As enterprises accelerate AI adoption, the semantic layer has become essential for unifying siloed data and delivering actionable, contextualized insights. Graph analytics plays a pivotal role within this architecture, serving as the analytical engine that reveals patterns and relationships often missed by traditional data analysis approaches. By integrating metadata graphs, knowledge graphs, and analytics graphs, organizations can bridge disparate data sources and empower AI-driven decision-making. With recent technological advances in graph-based technologies, including knowledge graphs, property graphs, Graph Neural Networks (GNNs), and Large Language Models (LLMs), the semantic layer is evolving into a core enabler of intelligent, explainable, and business-ready insights

The Semantic Layer: Foundation for Connected Intelligence

A semantic layer acts as an enterprise-wide framework that standardizes data meaning across both structured and unstructured sources. Unlike traditional data fabrics, it integrates content, media, data, metadata, and domain knowledge through three main interconnected components:

1. Metadata Graphs capture the data about data. They track business, technical, and operational metadata – from data lineage and ownership to security classifications – and interconnect these descriptors across the organization. In practice, a metadata graph serves as a unified catalog or map of data assets, making it ideal for governance, compliance, and discovery use cases. For example, a bank might use a metadata graph to trace how customer data flows through dozens of systems, ensuring regulatory requirements are met and identifying duplicate or stale data assets.

2. Knowledge Graphs encode the business meaning and context of information. They integrate heterogeneous data (structured and unstructured) into an ontology-backed model of real-world entities (customers, accounts, products, and transactions) and the relationships between them. A knowledge graph serves as a semantic abstraction layer over enterprise data, where relationships are explicitly defined using standards like RDF/OWL for machine understanding. For example, a retailer might utilize a knowledge graph to map the relationships between sources of customer data to help define a “high-risk customer”. This model is essential for creating a common understanding of business concepts and for powering context-aware applications such as semantic search and question answering.

3. Analytics Graphs focus on connected data analysis. They are often implemented as property graphs (LPGs) and used to model relationships among data points to uncover patterns, trends, and anomalies. Analytics graphs enable data scientists to run sophisticated graph algorithms – from community detection and centrality to pathfinding and similarity – on complex networks of data that would be difficult to analyze in tables. Common use cases include fraud detection/prevention, customer influence networks, recommendation engines, and other link analysis scenarios. For instance, fraud analytics teams in financial institutions have found success using analytics graphs to detect suspicious patterns that traditional SQL queries missed. Analysts frequently use tools like Kuzu and Neo4J, which have built-in graph data science modules, to store and query these graphs at scale. In contrast, graph visualization tools (Linkurious and Hume) help analysts explore the relationships intuitively.

Together, these layers transform raw data into knowledge intelligence; read more about these types of graphs here.

Driving Insights with Graph Analytics: From Knowledge Representation to Knowledge Intelligence with the Semantic Layer

• Relationship Discovery
  Graph analytics reveals hidden, non-obvious connections that traditional relational analysis often misses. It leverages network topology, how entities relate across multiple hops, to uncover complex patterns. Graph algorithms like pathfinding, community detection, and centrality analysis can identify fraud rings, suspicious transaction loops, and intricate ownership chains through systematic relationship analysis. These patterns are often invisible when data is viewed in tables or queried without regard for structure. With a semantic layer, this discovery is not just technical, it enables the business to ask new types of questions and uncover previously inaccessible insights.
  
• Context-Aware Enrichment
  While raw data can be linked, it only becomes usable when placed in context. Graph analytics, when layered over a semantic foundation of ontologies and taxonomies, enables the enrichment of data assets with richer and more precise information. For example, multiple risk reports or policies can be semantically clustered and connected to related controls, stakeholders, and incidents. This process transforms disconnected documents and records into a cohesive knowledge base. With a semantic layer as its backbone, graph enrichment supports advanced capabilities such as faceted search, recommendation systems, and intelligent navigation.
  
