In this article, we will explore the wide-ranging applications of knowledge graphs, examining how they are being used across industries to drive innovation, improve decision-making, and optimize processes. Whether it’s powering personalized recommendations, improving fraud detection, or enriching natural language processing, knowledge graphs are at the heart of modern intelligent systems.

What is a knowledge graph?

At the core of a knowledge graph lies a knowledge model—a network of interconnected descriptions of concepts, entities, relationships, and events. By linking data and enriching it with semantic metadata, knowledge graphs provide a structured framework for data integration, unification, analysis, and sharing.

At the core of a knowledge graph is a knowledge model—a network of interconnected descriptions of concepts, entities, relationships, and events. These descriptions:

• Have formal semantics, enabling both humans and machines to interpret and process them efficiently and without ambiguity.
• Contribute to one another, forming a cohesive network where each entity serves as part of the description for related entities.
• Connect and describe diverse data using semantic metadata, structured according to the knowledge model.

Defining Key Characteristics

Knowledge graphs integrate features from multiple data management paradigms:

• Database: They support structured queries, enabling efficient exploration of data.
• Graph: They can be analyzed as network-based data structures.
• Knowledge base: They incorporate formal semantics, allowing data interpretation and inference of new facts.

When represented in RDF, knowledge graphs offer an optimal framework for data integration, unification, linking, and reuse by combining the following:

1. Expressivity:
   • Standards like RDF(S) and OWL from the Semantic Web stack enable seamless representation of diverse data forms, including schemas, taxonomies, vocabularies, metadata, and master data.
   • The RDF* extension simplifies modeling of structured metadata, such as provenance.
2. Performance:
   • The specifications are carefully designed and tested to efficiently manage graphs containing billions of facts and relationships.
3. Interoperability:
   • A wide range of specifications supports data serialization, access (via SPARQL Protocols), management (SPARQL Graph Store), and federation.
   • The use of globally unique identifiers facilitates seamless data integration and publishing.
4. Standardization:
   • These frameworks are standardized through the W3C community process, ensuring they meet the needs of diverse stakeholders, from logicians to enterprise data professionals and system administrators.

The Role of Ontologies and Formal Semantics

Ontologies form the backbone of a knowledge graph's formal semantics, functioning as its data schema. They act as a formal contract between the developers of the knowledge graph and its users, ensuring clarity and consistency in interpreting the data. These users could be humans or software applications seeking reliable and precise understanding of the graph's contents. By fostering a shared understanding, ontologies establish a common framework for representing and interpreting data.

When formal semantics are employed in a knowledge graph, several key instruments are used for representation and modeling:

1. Classes

Entities are typically classified within a hierarchy of classes. For example, in a business context:

• Classes might include Person, Organization, and Location.
• Shared hierarchies can group these, such as Agent for persons and organizations or sub-classes like Country, City, and Populated Place under Location.
  This concept is borrowed from object-oriented design, where each entity generally belongs to one class.

2. Relationship Types

Relationships between entities are labeled with specific types that define their nature, such as friend, relative, or competitor. These types may also include formal definitions:

• parent-of is the inverse of child-of; both are subtypes of relative-of, which is symmetric.
• Relationships like sub-region or subsidiary can be defined as transitive.

3. Categories

Entities can be associated with multiple categories that capture semantic aspects. For example:

• A book might simultaneously belong to Bestsellers, Books by Italian Authors, and Books for Kids.
  Categories are often organized into a taxonomy, further structuring their relationships and meanings.

4. Free-Text Descriptions

Human-readable descriptions are frequently included to clarify the purpose or nature of an entity. These enhance usability by aiding search and providing context for users.

By combining these tools, ontologies not only define the structure and meaning of data within a knowledge graph but also enhance its versatility for both human interpretation and automated reasoning.

Misconceptions About Knowledge Graphs

Not all RDF graphs qualify as knowledge graphs. For example, a simple RDF representation of statistical data—such as GDP figures for different countries—doesn’t necessarily constitute a knowledge graph. While a graph format can be useful for organizing data, it doesn't always require capturing the semantic relationships behind it.

In some cases, an application may only need to associate the string ‘Italy’ with the string ‘GDP’ and a value like ‘1.95 trillion,’ without needing to define what constitutes a country or what ‘Gross Domestic Product’ actually means. The essence of a knowledge graph lies in the relationships between entities and the graph structure itself, not in the specific representation language used.

Similarly, not every knowledge base qualifies as a knowledge graph. A key characteristic of a knowledge graph is the interconnectedness of entity descriptions, where the definition of one entity inherently involves others. This interlinking of entities is what creates the graph structure (e.g., A is B, B is C, C has D, A has D). Knowledge bases that lack formal relationships and semantics—such as a simple Q&A system about a software product—are not knowledge graphs. Additionally, it's possible to have an expert system that organizes data in a non-graph format, using automated reasoning processes like ‘if-then’ rules to support analysis, but this still doesn’t represent a knowledge graph.

