Graph Data Modeling: Building Knowledge Graph from Unstructured Data Using Neo4j

In the era of AI, organizations are continually looking for innovative methods to extract meaningful insights from their vast amounts of unstructured data. Organizations have long struggled to extract meaningful insights from unstructured data. This data, including text, images, audio, and other non-tabular formats, holds immense potential. However, its inherent complexity often makes it difficult to utilize.

Let's explore graph data modeling to extract meaningful insights from unstructured data with the help of knowledge graphs. Knowledge graphs structure information by converting text into nodes and relationships. Neo4j, a leading graph database management system, allows the modeling of unstructured data. This allows organizations to discover valuable insights and enhance their decision-making processes.

Knowledge Graphs

A knowledge graph is a visual depiction of interconnected entities and their relationships, providing a comprehensive view of information. Unlike traditional databases that rely on tabular structures, knowledge graphs utilize graphs to organize data into nodes and edges, creating a flexible model.

They offer a robust framework for showcasing complex connections among various entities, allowing for intuitive querying and the exploration of contained information. This structured approach facilitates advanced semantic analysis, reasoning, and inference, leading to more accurate and comprehensive decision-making processes.

Neo4j

Neo4j is a graph database management system that stands at the forefront of the burgeoning field of graph databases. Renowned for its ability to efficiently model, store, and query highly interconnected data, Neo4j is particularly adept at handling complex relationships and intricate data structures.

Neo4j embraces a graph-based model, organizing data as nodes, relationships, and properties. This architecture facilitates the representation of real-world scenarios where relationships are as crucial as the entities themselves.

Components of a Neo4j graph

The components of Neo4j used to define the graph data model include:

• Nodes: Fundamental entities in Neo4j representing individual data points. They serve as the building blocks of the graph and can store information through properties.
• Labels: Labels categorize nodes, providing a way to organize and group related entities. Nodes can have one or more labels, defining their roles or characteristics within the graph.
• Relationships: Relationships define directional connections between nodes, representing meaningful associations within the data. They allow the modeling of real-world connections, such as friendships, dependencies, or transactions.
• Properties: Key-value pairs associated with nodes and relationships. Nodes can have properties that describe their attributes, while relationships can have properties to capture details about the connection. Properties enable the storage of additional information about the entities and connections in the graph.

Playing with Neo4J

To build a knowledge graph with Neo4j, we’ve to follow certain steps. These include:

• Defining an ontology, which serves as a conceptual blueprint for the data
• Ingesting data from various sources
• Identifying nodes and relationships within the data to highlight connections
• Querying and scrutinizing the data for valuable insights
• Visualizing the results for better comprehension and communication

Additionally, it is important to consistently update and refine the graph to accommodate new data and insights. This ensuring that the model remains relevant and efficient for the usecase.

Ontology

In Neo4j, there is no enforced schema like in SQL databases, it is often beneficial to define a conceptual schema or ontology, especially when dealing with complex data. An ontology outlines a set of concepts and categories in a domain and the relationships between them. This supports a structured framework for the data, simplifying the creation of nodes and relationships. In the context of Neo4j, an ontology can guide the creation of labels for nodes, their interrelationships, and their properties, thereby helping to prevent potential issues in data modeling.

Data Ingestion:

The first step in building a knowledge graph is to import the unstructured data into Neo4j. The data can come from various sources, such as text documents, social media, or web pages. Neo4j's flexible data model makes it easy to represent diverse entities and their relationships.

Nodes and Relationships:

We’ve to identify the entities in data, representing them as nodes in the graph. Establish relationships between these nodes to highlight the connections and dependencies within the information. If dataset contain people and their interests, we could represent individuals as nodes and relationships could indicate connections. For example, There is a person named “John” who used to play cricket. A relationship must have a type and direction which is by default from left to right.

**Text:** John used to play cricket
**Nodes:** John (Person) and Cricket (Activity)
**Realationship:** PLAYED
        

Querying and Analysis:

To extract the useful information from the knowledge graph a powerful query language is used known as Cypher. The expressive querying capabilities of Neo4j make it simple to discover patterns, analyze relationships, and derive valuable insights from interconnected data.

// Creating nodes for individual
CREATE (j:Person {name: "John"})

// Creating nodes for activity
CREATE (cricket:Activity {name: "Cricket"})

// Establishing a relationship between John and Cricket
CREATE (j)-[:PLAYED]->(cricket)
        

The above query first creates a Person node whose name is John with a label j. Similarly, it then creates an Activity node with a property name as cricket with a label cricket. After creating the two nodes it then creates a relationship between them whose direction is from left to right.

Visualization:

Neo4j offers built-in visualization tools for exploring and understanding the structure of your knowledge graph. These visual representations simplify the communication of insights and identification of trends within the data. For the above query the knowledge graph is created along with the details. The overview details shows that we’ve:

• Two node labels One for Person and one for Activity
• Relationship is PLAYED

Knowledge graph is shown below.

The example we've observed above is quite straightforward. Building a knowledge graph is an iterative process. The initial graph data model serves as a starting point, but it may need modification as use cases evolve or new knowledge, data emerges. Notably, as the graph scales, it may require refactoring to optimize performance for key use cases.

Automated Knowledge Graphs

In addition to manual knowledge graph creation, there are automated methods that utilize Language Models, like Transformer-based models, for the task.

These models, known as Large Language Models (LLMs), are capable of understanding and generating human-like text, providing an opportunity to automate the process of graph data modeling. They can extract entities and relationships from unstructured text and construct a knowledge graph without human intervention.

If you're interested in this topic, I can delve deeper into how LLMs can be used for automated knowledge graph creation in my next article.

This will include a deep dive into their capabilities, applications, and the potential impact on the overall field of data modeling and information extraction. This significantly streamlines the process and increases efficiency.

Conclusion

In this article, we have walked through the process of building a knowledge graph using Neo4j, starting from unstructured data. From the initial stages of data ingestion to the final visualization, we have seen how Neo4j efficiently constructs a graph.

This provides a visual representation of entities and relationships, thus enabling organizations to gain a deeper understanding of their data. As the quantity and complexity of unstructured data continue to escalate, the utilization of knowledge graphs through tools like Neo4j becomes increasingly crucial.

Additionally, the advent of automated knowledge graphs using Large Language Models offers a promising approach for further streamlining this process. Such tools empower organizations to harness actionable insights from their data, thereby granting them a competitive advantage in today's data-driven landscape.

This article is written by Mahnoor Shoukat , AI Engineer at Antematter