Knowledge graph software provides a way to catalog data that creates relationships between and context around data points. Knowledge graphs make data management easier and more flexible.
Unlike traditional relational databases, knowledge graphs can accommodate any amount or type of data. A data team’s needs often outpace the organization’s catalog capabilities, resulting in chaos and costly infrastructure changes. Knowledge graphs eliminate that problem. In this post, we’ll provide a detailed overview of knowledge graph software and how it works.
What is knowledge graph software?
Knowledge graph software is a tool used to organize and represent information using graph databases. A knowledge graph is a graph database that connects data points via semantic relationships and displays them in a graph format.
These relationships allow people and computers to bridge the “data-meaning gap,” connecting context and concepts with data. By creating rich relationships between data points, knowledge graph software makes it easier to understand, navigate, and generate answers to complex queries across data silos. In short, knowledge graph software provides a flexible and scalable method to catalog data and translate that into business knowledge.
How does knowledge graph software work?
Knowledge graph software works by organizing and representing information using graph databases, connecting data points through semantic relationships. Here's a detailed overview of the process.
Compile data from different sources
First, it collects data from various sources like databases, documents, spreadsheets, and websites. This data can be structured (like tables) or unstructured (like text).
Standardize to make it machine-readable
Next, data is standardized and converted into a machine-readable and understandable format. This data integration process can be time-consuming, as data typically needs to be cleaned, transformed, or merged to ensure consistency and represented to allow machines to comprehend the context and meaning.
Data points are connected through semantic relationships
The standardized data is broken down into individual data points or “entities,” like people, places, and things (referred to as nodes), which are connected through semantic relationships (edges). For example, "Apple" (the company) might be connected to "Steve Jobs" through the relationship "co-founded by."
Machine learning and natural language processing
With semantically rich and standardized data, machine learning and natural language processing (NLP) techniques can automatically identify and create these connections. Machine learning algorithms extract information from unstructured data like text documents, while NLP is used to understand the meaning of the text and identify semantic relationships between entities.
The role of ontologies
Knowledge graphs ensure a shared understanding of the meaning of data through ontologies. Ontologies define the set of concepts within a domain, the properties of each concept (attributes or characteristics), and the relationships between them.
They provide the semantic structure needed to interlink disparate data points into logical relationships, providing the underlying foundation of meaning on which knowledge graphs are built. By supporting semantic reasoning, ontologies allow AI systems to infer additional insights not explicitly stated in the data.
Enterprise knowledge graph vs. graph database
Graph databases
Graph databases use graph structures to store and query data. Unlike traditional relational databases, which store data in rows and tables, graphs are composed of nodes and edges, where nodes are “entities” (a real object or abstract concept), and the edge between two nodes conveys the relationship between entities.
This structural approach enables efficient storage and retrieval of networks of relationships, simplifying data modeling and querying by eliminating the need for excessive code (like join statements) found in relational databases. Graph databases offer flexibility by allowing schema-less data modeling, enabling data structures to evolve as data requirements change.
Enterprise knowledge graphs
Enterprise knowledge graphs have the same benefits as graph databases but also provide semantic enrichment (more detailed metadata) and data standardization. While there isn’t a standard property graph data model, knowledge graphs adhere to the Resource Description Framework (RDF) provided by the World Wide Web Consortium (W3C). With rich, more descriptive ontologies, metadata, and standardized data, knowledge graphs enable advanced search, analytics, and reasoning.
Why use knowledge graph software?
Knowledge graph software offers a powerful solution for organizing, representing, and managing data, opening up numerous data management benefits:
Unify disparate data resources
The graph-based structure allows you to unify disparate data resources and flexibly add and integrate new data, business context, and definitions. With federated queries, you can eliminate data silos and ensure enterprise data remains accessible and usable to all data consumers.
Answer complex queries
Knowledge graphs make all your data queryable. Unlike relational databases, they don’t just deliver an indexed list of items but also infer context from the graph to provide more accurate responses. With data points enriched by connections and context, search results are more relevant and often reveal insights that might otherwise remain hidden in data silos. These capabilities allow data consumers to perform advanced graph-powered searches and answer complex queries, making them far superior to traditional databases for projects requiring deep comprehension and analysis.
Document everything about your business
With knowledge graph software, businesses can document everything, from data resources and business definitions to employee access policies, creating a comprehensive and easily accessible knowledge base.
Utilize and embed AI-powered data apps
Knowledge graphs are widely used in AI systems like recommendation engines, offering the ideal enabling framework for AI-powered data applications, workflows, and advanced analytics. AI systems require detailed knowledge and context to perform complex tasks. Without context, a singular machine-learning-based approach to automation is often incorrect and incomplete. The semantically enriched and interconnected data structure of knowledge graphs allows machines to understand it, which enhances the performance of AI systems, enabling them to make inferences, apply logic, and automatically create and surface new connections.
Connect to your DataOps ecosystem
Knowledge graph software easily integrates with various solutions in the DataOps ecosystem, such as data warehousing, observability, lineage, and BI. This connectivity ensures that organizations can leverage the full potential of their data assets to drive critical business decisions.
Check out our white paper for more details on how and why knowledge graphs can solve the painful data management problems endemic to most enterprises today.
Optimize data with knowledge graph-powered software
How can you ensure your organization’s data consumers can find, access, and use new and critical data sources? Data catalogs built on traditional relational databases are inflexible, often requiring months and costly infrastructure changes to support new types of data sources.
Knowledge graphs make it easy to integrate diverse resources and extend your data catalog as your data ecosystem grows, future-proofing your data catalog for new and advanced use cases. Data catalogs built on knowledge graphs provide a single, semantically organized, contextualized view of your data.
data.world’s graph-powered catalog
We built data.world’s cloud-native data catalog platform on top of a knowledge graph architecture to offer companies a unified view of all their data resources and knowledge (metadata, processes, policies, people) in one place.
With our graph-powered catalog, your essential business context and data connections support AI-powered applications that generate accurate, explainable, relevant responses. And you can find and use your data from anywhere, whether on-premises or in the cloud.
With this flexibility to extend your graph model to any new sources of data, knowledge graphs provide the ideal data ecosystem for agile data governance.
To learn more about how data.world’s knowledge graph software can transform your organization’s data management, book a demo today.