Prominent Examples of Big Knowledge Graphs

Google Knowledge Graph: Google popularized the term "Knowledge Graph" with the launch of its own in 2012. However, there are few technical details available regarding its organization, scope, or scale. Additionally, access to Google’s Knowledge Graph for external projects is limited, with most of its usage confined to Google's own applications.

DBpedia: DBpedia extracts structured data from Wikipedia's infoboxes, creating a vast dataset of over 4.58 million entities (more details here) and an ontology that spans a wide range of topics such as people, places, films, books, organizations, species, and diseases. As a key resource in the Open Linked Data movement, DBpedia has been crucial in helping organizations build their own knowledge graphs, providing millions of crowdsourced entities.

Geonames: Available under a Creative Commons license, the Geonames dataset offers access to 25 million geographical entities and features, serving as a comprehensive resource for geographic data.

WordNet: WordNet is one of the most widely used lexical databases for the English language, providing definitions, synonyms, and semantic relationships between words. It is commonly used to enhance the performance of natural language processing (NLP) and search applications.

FactForge: FactForge is a knowledge graph that combines Linked Open Data with news articles about people, organizations, and locations. It incorporates data from various knowledge graphs, including DBpedia and Geonames, as well as specialized ontologies like the Financial Industry Business Ontology.

Knowledge Graphs and Their Connection to RDF Databases

Graph-Based Structure: Knowledge graphs are inherently graph-based, meaning they focus on the relationships between entities. RDF databases (also known as RDF triplestores) are designed to store, manage, and query data represented in the form of RDF triples, which naturally align with the graph structure of knowledge graphs. This means that an RDF database is often the underlying technology used to build and store a knowledge graph.

Semantic Representation: RDF is designed to represent data with clear semantics, making it possible to describe not only the entities but also the relationships between them in a machine-readable way. Knowledge graphs leverage this semantic capability to link entities in meaningful ways. For instance, in a knowledge graph, you might not just store the fact that "Einstein developed the Theory of Relativity"; you could also include metadata about the type of relationship (i.e., "developed") and how it connects to other concepts like "Physics" or "Science," all of which can be efficiently stored and queried in an RDF database.

Inference and Reasoning: RDF databases often support reasoning capabilities, which means that new facts can be derived from existing ones. By applying rules or ontologies, an RDF database can infer new relationships or properties that are not explicitly stored in the data but can be logically deduced from it. This is a powerful feature of both RDF and knowledge graphs, as it allows systems to make intelligent inferences and discover new knowledge. For example, if an RDF-based knowledge graph contains facts about multiple scientists and their discoveries, the system could infer additional relationships, such as "Einstein worked in the field of physics" even if this specific relationship was not explicitly stated.

Scalability and Flexibility: RDF databases are built to handle large volumes of data and can integrate data from diverse sources. Knowledge graphs often contain millions (or even billions) of interconnected entities, and RDF databases are well-suited to manage such large-scale, heterogeneous data. They do not require a rigid schema, meaning new types of data or relationships can be added without major reconfiguration. This flexibility allows knowledge graphs to evolve over time as new entities and relationships are discovered or as more data is incorporated.

Interoperability: One of the major advantages of using RDF is that it is based on open standards and is inherently interoperable. Knowledge graphs built on RDF can easily connect to other external RDF datasets, enabling the integration of external knowledge sources (e.g., DBpedia, Wikidata) into your own knowledge graph. This is a key feature of the Linked Data movement, where data from different domains can be linked together to create a more expansive and interconnected view of knowledge.

Leveraging Knowledge Graphs for Text Analysis

It's no surprise that modern text analysis technology heavily relies on knowledge graphs:

• Large knowledge graphs offer background information and human-like understanding of concepts and entities, leading to more accurate interpretation of text.
• The analysis produces semantic tags (annotations) that link text references to specific concepts within the graph. These tags serve as structured metadata, enhancing search capabilities and supporting deeper analytics.
• Facts extracted from the text can be incorporated into the knowledge graph, enriching its value for further analysis, visualization, and reporting.

In essence, the integration of knowledge graphs into text analysis doesn’t just improve accuracy—it unlocks a deeper level of understanding and insight that was previously difficult to achieve with traditional, keyword-based methods. With knowledge graphs, systems can interpret and analyze text in a more human-like, semantic manner, making them invaluable for a wide range of applications, from research and decision-making to personalized services and automation.

What Are Knowledge Graphs Used for?

Knowledge graphs are powerful tools used across various domains to structure and link data, enabling smarter applications and deeper insights. They enhance search engines by providing contextual understanding, improve recommendation systems through personalized suggestions, and aid virtual assistants in interpreting queries more effectively. In healthcare, they support drug discovery and patient care, while in finance, they enable fraud detection and risk management.

Conclusion

Knowledge graphs have reshaped how we organize and analyze information, enabling organizations to uncover connections within complex data. As the volume and complexity of information grow, knowledge graphs will play an increasingly important role in helping businesses make sense of their data and gain valuable insights.