What is Graph Technology? Introduction to Knowledge Graph Technology
Knowledge graph technology provides a way to represent and connect information in a way that mirrors how humans understand the world. A knowledge graph is a structured representation of real-world entities (like people, places, things, and concepts) and the relationships between them. It’s visualised as a network where entities are nodes and relationships are “edges” connecting those nodes.
Knowledge graph technology focuses on the meaning of the data, not just the raw data itself. This allows for more intelligent querying and reasoning. By showing relationships, it provides context to information, making it easier to understand and use. Knowledge graphs can also integrate data from diverse sources and adapt to evolving information.
How does Knowledge Graph Technology work?
Knowledge graph technology centers on constructing a digital representation of knowledge that mirrors the human understanding of interconnected concepts. This is achieved by establishing a network of “nodes,” which represent real-world entities like people, places, or ideas. These nodes are then linked by “edges,” which define the relationships between them, such as “is a” or “located in.”
To provide structure, “ontologies” or schemas define the types of entities and relationships that exist within the graph, acting as a blueprint for the data.
The process begins with the ingestion and integration of data from diverse sources, both structured and unstructured. Natural language processing techniques are then employed to extract entities and relationships from this data, effectively translating text into a structured format. This extracted information is used to build the graph itself.
A critical aspect of knowledge graphs is their ability to perform reasoning and inference. By analyzing the relationships between nodes, they can derive new knowledge and uncovering hidden connections. Finally, users can query the graph to retrieve information and explore these connections, enabling more sophisticated and context-aware searches. The benefits of this technology include enhanced understanding of data, improved search and discovery, advanced reasoning capabilities, and seamless data integration.
Is graph technology database SQL or NoSQL?
Graph databases are generally categorised as NoSQL databases due to their fundamental focus on relationships rather than rigid, tabular structures. This distinction arises from their purpose: while SQL databases excel at managing structured data in tables, graph databases prioritise the connections between data points, making them ideal for highly interconnected data.
This emphasis on relationships, coupled with the use of graph-specific query languages instead of SQL, places them firmly within the flexible and schema-less realm of NoSQL databases. Consequently, graph databases are well-suited for applications involving complex networks, such as social connections, recommendation systems, and knowledge graphs, where the relationships between data are paramount.
Instead of SQL, the graph databases use languages such as Cypher, Gremlin or GQL to query and traverse the connected data in complex knowledge graphs in a way that traditional databases cannot.
Graph Technology and AI relationship?
The synergy between graph technology and artificial intelligence is profound, with graph structures significantly enhancing AI capabilities. At its core, AI, particularly machine learning, relies heavily on data, and graph technology excels at capturing and analyzing the intricate relationships within that data.
This relational understanding is vital for AI, as many real-world scenarios involve complex interconnections between entities. A prime example is the application of knowledge graphs, a product of graph technology, which provides AI systems with structured knowledge representation. This enables contextual understanding, reasoning, and more informed decision-making, notably in areas like natural language processing and question-answering.
Recently, approaches such as GraphRAG (RAG stands for Retrieval Augmented Generation) are being used for reliable answers to more complex queries. With GraphRAG, knowledge graphs are used to find connections between data in large language models, providing structure for data similar to human reasoning.
Furthermore, Graph Neural Networks (GNNs) leverage graph-structured data, allowing AI models to learn from the relationships between nodes, leading to performance improvements in tasks like social network analysis and recommendation systems. Graph databases also facilitate reasoning and inference, empowering AI to derive new insights from existing connections, which is essential for building adaptable AI systems.
Finally, graph technology contributes to AI explainability by visualising the relationships and decision pathways, fostering transparency and understanding in AI processes, resulting in more robust and intelligent systems.
Graph Technology Landscape
Due to the graph technology being still on the rise, the landscape of graph technology tools is still very fragmented. There are no clear winners in fields that focus on knowledge graphs, their visualisation or practical use in different industry and government sectors.
Graph Databases
While Neo4j is considered a standard in graph database world, there are several competing solutions from software giants such as Microsoft Azure Cosmos DB and Amazon Neptune. Other competitors include ArangoDB, JanusGraph, Memgraph and TigerGraph and several others.
