Knowledge Graph Tools: The Ultimate Guide

Lei Huang
Chief Architect & Co-Founder
March 19, 2024
Knowledge Graph Tools: The Ultimate Guide

Decoding the complexities of vast datasets requires an adept tool, and knowledge graph tools are here to assist. These tools help to interlink diverse data sources for a holistic view, leading to enhanced analytics and decision-making. In this guide, we will look at what knowledge graph tools are, how they work, and key factors to consider when choosing one. By the end of the article, you'll be equipped with the foundational knowledge needed to navigate the world of smart data management.

What is a Knowledge Graph?

A knowledge graph is a powerful tool that encodes entities and relationships tailored to specific domains or organizations. The result is a graph that stores knowledge in a structured format that can serve as a foundation for informed decision-making. By creating a network of interconnected information, knowledge graphs make the data they contain more understandable and applicable.

The creation of knowledge graphs allows organizations to gain deeper insights from their data, thus facilitating more intelligent decision-making. The ecosystem of a knowledge graph (and graphs in general) is comprised of the following:

  • Nodes: Represent various entities such as objects, places, or persons. They are the key subjects of the stored information.
  • Edges: Define the relationships between these nodes and create a network of interconnected information within the knowledge graph.
  • Properties: Act as key-value pairs attached to nodes and edges, enriching them with detailed information. For instance, a node representing a person might have properties like name, age, and occupation. Similarly, an edge representing a relationship might have properties denoting the nature or strength of that relationship. Properties play a crucial role in adding context and depth to the data, allowing for more nuanced queries and analysis.

These components work together to create a comprehensive and interconnected system of knowledge. These ontologies, offering a formalized depiction of entities, significantly contribute to the standardization and interconnection of the knowledge held within the graph.

When applying the knowledge graph concept in real-world scenarios, the selection of appropriate tools is crucial. The right tools not only facilitate the effective implementation of knowledge graphs but also unlock their full potential. Different tools offer varying capabilities and can significantly influence the productivity and success of knowledge graph projects. Users continually seek advanced tools that push the boundaries of what knowledge graphs can achieve, broadening the scope and impact of their use cases.

What is a Knowledge Graph Tool?

A knowledge graph tool makes knowledge graphs work in real-world applications. These tools are key for a few important jobs:

  • Interconnect different data types. They combine structured, semi-structured, and unstructured data from various data sources. This is key to building a knowledge graph that covers all your information needs.
  • Create interconnected data systems, making all your information visible and connected in one place.
  • Provide coherent and searchable frameworks for enhanced analytics and insights.

The benefits of using knowledge graph tools include:

  • Complete View of Data: They bring together data from different places, giving you a full view of all your data.
  • Showing Data Connections: These tools are great at revealing how different data sets are linked, providing contextual search, suggestions, or visualizations of the relationships between data.
  • Handling Data Changes: They adapt well to changes in your data, keeping your knowledge graph current.
  • Easy Data Addition: These tools allow for easy integration of new data and ideas.

Overall, knowledge graph tools help you practically implement a knowledge graph. They usually come with built-in connections to various data platforms, simplifying the process of setting up and using a knowledge graph. Let's look further into how these tools work and their importance in knowledge graph applications.

A simple example of knowledge graph

How Does a Knowledge Graph Tool Work?

Knowledge graph tools function through a series of integrated processes that transform raw data into a structured and useful knowledge graph. The first step involves data modeling and preparation, where these tools define and structure the data model, crucial for accurately reflecting the relationships between different data elements. This stage sets the foundation for how data is interconnected within the graph. Machine learning techniques and other mechanisms, such as web scraping, can also contribute to building the complex datasets the knowledge graph will run on.

Once the data model is established, the next role of these tools is data import and storage. They handle the ingestion of data from various sources, accommodating different formats like RDF, CSV, JSON, and XML. Effective storage mechanisms are then employed to ensure that this data is stored efficiently, maintaining the integrity and scalability of the knowledge graph.

Accessing and visualizing the data is another critical role played by these tools. They provide functionalities for querying the graph, allowing users to retrieve specific data points and insights. Additionally, visualization features are often included to help users understand the complex network of relationships and patterns within the data.

Knowledge graph tools function through a series of integrated processes that transform raw data into a structured and useful knowledge graph.

Data Modeling and Preparation

Data preparation, a crucial step for knowledge graph development, involves extracting and integrating diverse information from various sources. Structured data sources, rich in organizational and web information, are integral to this process. A prime example of a structured data format used in knowledge graphs is the Resource Description Framework (RDF). RDF’s straightforward structure of triples (subject, predicate, object) simplifies the representation of complex relationships and is particularly well-suited for knowledge graph construction.

Integrating machine learning, web scraping, and data science significantly strengthens the process of building comprehensive datasets for knowledge graphs. These technologies are especially proficient at parsing and understanding linguistic data, a key component in the semantic web. This contributes significantly to the knowledge graphs' depth and accuracy. GraphGPT can be an early example of extrapolating knowledge graphs from unstructured text. This tool demonstrates how modern AI and machine learning algorithms can streamline the creation of knowledge graphs.

