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Why Knowledge Graphs?

A Knowledge Graph turns your data into machine-understandable knowledge. But what separates data from knowledge? Knowing this answer is key to understanding the definition of knowledge graphs.

Real-world knowledge is:

  • situational: meaning alters depending on circumstances
  • layered: associations between concepts allow for nuanced understanding
  • changing: new discoveries instantly change meanings

These facets of knowledge represent the context that is often missing from data. When traditional data management systems attempt to capture context, they fail. These failures generate gridlock over mastering data, delay timelines when adding new data sources or properties, and cause missing datasets from analyses that lead to mistrust.

Knowledge Graphs, on the other hand, are purpose-built for the fluctuating nature of knowledge. They offer a more flexible foundation for digital operations by easily accepting new data, new definitions, and new requirements.

Born via the tradition of the semantic web, knowledge base instances, such as the Google Knowledge Graph and the Wikipedia Knowledge Graph, have propelled their organizations to informational success.

So, why Knowledge Graphs? Because being data-driven is not enough — but knowledge-driven organizations can act with full context and full confidence in their decisions.

Knowledge graph tracing information

What is a Knowledge Graph?

A Knowledge Graph is a flexible, reusable data layer used for answering complex queries across data silos. They create supreme connectedness with contextualized data, represented and organized in the form of graphs. Built to capture the ever-changing nature of knowledge, they easily accept new data, definitions, and requirements. An Enterprise Knowledge Graph is simply a Knowledge Graph of enterprise data.

Old Way Approach

A different approach

The “graph” in Knowledge Graph refers to a way of organizing data that highlights relationships between data points. Graph representation looks like a network of interconnected points. This is in contrast to databases like Oracle or MySQL — relational systems — where data is stored in tables. Relational systems are designed for stable business processes where the data model doesn’t change.
Merge

Less rigidity

This orientation to stability causes problematic rigidity in data integration. Data integration platforms, based on relational systems, require permanent transformation of the data to create uniformity across all entities. This creates a so-called “unified view” of data, but offers no flexibility for analysis by preventing representation of situational, layered, or changing realities.
Transparent

A flexible data layer

A Knowledge Graph, with its ability to make real-world context machine-understandable, is the ideal tool for enterprise data integration. Instead of integrating data by combining tables, data is unified using graph’s ability to endlessly link concepts — without changing the underlying data. Thus, data unification connects data silos and produces a flexible data layer for the enterprise.

What is a Knowledge Graph?

3:11

Stardog’s Laura Firey and Tim Sedlak walk you through the definition of a knowledge graph, its history, and where it fits in the data ecosystem.

How Knowledge Graphs Work

Based on Graph image

Based on graph

Graph’s flexibility alone is not enough to turn data into knowledge. Graph databases — a common use of graph — can accept new data more easily than relational databases, but functionality is limited by its single schema. But a graph database imposes one point of view of the world and requires that business logic is coded into the application directly, whereas the low-code Knowledge Graph stores logic centrally.

implemented ontology

Implemented Ontology

Ontologies define domain knowledge, including definitions, relationships, and rules. A Knowledge Graph acquires and integrates data into an ontology (or many) and then makes that knowledge available to enterprise applications.

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Enhanced by Artificial Intelligence

The Knowledge Graph’s Inference Engine intelligently derives new knowledge from the data and the business logic. When there are conflicting definitions — say, a Top Account in one database is called situationally a Prospect or Churn Risk — you can express in the data model that these are all “Companies” and use inference to analyze all relevant data.

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Limitless data access

A Knowledge Graph is only as powerful as the data it can access. Virtualization is key to incorporating all relevant data, enriching the Knowledge Graph. The combination of Knowledge Graphs and virtualization allows for full flexibility to evaluate dependencies across different systems and structures of data. Trace data lineage, detect correlation and causation, or perform root cause or impact analysis with ease.

data is key to unlocking insights

Unleash Insights

Question answering is made much easier with a Knowledge Graph. Information is easily prepped for a variety of uses, including queries and search, natural language processing, machine learning algorithms, and even visualization and business intelligence.

Graph Database vs Enterprise Knowledge Graph

What distinguishes an Enterprise Knowledge Graph platform from a plain old graph database? The difference is using graph for data storage versus using graph for data management.

Graph databases are awesome. We think they are so awesome that we built one, from scratch. But that wasn’t enough to make an Enterprise Knowledge Graph platform.

Property Graph vs. RDF Graphs

First things first. There are two types of main graph data models: Property Graphs and Knowledge (RDF) Graphs. The property graph data model generally comprises three elements:

  • Nodes: The entities in the graph.

  • Edges: The directed links between nodes. Consider them relationships.

  • Properties: The attributes associated with a node or with an edge.

