Building Smarter AI Systems with Knowledge Graphs

Through their ability to connect, structure, and contextualize data, knowledge graphs have proven invaluable in addressing complex data challenges and powering successful apps. As we transition into the era of AI, these strengths make knowledge graphs a critical enabler for advanced AI systems.

However, building an effective knowledge graph requires careful consideration of the use case and data involved. There isn't a one-size-fits-all approach. Depending on the goals and the nature of the data, different types of knowledge graphs must be designed to address specific needs, ensuring the AI can function optimally.

In this series on building smarter AI systems with knowledge graphs, we'll explore some of the most important and interesting ways that a knowledge graph engine powers the AI apps of tomorrow. We'll start with a more theoretical overview and then work our way towards more hands-on implementations.

In the first post of this series, we'll introduce a typology of knowledge graphs to help you navigate these choices.

What are knowledge graphs?

A knowledge graph is a structured representation of information that models entities (nodes) and their relationships (edges) within a specific domain. By organizing and linking data points, knowledge graphs capture the context and connections needed to derive deeper insights from complex datasets. Unlike traditional databases, knowledge graphs provide a dynamic and intuitive way to explore data, enabling users, and AI agents, to uncover patterns and relationships that may not be immediately obvious.

Knowledge graphs are widely used in various fields, particularly for:

• Master Data Management (MDM): knowledge graphs help integrate data from multiple sources to create a unified, consistent view of entities, such as customers or products. This enables organizations to identify inconsistencies, reveal connections, and improve overall data quality.
• Recommendation Systems: By analyzing relationships between users, items, and interactions, knowledge graphs enhance recommendation engines in e-commerce, streaming services, and social platforms, delivering more personalized suggestions.
• Search and Unstructured Data Analysis: knowledge graphs structure information from unstructured data, such as text and multimedia, enabling search engines to provide more relevant, contextualized results and facilitating easier data exploration.

Knowledge Graph in the era of AI

Knowledge graphs offer a unique advantage in building AI systems that are capable of reasoning, adapting, and explaining their outputs. Here’s how knowledge graphs enhance AI:

• Explainable AI and Guardrails Knowledge graphs provide clear, interpretable relationships between data points, making it easier for users to trace and understand how AI systems arrive at their conclusions. Furthermore, knowledge graphs serve as an essential guardrail for AI models by grounding their predictions in structured, verified data, reducing the risk of hallucinations or misleading outputs.
• Contextual Understanding Unlike simple vector-based approaches, knowledge graphs model explicit relationships between data points, giving AI systems a richer, context-aware understanding. This contextual depth enables AI to make more nuanced and accurate predictions. The structured nature of knowledge graphs also plays a key role in Retrieval-Augmented Generation (RAG), where real-time context from knowledge graphs improves the accuracy and relevance of language model outputs.
• Cross-Domain Integration Knowledge graphs are excellent at integrating data from diverse sources—structured, semi-structured, and unstructured—into a unified representation. This holistic approach allows AI to draw meaningful insights from disparate data, enabling more comprehensive analysis and cross-domain understanding.
• Enhanced Search Capabilities By structuring relationships and entities, knowledge graphs enable AI to deliver more accurate and meaningful search results. Going beyond simple pattern matching or vector comparisons, knowledge graphs provide contextually rich, semantically relevant answers, greatly improving the search experience.
• Scalability Knowledge graphs provide a scalable way to organize and structure large datasets, allowing AI systems to process vast amounts of information without sacrificing context or accuracy. .

A knowledge graph typology

Implementing a knowledge graph might seem overwhelming at first, given the numerous ways to define, organize, and structure knowledge. The ideal approach depends on the nature of your data, the objectives you aim to achieve, and the specific use cases you wish to support.

Knowledge graphs vary widely in their structure and purpose, ranging from highly formalized, ontology-driven models to more flexible, dynamic graphs that evolve with changing data. Understanding these distinctions is crucial for selecting the right framework for your needs.

Let's explore three common types of knowledge graphs:

1. Taxonomy Knowledge Graphs - focused on language and semantic relationships,
2. Domain-Specific Knowledge Graphs - tailored to represent knowledge within a specific industry or field, and
3. Unified Knowledge Graphs - designed to unify and link data from multiple disparate sources.

Through these examples, we'll demonstrate how different types of knowledge graphs can power AI innovations in areas such as healthcare, finance, search, and personalized recommendations. By understanding these foundational categories, you'll be better equipped to design a knowledge graph that aligns with your goals and unlocks the full potential of your data.

Taxonomy knowledge graphs

A taxonomy knowledge graph organizes information hierarchically, with entities arranged as Parent → Child. The semantic of the relationship is usually has-a or includes, indicating that the parent concept encompasses or contains its child concepts.

For example a technology taxonomy may have a topic Artificial Intelligence which has a sub-topic Machine Learning, which has a sub-topic Neural Networks.

