Build a Knowledge Graph in NLP

A knowledge graph is a structured representation of knowledge that captures relationships and entities in a way that allows machines to understand and reason about information in the context of natural language processing. This powerful concept has gained prominence in recent years because of the frequent rise of semantic web technologies and advancements in machine learning. Knowledge graphs in NLP aim to model real-world entities and the relationships between them, providing a contextual understanding of information extracted from text data. This enables more sophisticated and nuanced language understanding, making it a valuable tool for various NLP applications. In this article, we will discuss knowledge graphs and see the process of implementation.

What is a Knowledge graph?

A knowledge graph is a graph-based knowledge representation that connects entities through relationships. These graphs are useful as we can integrate the generated knowledge graph with natural language processing models for tasks like question answering, summarization, or context-aware language understanding.

Key Steps in Knowledge graph:

But to generate knowledge graphs, we need to perform several steps, which are discussed below:

1. Data Acquisition: Gathering relevant textual data from diverse sources, which could include books, articles, websites, or domain-specific documents.
2. Entity Recognition: Then we need to use NLP techniques to identify entities (e.g., people, organizations, locations) within the text. Named Entity Recognition (NER) is an advanced method for this step.
3. Relation Extraction: Determining the relationships between identified entities This can involve parsing the syntactic and semantic structure of sentences to extract meaningful connections, which is called relationship extraction.
4. Graph Construction: Finally, building a graph structure where entities are nodes and relationships are edges. This step involves organizing the extracted information into a coherent graph representation. For advanced cases, we can enhance the graph by incorporating additional information like entity attributes, sentiment analysis or contextual details derived from the text but that are very complex, time-consuming and costly tasks.

What are the benefits of building a knowledge graph?

Some of the key benefits of the Knowledge graph are as follows:

• Improved Language Understanding: Knowledge Graphs provide a structured representation of information, enabling machines to better understand the context and semantics of language.
• Enhanced Information Retrieval: The graph structure facilitates efficient and precise retrieval of relevant information, improving search capabilities and reducing ambiguity.
• Context-Aware Applications: Knowledge Graphs enable context-aware NLP applications by capturing relationships between entities, supporting tasks such as sentiment analysis, named entity disambiguation, and coreference resolution.
• Support for Complex Queries: With the rich structure of a Knowledge Graph, systems can handle complex queries involving multiple entities and relationships, contributing to more advanced language processing.
• Facilitation of Inference and Reasoning: The graph structure allows for reasoning and inference, enabling the system to draw logical conclusions and provide more accurate responses.
• Domain-Specific Insights: Tailoring Knowledge Graphs to specific domains results in a deeper understanding of subject matter, facilitating domain-specific insights and applications.
• Interoperability and Integration: Knowledge Graphs promote interoperability by providing a common framework for integrating information from diverse sources, fostering collaboration between different systems and applications.

Knowledge Graph step-by-step implementation

Importing required modules

At first, we need to import all required Python modules like Pandas, Matplotlib, Networkx and NLTK etc.

Python3

import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from nltk import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import nltk

Downloading NLTK resources

As we have discussed previously that generating knowledge graph requires several NLP processing so we need to download some extra resources which will be used to pre-process the sentence texts.

Python3

# Download NLTK resources
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')

Output:

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!

Dataset loading

For this implementation, we will use a custom dataset or synthetic dataset for simple visualization. Then we will initialize the wordNet lemmatizer to preprocess the sentences using a small function (preprocess_text).

Python3

# Create a small custom dataset with sentences
data = {
    'sentence': ["Sandeep Jain founded GeeksforGeeks.",
                 "GeeksforGeeks is also known as GFG.",
                 "GeeksforGeeks is a website.", 
                 "Authors write for GFG."],
    'source': ["Sandeep Jain", "GeeksforGeeks", "GeeksforGeeks", "Authors"],
    'target': ["GeeksforGeeks", "GFG", "website", "GFG"],
    'relation': ["founded", "known as", "is", "write for"],
}

df = pd.DataFrame(data)
print(df)

Output:

                              sentence         source         target  \
0  Sandeep Jain founded GeeksforGeeks.   Sandeep Jain  GeeksforGeeks   
1  GeeksforGeeks is also known as GFG.  GeeksforGeeks            GFG   
2          GeeksforGeeks is a website.  GeeksforGeeks        website   
3               Authors write for GFG.        Authors            GFG   
    relation  
0    founded  
1   known as  
2         is  
3  write for  

Data pre-processing

Python3

# NLP Preprocessing
stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()

def preprocess_text(text):
    words = [lemmatizer.lemmatize(word.lower()) for word in word_tokenize(text) if word.isalnum() and word.lower() not in stop_words]
    return ' '.join(words)

# Apply preprocessing to sentences in the dataframe
df['processed_sentence'] = df['sentence'].apply(preprocess_text)
print(df)

Output:

                              sentence         source         target  \
0  Sandeep Jain founded GeeksforGeeks.   Sandeep Jain  GeeksforGeeks   
1  GeeksforGeeks is also known as GFG.  GeeksforGeeks            GFG   
2          GeeksforGeeks is a website.  GeeksforGeeks        website   
3               Authors write for GFG.        Authors            GFG   
    relation                  processed_sentence  
0    founded  sandeep jain founded geeksforgeeks  
1   known as        geeksforgeeks also known gfg  
2         is               geeksforgeeks website  
3  write for                    author write gfg  

Knowlwdge Graph Edges adding loop

Now we will define a for loop to iterate over the dataset and extracting the subject, object and relationships from each sentences. This step is very important because here we will create the nodes of the graph and their corresponding relationships will create the edges of the graph.

Python3

# Initialize a directed graph
G = nx.DiGraph()

# Add edges to the graph based on predefined source, target and relations
for _, row in df.iterrows():
    source = row['source']
    target = row['target']
    relation = row['relation']

    G.add_node(source)
    G.add_node(target)
    G.add_edge(source, target, relation=relation)

Visualizing the knowledge graph

We have already got the nodes and edges of our knowledge graph. Now it is time to just draw the graph for visualization. We will different node colors to make the graph more understandable. We will calculate node degree which is the number to connection one node have to assign different colors to less connected nodes and strong connected nodes.

Python3

# Visualize the knowledge graph with colored nodes
# Calculate node degrees
node_degrees = dict(G.degree)
# Assign colors based on node degrees
node_colors = ['lightgreen' if degree == max(node_degrees.values()) else 'lightblue' for degree in node_degrees.values()]

# Adjust the layout for better spacing
pos = nx.spring_layout(G, seed=42, k=1.5)

labels = nx.get_edge_attributes(G, 'relation')
nx.draw(G, pos, with_labels=True, font_weight='bold', node_size=700, node_color=node_colors, font_size=8, arrowsize=10)
nx.draw_networkx_edge_labels(G, pos, edge_labels=labels, font_size=8)
plt.show()

Output:

The generated knowledge graph

Conclusion

We can conclude that building a knowledge graph in NLP consisting of several steps. But we can make it easier by using Python modules of NLP processing and these graphs are very important for various real-time applications. However, we can face various challenges in the time of utilizing Knowledge graphs like data integration, maintaining quality and accuracy, scalability and storage, semantic heterogeneity and more.

Knowledge graphs aim to represent entities and relationships in continuous vector spaces which provide more clear understanding of semantic relationships and in future, knowledge graph may dynamically evolve to adapt to real-time changes, enabling system to stay current and responsive to dynamic environments.