Introduction

This Post outlines a comprehensive approach to building knowledge graphs using Python, focusing on text analytics techniques such as Named Entity Recognition (NER), syntactic parsing, and relationship extraction. It details the process of cleaning and preprocessing text, identifying key entities and their relationships, and visualizing the data as structured graphs. The methodology leverages libraries like Spacy for NER and Large Language Models (LLMs) for relation extraction. The document also provides code snippets and examples for implementing these techniques, emphasizing the importance of event detection and co-occurrence analysis in generating insightful knowledge graphs. Finally, it offers a step-by-step guide for running the scripts to create and visualize the knowledge graphs.

Understanding Knowledge Graphs

A knowledge graph is a network of entities and their interrelations, represented in a graph structure. Entities are nodes, and relationships are edges connecting these nodes. This structure allows for efficient data querying and knowledge extraction. Knowledge graphs are used in various applications, including search engines, recommendation systems, and natural language processing. This is the graph we get after at the end of blog

https://github.com/kernel-loophole/KG-graph

Note: This blog discusses the core NER and relation extraction for knowledge graphs. For full usage and examples, please see the GitHub page.

The Power of Text Analytics

Text analytics is the process of converting unstructured text into structured data to gain insights, trends, and patterns. In the context of news mining, this involves identifying key entities such as people, organizations, and locations, understanding their relationships, and extracting meaningful events or facts.

Our approach leverages several techniques and libraries, including:

• Named Entity Recognition (NER): Identifying and categorizing entities in the text.
• Syntactic Parsing: Analyzing the grammatical structure of sentences.
• Relationship Extraction: Finding and interpreting the connections between entities.

Let’s dive into the code that makes this possible.

Building the News Mining Tool

The code is designed to process large amounts of text, identify key entities and relationships, and visualize the extracted data as a structured graph. Here's how it works:

def clean_spaces(self, s):
    s = s.replace('\\r', '')
    s = s.replace('\\t', ' ')
    s = s.replace('\\n', ' ')
    return s

def remove_noisy(self, content):
    """Remove brackets"""
    p1 = re.compile(r'（[^）]*）')
    p2 = re.compile(r'\\([^\\)]*\\)')
    return p2.sub('', p1.sub('', content))

Named Entity Recognition (NER)

Once the text is clean, we use Spacy's powerful NER capabilities to identify entities such as persons, organizations, and locations. These entities are the building blocks of our analysis.