U.S. Department of Justice and Warnings of Extremist Violence

The U.S. Department of Justice has removed a significant study showing that violence from white supremacists and the far-right continues to surpass all other forms of domestic terrorism and violent extremism in the United States. This study, prepared by the National Institute of Justice and hosted on an official Department of Justice website, was available until at least September 12, 2025, according to records archived in the Wayback Machine. Jason Paladino was the first to uncover this removal. 404 Media noted this removal after Daniel Malmir, a Ph.D. student at the University of North Carolina at Chapel Hill whose research focuses on extremism via the Internet, brought it to their attention. A message posted on the page that previously hosted the study states that "The Department of Justice's Office of Justice Programs is currently reviewing its websites and electronic materials in line with recent executive orders and relevant guidelines." This action reflects ongoing changes in the field of Technology and policies. The message also stated: "During this review, some pages and publications will be unavailable. We apologize for any inconvenience this may cause."

What is a Knowledge Graph?

A diagram illustrating a real-world example of a Knowledge Graph from Wikidata. The diagram shows how different entities (in this case: women, their occupations, and the schools they attended) are interconnected and the relationships between them, visually embodying the concept of a Knowledge Graph in organizing data and showing context and relationships between them.

“Wikidata-knowledge-graph-awhi-women-occupations-schools-2021-0216.png” — Source: Wikimedia Commons. License: CC BY-SA 4.0.

The Knowledge Graph, also known as a semantic network, is a structured representation of real-world entities such as objects, events, situations, or concepts, illustrating the relationships between them. This data is typically stored in a graph database and displayed as a graphical structure, which explains its naming as a "Knowledge Graph". The Knowledge Graph aims to contextualize data by linking and semantic metadata, providing a framework for data integration, unification, analysis, and sharing.

At its core, the Knowledge Graph uses a graphical data model to store interconnected descriptions of entities, encoding free semantics or fundamental relationships that link these entities. This structure allows systems to answer complex queries by understanding the underlying meaning behind words, not just simple keyword matching. Source: IBM, Source: Ontotext, Source: Wikipedia (Publication Date: September 17, 2025).

Key Components of a Knowledge Graph

A diagram illustrating organized knowledge in the form of a Knowledge Graph, showing various entities (such as women, professions, and schools) and the relationships connecting them, representing the key components of a Knowledge Graph.

“Wikidata-knowledge-graph-awhi-women-occupations-schools-2021-0216.png” — Source: Wikimedia Commons. License: CC BY-SA 4.0.

A Knowledge Graph consists of three main components that form the basis of its data structure and relationships:

• Nodes: Nodes represent entities or concepts in the real world, such as people, places, things, or organizations. Each node typically has one or more labels to identify the type of node, and may optionally contain one or more properties (attributes). Source: IBM, Source: Neo4j (Publication Date: July 22, 2024).
• Relationships (or Edges): Relationships connect two nodes and illustrate how entities are related to each other. Like nodes, each relationship has a type that defines the kind of relationship, and may optionally contain one or more properties. These relationships provide context and meaning to the data. Source: IBM, Source: Neo4j (Publication Date: July 22, 2024).
• Organizing Principles: Also known as schemas or ontologies, these provide a framework for organizing nodes and relationships according to fundamental concepts necessary for specific use cases. Organizing principles allow for the integration of multiple schemas, appropriate classification of nodes, and determination of the context in which knowledge exists. Source: IBM, Source: Neo4j (Publication Date: July 22, 2024).

Benefits of Using Knowledge Graphs

via GIPHY

Knowledge Graphs offer several significant benefits for various applications and sectors:

• Improved Search and Question Answering: Knowledge Graphs enable search and question-answering systems to retrieve and reuse comprehensive answers to specific queries, saving time and improving result accuracy. Source: IBM (Publication Date: July 22, 2025).
• Data Integration and Unification: Knowledge Graphs serve as a framework for integrating and unifying data from diverse sources, even if they differ in structure, providing a comprehensive and interconnected view of information. Source: Ontotext (Publication Date: June 19, 2025).
• Discovery of New Knowledge: By connecting data points that may have been previously unnoticed, Knowledge Graphs support the creation of new knowledge and uncover hidden relationships between entities. Source: IBM (Publication Date: July 22, 2025).
• Supporting Generative AI Applications: Knowledge Graphs are used to ground Large Language Models (LLMs) with domain-specific or company-specific data, increasing response accuracy and improving interpretability through the context provided by data relationships. Source: Neo4j (Publication Date: July 22, 2024).
• Enhanced Analytics and Decision Making: By organizing data, relationships, and organizing principles, Knowledge Graphs contribute to extracting deeper insights from data, supporting better business decisions and eliminating the need for manual data collection. Source: IBM (Publication Date: July 22, 2025).