• Dynamic Knowledge Integration
  Enterprise data landscapes evolve rapidly with new data sources, regulatory updates, and changing relationships that must be accounted for in real-time. Graph analytics supports this by enabling incremental and dynamic integration. Standards-based knowledge graphs (e.g., RDF/SPARQL) ensure portability and interoperability, while graph platforms support real-time updates and streaming analytics. This flexibility makes the semantic layer resilient, future-proof, and always current. These traits are crucial in high-stakes environments like financial services, where outdated insights can lead to risk exposure or compliance failure.
  

These mechanisms, when combined, elevate the semantic layer from knowledge representation to a knowledge intelligence engine for insight generation. Graph analytics not only helps interpret the structure of knowledge but also allows AI models and human users alike to reason across it.

Business Impact and Case Studies

Enterprise Knowledge’s implementations demonstrate how organizations leverage graph analytics within semantic layers to solve complex business challenges. Below are three real-world examples from their case studies:
1. Global Investment Firm: Unified Knowledge Portal

A global investment firm managing over $250 billion in assets faced siloed information across 12+ systems, including CRM platforms, research repositories, and external data sources. Analysts wasted hours manually piecing together insights for mergers and acquisitions (M&A) due diligence.

Enterprise Knowledge designed and deployed a semantic layer-powered knowledge portal featuring:

• A knowledge graph integrating structured and unstructured data (research reports, market data, expert insights)
• Taxonomy-driven semantic search with auto-tagging of key entities (companies, industries, geographies)
• Graph analytics to map relationships between investment targets, stakeholders, and market trends

Results

• Single source of truth for 50,000+ employees, reducing redundant data entry
• Accelerated M&A analysis through graph visualization of ownership structures and competitor linkages
• AI-ready foundation for advanced use cases like predictive market trend modeling

2. Insurance Fraud Detection: Graph Link Analysis

A national insurance regulator struggled to detect synthetic identity fraud, where bad actors slightly alter personal details (e.g., “John Doe” vs “Jon Doh”) across multiple claims. Traditional relational databases failed to surface these subtle connections.

Enterprise Knowledge designed a graph-powered semantic layer with the following features:

• Property graph database modeling claimants, policies, and claim details as interconnected nodes/edges
• Link analysis algorithms (Jaccard similarity, community detection) to identify fraud rings
• Centrality metrics highlighting high-risk networks based on claim frequency and payout patterns

Results

• Improved detection of complex fraud schemes through relationship pattern analysis
• Dynamic risk scoring of claims based on graph-derived connection strength
• Explainable AI outputs via graph visualizations for investigator collaboration

3. Government Linked Data Investigations: Semantic Layer Strategy

A government agency investigating cross-border crimes needed to connect fragmented data from inspection reports, vehicle registrations, and suspect databases. Analysts manually tracked connections using spreadsheets, leading to missed patterns and delayed cases.

Enterprise Knowledge delivered a semantic layer solution featuring:

• Entity resolution to reconcile inconsistent naming conventions across systems
• Investigative knowledge graph linking people, vehicles, locations, and events
• Graph analytics dashboard with pathfinding algorithms to surface hidden relationships

Results

• 30% faster case resolution through automated relationship mapping
• Reduced cognitive load with graph visualizations replacing manual correlation
• Scalable framework for integrating new data sources without schema changes
  

Implementation Best Practices

Enterprise Knowledge’s methodology emphasizes several critical success factors :

1. Standardize with Semantics
Establishing a shared semantic foundation through reusable ontologies, taxonomies, and controlled vocabularies ensures consistency and scalability across domains, departments, and systems. Standardized semantic models enhance data alignment, minimize ambiguity, and facilitate long-term knowledge integration. This practice is critical when linking diverse data sources or enabling federated analysis across heterogeneous environments.

2. Ground Analytics in Knowledge Graphs
Analytics graphs risk misinterpretation when created without proper ontological context. Enterprise Knowledge’s approach involves collaboration with intelligence subject matter experts to develop and implement ontology and taxonomy designs that map to Common Core Ontologies for a standard, interoperable foundation.

