of your semantic parsing and knowledge graph construction pipeline. Happy coding!

Related Post