Data Integration Tools
Data integration tools take care of data ingestion and orchestration from various sources into graph databases to fully reap the benefits of graph data processing they provide. Neo4j offers its own Neo4j ETL tool, but other tools are also available to process data in bulk. They include Apache Hop and Apache Airflow, Graph.Build and Kafka Connect Neo4j Connector. GraphAware Hume uses its own data ingestion module called Orchestra that can be used to ingest data from various sources in an easy-to-setup pipeline with graphical UI.
Graph Processing Engine
Each of the producers of graph databases provides its own graph processing engine that allows the user to interact with the data stored in the database. They allow the use of graph data algorithms and queries to process the data. Neo4j provides its Neo4j GDS solution, Microsoft has GraphEngine, Memgraph has Mage. Third-party solutions include Deep Graph Library, PuppyGraph and GraphStorm. GraphAware Hume provides a graph processing engine capabilities that can be used even without the knowledge of the Cypher query language.
Natural Language Processing
Natural language processing (NLP) uses large language models to interact with graph databases. It allows the ingestion of unstructured data from e.g. text reports or transcripts as well as interacting with the data in natural language, e.g. via a chatbot. Tools such as NetOwl and Rosette are able to translate and process even multilingual data. GraphAware Hume allows the ingestion of unstructured data and can parse them via NLP into entities and their relationships to feed into a graph database.
Entity Resolution
Entity resolution makes sure that entities such as persons are properly identified when entered into a graph database. This is especially important in law enforcement and fraud detection, where the correct identification of a person is paramount. Specialized companies such as Senzing or Zingg provide entity resolution as a service via API and with the help of machine learning. GraphAware Hume also provides its own rule-based entity resolution capabilities.
Master Data Management
Master data management tools in the graph world allow users to clean, curate and share the data within the graph databases. All the main database providers such as Neo4j or Microsoft have their tools for their databases. Other players include CluedIn or Reltio.
Industry Specific Graph Apps
Main industries that currently rely on graph-based applications are intelligence analysis (both governmental and enterprise), law enforcement and finance sector (mainly for fraud prevention). While each provider of graph database has their own tools such as Neo4j Bloom, there are number of advanced graph apps that provide additional functionalities for data exploration. These include GraphAware Hume, Linkurious or GraphXR by Kineviz.
Graph Query Language
Each of the graph database providers uses its own graph query language that allows the users to interact efficiently with the stored data. Unlike SQL, the graph query languages allow to explore the relationships between data as well, making them extremely useful and efficient when querying highly interconnected data. Neo4j uses a language called Cypher, while Apache has its own called Gremlin. There is also an ISO-standardized Graph Query Language and several other languages, often adopted from SQL.
What are the benefits and advantages of graph technology?
Graph technology provides exceptional ability to manage and query relationships within data, a task that can be difficult and inefficient in traditional relational databases, especially when dealing with large amounts of data. This focus on connections enables a deeper comprehension of how various data elements interact. Furthermore, by emphasising these relationships, graph technology provides a richer context for data, which is crucial for applications that require understanding intricate interactions, such as social network analysis and fraud detection.
The architecture of graph databases is optimised for efficient traversal of relationships, resulting in significantly faster query performance for highly interconnected data, a critical advantage for real-time analysis of complex networks. The inherent flexibility and adaptability of graph data models allow for seamless modification and expansion, accommodating evolving data requirements.
Graph technology also streamlines data integration from diverse sources by concentrating on entity relationships, providing a unified data perspective regardless of origin. Its aptitude for identifying intricate patterns and connections makes it ideal for applications like recommendation systems and network analysis.
Graph technology’s natural affinity for data visualisation simplifies the understanding of complex relationships, revealing insights that would be challenging to extract using conventional methods. In essence, graph technology empowers organisations to leverage the value of interconnected data, leading to enhanced decision-making, deeper insights, and greater operational agility.
What are the applications of graph technology?