Public knowledge graphs are also popular for testing and developing knowledge graph products. Wikidata, DBpedia, and Google Knowledge Graph are all available for different use cases and research.

Data Storage and ETL

In the development of knowledge graphs, the import and storage of data are vital components. This process involves various technologies and formats to handle diverse data types efficiently.

Graph Databases

Graph databases like Neo4j, Amazon Neptune, and Microsoft Azure Cosmos DB are popular for storing knowledge graphs. These databases are optimized for storing and managing relationships, making them a good choice for handling the interconnected nature of knowledge graph data. They provide capabilities for querying complex relationships and patterns within the graph.

Files with Open Data Formats

Knowledge graph data can be imported and stored using open data formats. RDF (Resource Description Framework) is commonly used for its ability to represent complex relationships in a flexible manner. CSV files are another option, offering simplicity and wide compatibility for structured data. Parquet, known for its efficiency in handling large datasets, is particularly useful for big data scenarios.

Traditional Relational Databases

In some cases, traditional relational databases are employed for knowledge graph storage, especially when dealing with structured data that fits well into tabular formats. While they might not offer the same efficiency level in querying relationships as graph databases, they are widely used and offer robustness and reliability for specific data types.

Each of these storage methods offers distinct advantages, and the choice often depends on the specific requirements of the knowledge graph, such as the complexity of data relationships, scalability needs, and the intended use cases. Integrating these diverse storage solutions allows for a more flexible and comprehensive approach to knowledge graph management.

Implementing Machine Learning Algorithms

Implementing machine learning algorithms in knowledge graphs opens up various possibilities for enhancing how we process and analyze data. Knowledge graphs can systematically improve the accuracy of machine learning systems and extend their range of capabilities. This improvement is largely due to the rich, contextual information that knowledge graphs provide, which is crucial for decision-making systems' predictive accuracy and flexibility.

Knowledge graphs, including Google’s Knowledge Graph, utilize machine learning to fill in gaps in training data, mapping explanations for predictions to specific nodes in the graph using knowledge graph data.

Introducing PuppyGraph

After considering the various traditional data import and storage methods in knowledge graph development, it's worth noting a cutting-edge solution in this space: PuppyGraph. PuppyGraph distinguishes itself as a graph query engine capable of directly ingesting data from open data formats and traditional relational databases without needing a separate ETL (Extract, Transform, Load) process. This feature significantly simplifies the data integration process.

Critical features of PuppyGraph that lend themselves well to knowledge graphs include:

Support for Open Graph Data Query Languages: PuppyGraph is versatile in its query capabilities, supporting open graph query languages like Gremlin and OpenCypher. This makes it adaptable to various user preferences and use cases.

Querying Beyond Graph Databases: While providing graph query capabilities like a graph database, PuppyGraph can query data directly from data lakes, relational databases, and other sources. This offers a level of flexibility and convenience that is not typically found in other solutions.

All supported sources for PuppyGraph

Enhanced Flexibility: PuppyGraph's ability to work with various data sources and query languages positions it as a highly flexible tool for knowledge graph applications. It caters to the diverse needs of different data environments, making it a valuable asset for organizations looking to leverage the power of knowledge graphs.

How traditional graph databases work
How PuppyGraph works

Visualizing Data with Knowledge Graph Tools

In knowledge graphs, visualizing data is a critical aspect that enhances the usability and comprehensibility of complex datasets. Visualization bridges intricate data relationships and user-friendly interpretations, enabling experts and non-experts to explore and derive insights from knowledge graphs.

The complexity of knowledge graph data, often featuring intricate relationships and multi-dimensional entities, can be challenging to navigate and understand in raw, textual formats. Visualizations convert these complexities into intuitive, graphical representations, making the data more accessible and understandable. This approach not only aids in recognizing patterns and anomalies in the data but significantly enhances user engagement across technical and non-technical users. Users can interactively explore the graph, click on nodes to discover related entities, or observe patterns visually that signify important relationships or groupings.

Knowledge Graph Visualization

Visualizing knowledge graph data comes with particular challenges that need to be managed for effective data representation:

  1. Handling Large Graphs: Knowledge graphs can be massive, with many nodes (entities) and edges (relationships). This makes it tough to show the entire graph clearly. Visualizations can get too crowded, making it hard to see individual details or patterns.
  2. Emphasizing Relationships: Since knowledge graphs focus on how nodes are connected, it's essential for visualization tools to show these relationships clearly. This includes easily finding and displaying links and neighbors in the graph, which can be challenging in dense networks where many nodes are closely connected.
  3. Clear Display of Data and Properties: Knowledge graphs often include detailed information for each node and relationship. Showing this information without making the visualization too busy is a key challenge. Tools need to balance providing enough details and keeping the graph easy to read.
  4. Flexible Querying: For effective visualization, tools must allow users to make specific queries to explore certain parts of the graph or highlight particular relationships. This requires sophisticated technology to handle complex queries and a user-friendly interface for different levels of users.

Overcoming these challenges is crucial for creating visualization tools that can make complex knowledge graph data understandable and useful. These tools must manage large, complex data sets and provide intuitive ways for users to interact with and explore this data. Next, we can look at some tools that can help with implementing visualizations for knowledge graphs.