Many taxonomies exists for different domains, for example ESCO, the European multilingual classification of Skills, Competences and Occupations, defines a hierarchy of 13939 skills. One of them is “create a financial report”, found in the hierarchy

skills > information skills > documenting and recording information > preparing financial documents, records, reports, or budgets > create a financial report

Benefits for AI

Taxonomy knowledge graphs enhance AI by providing a clear hierarchical structure that enriches data with semantic context, improves classification accuracy, refines search results, supports natural language processing.

AI systems also use the fact that taxonomies implicitly support Inheritance of Properties: the child concept inherits the attributes or characteristics of the parent. For example, a product taxonomy may have a sub-category “Laptop” in a category “Electronics” which has the property that any Electronics “Requires electricity”. A system knowing that a product is related to “Laptop” sub-category, can infer that the product “requires electricity” by querying the relationships in the taxonomy knowledge graph.

Domain knowledge graphs

A domain knowledge graph represents knowledge specific to a particular field, capturing richer semantics than taxonomy graphs by modeling diverse, context-specific relationships such as part-of, knows, causes, or used-for. This enables a nuanced understanding of complex domains, allowing AI systems to reason, infer, and uncover insights beyond surface-level connections.

Here is an example of a knowledge graphs model used to explore donations to schools:

In healthcare, a domain knowledge graph could connects related entities such as diseases, symptoms, treatments, and risk factors. For example, "Diabetes" is linked to symptoms like "Fatigue" through a has-symptom relationship, and to "Insulin therapy" through a treated-by relationship. It can also highlight risk factors like "Obesity" using an increased-risk-by link.

By analyzing these connections, AI systems can assist in diagnosing illnesses, recommending treatments, and predicting potential health complications.

Benefits for AI

1. Contextual Reasoning

   By capturing intricate relationships, domain knowledge graphs enable AI systems to reason contextually, making more informed and accurate decisions.

2. Knowledge Integration

   Domain knowledge graphs unify diverse datasets, integrating structured data (such as databases) and unstructured data (such as research papers or reports) to create a holistic view of the domain.

3. Mitigating Hallucinations

   The precise relationships in domain knowledge graphs reduce AI errors by grounding outputs in verifiable, domain-specific facts, minimizing the likelihood of generating inaccurate or irrelevant information.

4. Complex Query Handling

   Domain knowledge graphs support sophisticated queries, such as “Which medications are effective for patients with both diabetes and hypertension?”—something traditional databases or simple taxonomies can't handle efficiently.

In summary, domain knowledge graphs extend the capabilities of AI by providing rich, domain-specific semantics. This enables deeper understanding, contextual reasoning, and reliable insights, making them essential for advanced AI apps in specialized fields.

Unified knowledge graphs

A unified knowledge graph unifies information from disparate data sources, creating a cohesive, comprehensive view of entities and their relationships. It connects structured, semi-structured, and unstructured data, breaking down silos. The power of unified knowledge graphs lies in their ability to add contextual layers through taxonomies or domain-specific ontologies, enriching the raw data with semantic meaning.

Unified knowledge graphs can be seen as an app of taxonomy or domain knowledge graphs. A unified knowledge graph can use a taxonomy but extends this by linking entities across different systems. For instance, a unified knowledge graph might categorize products in a taxonomy as "Electronics" → "Laptops" while also connecting these to customer data, transaction histories, or supplier details.

Similarly, unified knowledge graphs can leverage the rich semantics of domain knowledge graphs. For example, in healthcare, a unified knowledge graph might integrate patient records, clinical trial data, and medical literature. By using the structure and relationships of a domain knowledge graph, such as linking "Diabetes" to "Insulin therapy" or "Obesity" as a risk factor, the unified knowledge graph connects this information with external data sources like wearable device data or insurance claims, providing deeper insights.

Benefits for AI

A unified knowledge graph provides a holistic view of data by integrating information from diverse sources, offering AI systems a comprehensive understanding of entities and their relationships. This enables more accurate analysis, as the unified knowledge graph ensures that AI has access to a complete set of information, improving the precision of insights and decisions. Furthermore, the semantic structure of the unified knowledge graph, enhanced by taxonomies or ontologies, adds essential context to the data, enabling AI to make more nuanced predictions and reducing errors like hallucinations.

Conclusion

Knowledge graphs are a powerful tool for unlocking the potential of data, offering AI agents the ability to reason, adapt, and make more accurate, contextual decisions. By understanding the various types of knowledge graphs—taxonomy, domain, and unified—you can better tailor your AI apps to your data and use cases.

In the next post of this series, we'll dive into how to build knowledge graphs with Dgraph and use them in AI scenarios, leveraging Modus to create AI-powered APIs.

Start identifying knowledge graph use cases in your own projects and explore how Hypermode can help streamline your AI initiatives.

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