Use Cases for Knowledge Graphs

via GIPHY

Knowledge Graphs are used in a wide range of industries and applications due to their ability to organize and provide context for complex data:

• Generative AI for Enterprise Search Applications: Knowledge Graphs play a crucial role in collecting and organizing domain-specific or proprietary company information. GraphRAG technology, which grounds Large Language Models with Knowledge Graphs, is fundamental to AI applications that use private data to increase response accuracy and improve interpretability through the context provided by data relationships. Source: Neo4j (Publication Date: July 22, 2024).
• Fraud Detection and Analytics in Financial Services: Knowledge Graphs are used to represent networks of transactions and participants, helping companies quickly identify suspicious activity, investigate fraud, and adapt their strategies to changing fraud patterns. Algorithms such as pathfinding and community detection are applied to identify complex fraud networks. Source: IBM, Source: Neo4j (Publication Date: July 22, 2024).
• Master Data Management: Knowledge Graphs provide a unified and organized database for customers and company interactions with them, which is crucial for obtaining an accurate customer view, especially for companies with multiple departments. Source: Neo4j (Publication Date: July 22, 2024).
• Supply Chain Management: Knowledge Graphs represent the network of suppliers, raw materials, products, and logistics, providing a comprehensive view of the supply chain and helping managers identify vulnerabilities and predict disruptions. Source: Neo4j (Publication Date: July 22, 2024).
• Retail and Entertainment: Knowledge Graphs are used for product recommendations in e-commerce, suggesting personalized content to users based on their purchasing behavior and preferences, and in AI-powered recommendation engines for content platforms and social media. Source: IBM (Publication Date: July 22, 2025).
• Healthcare and Drug Discovery: Knowledge Graphs help organize and classify relationships within medical research, supporting physicians in verifying diagnoses and determining individualized treatment plans, and are used to store information about research topics such as protein sequences, genomes, and chemical data. Source: IBM, Source: Neo4j (Publication Date: July 22, 2024).

Building Knowledge Graphs

via GIPHY

Building a Knowledge Graph involves conceptually sketching a graph data model and then implementing it in a database. Knowledge Graphs typically consist of datasets from different sources, which often vary in their structure. Schemas, identities, and context work together to provide structure for diverse data:

• Schemas: Provide the overall framework for the Knowledge Graph.
• Identities: Classify core nodes appropriately.
• Context: Defines the environment in which knowledge exists, helping to distinguish words with multiple meanings.

Knowledge Graphs, powered by Machine Learning, use Natural Language Processing (NLP) to build a comprehensive view of nodes, edges, and labels through a process called semantic enrichment. When data is ingested, this process allows Knowledge Graphs to identify individual objects and understand relationships between different objects. This working knowledge is then compared and integrated with other relevant and similar datasets. Source: IBM (Publication Date: July 22, 2025).

Native graph databases, such as Neo4j, are a logical choice for implementing Knowledge Graphs. They natively store information as nodes, relationships, and properties, enabling intuitive visualization of highly interconnected data structures. These databases offer:

• Simplicity and Ease of Design: Facilitate straightforward data modeling.
• Flexibility: Easy to add new data, properties, relationship types, and organizing principles without extensive restructuring.
• Performance: Superior query performance, especially for complex paths and multi-hop relationships, as they store relationships directly rather than recreating them.
• Developer-Friendly Code: Support an intuitive and expressive ISO query language like GQL. Source: Neo4j (Publication Date: July 22, 2024).

Challenges in Building Knowledge Graphs

via GIPHY

Despite the many benefits of Knowledge Graphs, building and operating them face several challenges:

• Lack of Standardized Norms: There is no single generally accepted standard for building or representing Knowledge Graphs. This makes identifying entities that correspond to the same real-world entity across different graphs a non-trivial task, known as Knowledge Graph entity alignment, which is an active research area. Source: Wikipedia (Publication Date: September 17, 2025).
• Complexity of Data Storage: Triple stores, also known as RDF databases, face significant drawbacks compared to proprietary graph databases. They express all data in "triples" (subject-predicate-object) form and do not directly support relationships with properties or multiple relationships of the same type between entities. This often requires workarounds such as reification or using single properties, leading to larger databases, increased complexity, and poor query performance. Source: Neo4j (Publication Date: July 22, 2024).
• Limitations of Relational Databases: Relational databases do not natively store data relationships; instead, they must be constructed at runtime using joins or value lookups in query code. This means that each application and use of data requires its own implementation of relationships, making Knowledge Graph management more difficult and leading to poor performance as the number of relationships expands. Source: Neo4j (Publication Date: July 22, 2024).

Future Trends in Knowledge Graphs

Knowledge Graphs are moving towards deeper integration with Artificial Intelligence and Machine Learning technologies, significantly expanding their application scope:

• Deep Learning and Graph Neural Networks (GNNs): Recent advances in data science and Machine Learning, especially in Graph Neural Networks and representation learning, have expanded the scope of Knowledge Graphs beyond their traditional uses in search engines and recommendation systems. They are now increasingly used in scientific research, with prominent applications in fields such as genomics, proteomics, and systems biology. Source: Wikipedia (Publication Date: September 17, 2025).
• Large Language Models (LLMs): The recent successes of Large Language Models, specifically their effectiveness in producing syntactically meaningful embeddings, have propelled the use of Large Language Models in the task of entity alignment within Knowledge Graphs. Source: Wikipedia (Publication Date: September 17, 2025).
• Grounding Generative AI: Knowledge Graphs are considered "high-block" and influential technology for Generative AI today, used to ground Large Language Models for question-answering applications, providing rich data context and enhancing the accuracy and transparency of responses. Source: Neo4j (Publication Date: July 22, 2024).