The Knowledge (RDF) Graph model comprises two elements: nodes and edges, but they differ a bit from the property graph model.

  • In a knowledge graph, nodes can be resources with unique identifiers, or they can be values with literal strings, integers, or whatever. The edges (also called predicates or properties) are the directed links between nodes.

  • The “from node” of an edge is called the subject. The “to node” is called the object. When you connect two nodes with an edge, you form a subject-predicate-object statement, known as a Triple. The edges can be navigated and queried in either direction. So, a Knowledge Graph is a directed graph of triple statements.

It’s also important to note that there is no true standard property graph data model, whereas Knowledge Graphs do use a standard model, managed by the World Wide Web Consortium (W3C). This may mean that a property graph would be less interoperable in your organization. The strong standardization used in Knowledge Graphs also easily enable the addition of knowledge toolkits.

Knowledge for Enterprise Data Management

Stardog provides an Enterprise Knowledge Graph platform whose end result is an Enterprise Knowledge Graph. An Enterprise Knowledge Graph platform enriches and amplifies RDF graph (as a data structure and a data model) into something greater than the sum of its parts. And it does this by adding a knowledge toolkit to a Graph Database. Real Enterprise Knowledge Graph platforms require integrated machine learningdata quality management tools, query explanation, and model checking. Additionally, a suite of tools and connectors to make it easy to connect, map, and model all the data that matters, regardless of its structure. By deeply integrating all these features with a graph database, the Enterprise Knowledge Graph platform supports a much wider and deeper range of services.

Relational silos and graph silos

Graph database vendors, including Neo4j, which is the leading property graph database, are busy turning relational silos into graph silos. We applaud that effort. Graph silos are often better than relational silos. But at the end of the day, a silo is still a silo, whether it’s got tables, key-value pairs, or nodes and edges inside of it. And unconnected data sucks.

Benefits of Knowledge Graphs

Launch faster, iterate easily
Stardog’s flexible data model and low-code platform mean easier updates — making you more responsive to market opportunities and customer needs.
Agile data-driven operations
Translate and automate even the most complex business processes, saving time and money by replacing laborious manual analyses and procedures.
More accurate results
With real-world context captured in your data, analytics are higher-quality and search results are more relevant. Empower employees to make better decisions with unified data.
Forrester Consulting TEI Cover
Forrester Consulting TEI study shows Stardog can deliver 320% ROI

Read this study to learn how several customers turned their data into knowledge, completed their data analytics projects faster, saved on infrastructure costs, and unlocked new business opportunities with Stardog.

The Stardog Difference

Access any data

Access the most popular SQL and NoSQL databases via our custom Connectors and extract info from unstructured sources with our NLP pipeline.

Advanced virtualization

Stardog’s patented virtualization accesses data remotely — whether its cloud or on-prem. No more copies of copies of data.

Best-in-class Inference Engine

Our Inference Engine was designed from the ground up to solve complex problems and offers full explainability so you can trust your results.

Unify data for insight

Put your unified data in the hands of more analysts with our BI/SQL Server, which easily connects to SQL-based BI platforms like Tableau and Power BI.

Reliable and scalable

Stardog’s enterprise-ready platform can scale up to 1 trillion triples; we’re also Kubernetes compatible and ACID-compliant.

Knowledge Graph Use Cases

  • Data fabric

  • Data lake acceleration

  • Analytics modernization

  • Semantic search

  • Supply chain

  • Drug discovery

  • Build your data fabric with Stardog’s Enterprise Knowledge Graph platform

    An Enterprise Knowledge Graph is the key ingredient to transforming existing data infrastructure into a data fabric. Create a flexible, reusable data layer for answering complex queries across data silos.

    See how
  • Unlock the power of data lakes

    Connect your data with the knowledge graph built to empower data teams to streamline data access and discovery and improve analytics insights.

    See how
  • Unleash your analytics

    Modernize your data platform with the knowledge graph built to empower data and analytics teams to connect any type of data, uncover impactful insights and speed time to market.

    See how
  • Stop sifting through irrelevant results

    Semantic search understands the user’s intent behind search terms, returning more relevant results in fewer hits. Unify data from disparate sources to build a complete repository of information; virtualization ensures the most up-to-date data is accessed.

    See how
  • Flexible data exchange for all stakeholders

    Supply chain stakeholders from product to manufacturing to distribution create their own data silos. Stardog solves the data silo problem, unifying data from existing systems without changing the underlying data and providing end-to-end transparency.

    See how
  • Complete traceability enables deeper analysis

    Data unification uncovers correlations across disparate data sources and represents the complex inter-relationships inherent in biomedical data. Unify clinical data, scientific research, disease models, and more to accelerate drug discovery.

    See how

Learn more

Download our e-guide to learn more about Knowledge Graphs and how they differ from other data management technologies.

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