Popular Tools for Knowledge Graph Visualization

When looking at visualization tools for knowledge graphs, there aren't many specifically made for them. Because of this, it often makes sense to pick tools that can handle extensive, complex data and show detailed connections, even if they weren't originally designed for knowledge graphs. Below, there are some selected tools that meet the critical needs of knowledge graph visualization, like showing lots of interconnected data clearly and in a way that's easy to understand.


This is a powerful JavaScript library for graph visualization and analysis in web applications. It can integrate with graph databases using Gremlin or Cypher to fetch data and render it into interactive and visually appealing graph structures. Cytoscape.js is highly customizable and is suitable for data-driven web applications.


Another JavaScript library, Sigma.js, is designed specifically for graph drawing. It can be used with graph databases supporting Gremlin or Cypher to visualize the queried data. Sigma.js is known for its performance in rendering large networks.

GraphXR by Kineviz

GraphXR is a powerful visualization tool known for its ability to handle large-scale graph data. It stands out for its user-friendly interface and interactive visualization capabilities. GraphXR can connect to various graph databases that use query languages like Gremlin or Cypher, making it a versatile choice for data analysts and non-technical users.


Linkurious is a commercial tool that offers advanced visualization capabilities and can connect to various graph databases using Gremlin or Cypher. It provides an intuitive interface for exploring and visualizing graph data and is aimed at enterprise users.


gDotV is an effective tool for graph data visualization. It offers functionalities that allow it to connect with graph databases, fetch data using Gremlin or Cypher queries, and visualize the data meaningfully. 

When it comes to enabling visualization capabilities, it's worth noting that PuppyGraph can significantly enhance this visualization process. Since PuppyGraph supports popular graph query languages like Gremlin and Cypher, it can be easily integrated with these visualization tools. This integration allows PuppyGraph to leverage popular visualization solutions, making it versatile for handling and visualizing data from various sources. 

PuppyGraph also has its own built-in visualization tool that can also be used without any need for further integration into a tool such as those listed above

The ability of PuppyGraph to work seamlessly with these tools means that users can benefit from the advanced visualization capabilities of these libraries while using PuppyGraph's querying strengths. This integration makes creating comprehensive and insightful visualizations for knowledge graphs easier, regardless of the data source.

A gif showing PuppyGraph visualize a cybersecurity data sets with different devices and their properties

PuppyGraph for Large Knowledge Graphs

PuppyGraph stands out as an exceptionally suitable tool for managing and leveraging large knowledge graphs, offering a comprehensive suite of features:

  • Efficient Data Importing: One of the critical strengths of PuppyGraph is its ability to import data from various sources without the need for an ETL (Extract, Transform, Load) process. This simplifies the integration of structured and unstructured data into the knowledge graph, making the data preparation phase quicker and more efficient.
  • Various Data Sources: PuppyGraph can handle data storage across different sources, including large datasets in terabytes and beyond. This scalability is crucial for organizations dealing with vast amounts of data, ensuring that the growth in data size doesn't impede performance or insights extraction.
  • Flexible Data Querying: With support for common graph query languages like Gremlin and Cypher, PuppyGraph can easily work with existing data access solutions and visualization tools. This flexibility allows seamless integration into existing knowledge graph ecosystems and facilitates a wide range of data exploration and analysis tasks.
  • Future Development and Innovation: Looking forward, PuppyGraph is proactively enhancing its capabilities by adding support for graph algorithms and integrating with large language model AI solutions. These developments are poised to further augment its functionality, particularly in advanced analytics, AI-driven insights, and predictive modeling.
PuppyGraph for large knowledge graph

PuppyGraph's ease of data integration across multiple sources, robust data storage capabilities, flexible querying options, and commitment to future enhancements makes it an ideal tool for organizations looking to harness the power of knowledge graphs. Whether for data exploration, visualization, or advanced analytics, PuppyGraph provides a versatile and powerful platform for knowledge graph management and utilization.


In conclusion, knowledge graphs and their tools are revolutionizing data management in organizations. By encoding entities and relationships tailored to specific domains, these graphs provide a foundation for knowledge-driven decisions. The right knowledge graph tool for an organization depends on specific needs and requirements. However, regardless of the tool chosen, the benefits are clear - improved data visibility, enhanced analytics and insights, and smarter decision-making.

Ready to leverage these benefits for your organization? Download the forever free PuppyGraph Developer Edition or begin your free 30-day trial of the Enterprise Edition today, and lay the foundation for knowledge-driven decisions in your organization.

Lei Huang, Chief Architect and co-founder of PuppyGraph, with over 10 years of experience in developing and managing high-performance data platforms. Lei was a Staff Software Engineer at Instacart, where he co-led the core payments team, led a major overhaul of the payments stack, integrating with various third-party systems. Prior to Instacart, Lei was the tech lead of Google payments full stack team, grow the team from 4 to 24 engineers.Lei is a three-time Google Code Jam world finalist, took 6th place in New York, and a two-time ACM/ICPC world-finalist with his team ranking 14th worldwide in Stockholm.

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