3. Adopt Phased Implementation
Enterprise Knowledge develops iterative implementation plans to scale foundational data models and architecture components, unlocking incremental technical capabilities. EK’s methodology includes identifying starter pilot activities, defining success criteria, and outlining necessary roles and skill sets.

4. Optimize for Hybrid Workloads
Recent research on Semantic Property Graph (SPG) architectures demonstrates how to combine RDF reasoning with the performance of property graphs, enabling efficient hybrid workloads. Enterprise Knowledge advises on bridging RDF and LPG formats to enable seamless data integration and interoperability while maintaining semantic standards.

Conclusion

The semantic layer achieves transformative impact when metadata graphs, knowledge graphs, and analytics graphs operate as interconnected layers within a unified architecture. Enterprise Knowledge’s implementations demonstrate that organizations adopting this triad architecture achieve accelerated decision-making in complex scenarios. By treating these components as interdependent rather than isolated tools, businesses transform static data into dynamic, context-rich intelligence.

Graph analytics is not a standalone tool but the analytical core of the semantic layer. Grounded in robust knowledge graphs and aligned with strategic goals, it unlocks hidden value in connected data. In essence, the semantic layer, when coupled with graph analytics, becomes the central knowledge intelligence engine of modern data-driven organizations.
If your organization is interested in developing a graph solution or implementing a semantic layer, contact us today!

The post Graph Analytics in the Semantic Layer: Architectural Framework for Knowledge Intelligence appeared first on Enterprise Knowledge.

Graph machine learning (Graph ML) offers a new paradigm for handling the complexities of real-world data, which often exists in interconnected networks. For example, Graph ML can be leveraged to build highly effective recommender systems, to identify critical connections and enhance decision-making. Unlike traditional ML, which treats data as independent observations, Graph ML captures the interactions and connections between data points, revealing patterns that are invisible to traditional methods. Recognizing the pivotal role of graph technologies in driving innovation across data analytics, data professionals are increasingly optimizing their workflows to harness these powerful tools. But why should data professionals care about Graph ML? By understanding these differences and leveraging graph structures, data professionals can unlock new predictive capabilities that were previously out of reach. Whether you’re aiming to enhance fraud detection, optimize recommendation systems, or improve social network analysis, Graph ML is an increasingly valuable tool in the data scientist’s toolkit.

In this blog, we will explore the unique advantages that Graph ML offers over traditional approaches. We’ll dive into graph-specific considerations throughout each step of the machine learning process, from pre-processing to model evaluation, and provide expert advice for effectively integrating graph techniques into your ML workflow. While you can use standard machine learning processes to answer simple use cases and scenarios such as image classification, basic customer churn prediction, or straightforward regression analysis–graph machine learning allows you to tackle richer, network-driven scenarios, including fraud detection through network anomaly patterns, sophisticated recommendation engines built on user-item graphs, and social network influence analysis. If you haven’t yet built a graph for your organization, here are the high-level steps: identify the entities and relationships within your use-case, build a graph schema, and load your data into a graph database. For more in-depth guidance, see this detailed guide on developing an enterprise-level graph. This process often includes breaking down your data into triples (subject-predicate-object) and representing the connections between nodes through methods like adjacency matrices, embeddings, or random walks.

Understanding the ML Development Lifecycle

Take a moment to review the ML development lifecycle wheel above. The wheel is divided into five distinct sections: Pre-Processing, Train-Test Split, Model Training, Model Evaluation, and Document and Iterate. Below, we start with Pre-Processing, where we transform raw data into a graph structure, extract critical graph features, and apply compression techniques to manage complexity. Each subsequent section will build on these foundations by detailing the specific approaches and methodologies used in Train-Test Split, Model Training, and Model Evaluation. The wheel serves as our roadmap for understanding how Graph Machine Learning allows for deeper insights from complex, interconnected data.

Step 1: Pre-Processing

Graph Conversion

Business Value: In traditional ML, raw data is processed as independent feature vectors–meaning models often miss the relationships between entities and can’t leverage network effects. In contrast, graph conversion allows for the systemic mapping of raw data into a structured network of entities and relationships, revealing new insights and perspectives.

