Building Automated Knowledge Graph from Unstructured Data Using LLMs and Neo4j
In our previous graph data modeling article, we uncovered the power of knowledge graphs in extracting meaningful insights from unstructured data. We delved into how these graphs organize information by transforming text into interconnected nodes and relationships, with Neo4j as a pivotal tool for structuring unstructured data thus facilitating insightful discoveries.
However, despite its advantages, manual knowledge graph creation poses significant challenges. These challenges include time consumption, resource intensiveness, and susceptibility to errors, especially as data volumes surpass exponentially.
Recognizing these hurdles, the spotlight turns to automated knowledge graph creation driven by Large Language Models (LLMs), promising a revolutionary shift in data modeling. Automating graph data modeling through LLMs streamlines knowledge graph creation by extracting entities and relationships.
In this article, we delve deeper into the realm of automated knowledge graph creation powered by LLMs, exploring their capabilities, and potential impact on data modeling and information extraction.
Knowledge Graphs
A knowledge graph serves as a visual representation of interconnected entities and their relationships, offering a better way to visualize information. 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.
Large Language Models (LLMs)
A large language model (LLM) is?a model that can recognize a vast amount of text and possesses an impressive ability to generate human-like responses, by semantically understanding the contexts within the data. They are trained on huge sets of data. LLMs are known as transformer models as they’re built on neural network-based transformer architecture.
Automated Knowledge Graphs Using LLMs and Neo4j
The creation of knowledge graphs traditionally requires a significant amount of manual effort, including data cleaning, entity recognition, and relationship identification. However, large language models can significantly automate and enrich this process in several ways. For our scenario, the automated knowledge graph construction process is:
We started the process by passing a Wikipedia article into a Python script. This script then automatically identified entities and relationships from the text, creating a knowledge graph. Finally, we visualized the results using a Neo4j database. Let’s delve into the entire process, examining each stage in detail.
On the Fly Ontology
In the context of Neo4j, an ontology serves as a guiding framework for defining nodes, relationships, and their properties, thereby helping to prevent potential issues in data modeling. Without a predetermined graph schema, the LLM decides on the fly what types of entities — node labels and relationship types it will use.
However, this approach can sometimes lead to problems, such as the creation of redundant nodes or relationships that are semantically similar or identical. To address this, it's better to specify the ontology that the LLM should use to extract information. We can also pass an additional parameter to the LLM in the prompt to restrict the entities and relationships accordingly.
For the current scenario, we're not passing the ontology as the data isn't too complex.
Automated Entity Recognition
We pass the data as input and LLMs automatically identify entities from unstructured text data. The dataset includes various sources such as articles, reports, spreadsheets, or social media posts but in this article, we’re using Wikipedia Article. LLM links all the pronouns to the referred entity. Subsequently, through entity recognition, we aim to extract all the mentioned entities from the text.
This streamlined process significantly reduces the time and effort required for constructing a knowledge graph. To illustrate, we first load the article using the langchain WikipediaLoader. As depicted in the figures below, the LLM identifies a total of sixty three nodes.
Retrieving all the nodes by running a query.
Automated Relationship Extraction
Next, we need to establish the relationships between the retrieved nodes. At first, the LLM tried to identify various relationships between entities by analyzing their co-occurrence patterns in the text. This helps to automate the process of relationship identification and ensures that all relevant relationships are captured in the knowledge graph. The LLM identified a total of sixty one relationships across the above extracted entities.
Retrieving all the relationships associated with the nodes/entities.
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Enrichment of Knowledge Graphs
After automating the knowledge graph creation using LLMs, it's essential to recognize their pivotal role in enhancing knowledge graphs. LLMs contribute significantly by introducing new entities and relationships that might not have been previously identified. Moreover, they assist in disambiguating entities by linking them to their corresponding concepts in a knowledge base.
?? Entity disambiguation involves accurately identifying and distinguishing between entities with similar names or references, ensuring the correct entity is recognized in a given context.
For optimal graph construction, it's crucial to define the graph ontology comprehensively and perform entity disambiguation. This process maintains the depth and accuracy of the knowledge graph. It ensures a more comprehensive representation of the underlying domain, enriching the graph with valuable insights.
Querying and Visualization
The Cypher query language serves as a tool for extracting useful information from the knowledge graph. However, in the construction of an automated knowledge graph, we can simplify the process by formulating queries in plain English and passing them to an LLM which generates the corresponding cypher query along with the response. Subsequently, we execute the cypher query to observe the results, and finally, visualize the results in the form of a knowledge graph.
Example 01: Our query is to find the city of Walter Disney's birth.
The fusion of knowledge graphs and LLM leads to more accurate and comprehensive insights, accelerating decision-making processes, and fostering a better understanding of complex relationships within large and unstructured datasets. The synergy between knowledge graphs and LLMs is evident in streamlining and enriching the process of knowledge graph creation.
Continuous Learning and Adaptation
Building a knowledge graph is not static; it necessitates ongoing refinement and evolution. While the initial graph data model serves as a starting point, continuous learning and adaptation are imperative for its sustained relevance and effectiveness. As the graph expands in scale, entity and relationship disambiguation modifications become essential. Moreover, as the graph expands in scale, it may require refinements to optimize performance for key use cases. Through continuous monitoring and refinement, the knowledge graph can dynamically adapt to changing data and requirements, ensuring its continued utility and accuracy over time.
Conclusion
In conclusion, we've explored the transformative potential of automated knowledge graph construction using Large Language Models and Neo4j. By seamlessly integrating LLMs into the process, organizations can streamline graph data modeling, extract valuable insights from unstructured data, and enhance decision-making processes.
Through automated entity recognition, relationship extraction, and continuous learning, LLMs offer a promising pathway to create more accurate and comprehensive knowledge graphs. As data volumes continue to escalate, the synergy between LLMs and knowledge graphs becomes increasingly crucial in navigating the complexities of today's data-driven landscape. This fusion of technology empowers organizations to derive actionable insights, remain competitive, and drive innovation in their respective domains.
This article is written by Mahnoor Shoukat, AI Engineer at Antematter