Graph technology has found its way into a diverse range of applications, leveraging its ability to represent and analyze interconnected data. One of the most prominent applications is in social network analysis, where graph databases excel at mapping and understanding relationships between users, enabling features like friend recommendations and network influence analysis.
Recommendation systems also heavily rely on graph technology, using relationships between users and items to provide personalised suggestions for products, movies, or content. In the realm of fraud detection, graph databases uncover suspicious patterns and connections within financial transactions or user behavior, helping to identify fraudulent activities.
Knowledge graphs are a specialised application of graph technology and are used to build structured representations of knowledge, powering applications like search engines, question-answering systems, and semantic search.
In the field of logistics and supply chain management, graph technology optimises routes, manages inventory, and analyzes dependencies between suppliers and distributors.
Network infrastructure management utilises graph databases to model and analyse complex network topologies, aiding in troubleshooting and performance optimisation. In the life sciences, graph technology is employed in drug discovery and genomics, investigating relationships between genes, proteins, and diseases to accelerate research.
In enterprise environment, customer 360 applications utilise graphs to merge and analyse customer data from various sources, giving a holistic view of the customer journey and improving customer service and marketing.
How does Graph Technology help Law Enforcement Agencies?
Graph technology provides law enforcement agencies with powerful tools to analyze complex relationships and uncover hidden patterns within vast amounts of data. Here’s how it helps:
Mapping Criminal Networks:
Law enforcement can use graph databases to visualise and analyse connections between individuals, organisations, and activities. This helps to identify key players in criminal networks, understand their roles, and disrupt their operations. By mapping relationships between phone records, financial transactions, and social media activity, investigators can gain a comprehensive view of criminal organisations.
Fraud and Financial Crime Detection:
Graph technology excels at detecting anomalies and patterns in financial data. This helps to identify fraudulent transactions, money laundering schemes, and other financial crimes. By visualising the flow of money between accounts, investigators can trace illicit funds and identify the individuals involved.
Investigating Online Activity:
With the rise of online crime, law enforcement agencies need tools to analyze online activity. Graph databases can be used to map connections between online identities, social media accounts, and online forums. This helps to identify individuals involved in online radicalisation, cybercrime, and other online offenses.
Locating Missing Persons:
Graph technology can assist in locating missing persons by analysing their social connections, call records, travel patterns, and other data. By mapping relationships between the missing person and their contacts, investigators can identify potential leads and narrow their search.
Enhanced Intelligence Analysis:
Graph technology facilitates the integration of data from various sources, providing a unified view of intelligence. This enables analysts to identify connections and patterns that would be difficult to detect using traditional methods. By visualising relationships between different pieces of information, analysts can gain a deeper understanding of criminal activity.
What is the future of Graph Technology?
The future of graph technology will be driven by the increasing complexity of data and the growing demand for interconnected insights. It is becoming an indispensable tool for any organisation aiming to leverage the power of their interconnected data.
The key future trends include:
Increased Integration with AI:
Graph technology is becoming increasingly intertwined with artificial intelligence, particularly in areas like machine learning and natural language processing. Knowledge graphs are set to play a pivotal role in enhancing AI’s ability to understand context, reason, and generate accurate responses. Graph Neural Networks (GNNs) will continue to evolve, enabling AI to learn from complex relationships within data.
Advancements in Knowledge Graphs:
Knowledge graphs will become more sophisticated, with enhanced capabilities for semantic understanding and automated knowledge discovery. This will lead to more intelligent search engines, question-answering systems, and virtual assistants.
Expansion Across Industries:
Graph technology’s applications will expand across various industries, including healthcare, finance, supply chain management, and cybersecurity. In healthcare, graphs will aid in drug discovery and personalised medicine. In finance, they will enhance fraud detection and risk management.
Real-Time Graph Processing:
The demand for real-time graph processing will increase, enabling organisations to analyze and respond to data in real-time. This will be crucial for applications like fraud detection, network monitoring, and social media analysis.
Graph Technology and Generative AI:
The combination of Graph technology and Generative AI is a very exciting field. Graph databases and knowledge graphs are being used to ground Generative AI with factual data, and to provide context to the LLM‘s. This helps to reduce “hallucinations” that LLM’s can produce.