The first step in Pre-Processing is graph conversion. Graph conversion is the process of transforming unstructured, semi-structured, or structured data into a graph model where individual entities become nodes and the connections, or relationships, between them are represented as edges. This conversion lays the groundwork for advanced graph analysis by explicitly modeling the relationships within the data, rather than leaving all of the connections to be inferred.

This foundational graph conversion not only organizes the raw data into a clear structure but also enables the extrapolation of clusters, central nodes, and intricate, multi-hop relationships. This structured representation not only enhances the clarity of data analysis, but also establishes a foundation for scalable predictive modeling and clearer understanding of intricate linkages. This base sets the stage for the next step of Pre-Processing: Graph Feature Extraction.

Graph Feature Extraction

Business Value: Conventional feature extraction methods treat each data point in isolation, often missing how entities connect in a network. Graph features capture both individual data attributes and relational patterns, allowing models to assess influence, connectivity, and community dynamics, providing a richer context compared to traditional feature extraction.

Graph-specific feature extraction captures not only individual data point attributes but also the relationships and structural patterns between data points that traditional methods miss. Graph features, such as Degree Centrality and Betweenness Centrality, reveal the importance of a node within the overall network, allowing models to predict how influential or well-connected an entity is in relation to others. 

Features like PageRank Scores help in ranking nodes based on their connectivity and importance, making them especially useful in recommendation systems and fraud detection, where influence and connectivity play a key role. Clusters and community detection features capture groups of interconnected nodes, enabling tasks like identifying suspicious behavior within certain groups or detecting communities in social networks. These rich, interconnected features allow Graph ML models to make predictions based on the broader context, not just isolated points, giving them a deeper understanding of the data’s inherent relationships. This comprehensive feature extraction naturally leads into the next step in Pre-Processing: Compression, where we streamline the data while preserving these critical relational insights.

Compression

Business Value: Graph compression preserves structural relationships while reducing complexity, enabling efficient analysis without sacrificing key insights embedded in the graph’s intricate connections.

Compression is used to reduce the size, complexity, and redundancy of a graph while ensuring its structure and information are preserved. In traditional ML, dimensionality reduction methods like PCA or feature selection are used to reduce data complexity, but these methods overlook the relational structure between entities. In contrast, graph compression techniques, such as node embeddings, graph pruning, and adjacency matrix compression, preserve the graph’s inherent connections and patterns while simplifying the data. Node embeddings, in particular, are a powerful way to represent nodes as feature-rich vectors, capturing both the attributes of a node and its relational context within the graph. 

Compression is an essential step in Graph ML because graphs often contain far more intricate details about the relationships between entities, which require high computing power to analyze. Compression helps reduce the noise and irrelevant connections that can distract the model, allowing it to focus on the most critical relationships within the graph. This ability to retain the most meaningful structural information while reducing computational overhead gives Graph ML an edge over traditional methods, which may lose key insights during dimensionality reduction. With Compression, the Pre-Processing phase is complete, setting a clear and efficient foundation as we move into Step 2: Train-Test Split.

Step 2: Train-Test Split

Subgraph Sampling

Business Value: Basic train-test splitting methods sample instances without regard to connectivity, which can sever critical network links, so subgraph sampling ensures the test set reflects the overall graph structure, allowing models to learn and generalize from complex relationships present in real-world data.

Subgraph sampling is an essential part of graph machine learning, as it ensures the test set is representative of the entire graph structure by sampling subgraphs that reflect the entities and relationships in the overall graph. In traditional ML, splitting data into training and test sets is straightforward because data points are often independent, but graph data introduces dependencies between nodes. Complex graph data captures interconnected relationships such as communities, hierarchies, and long-range dependencies that traditional models would overlook. Subgraph sampling preserves these relationships, enabling the model to learn from the complex structures and generalize better to unseen data. By capturing these dependencies in the train-test split, the model maintains a more complete understanding of how entities interact, allowing it to make better predictions in cases where the relationships between data points are key, such as social network analysis or fraud detection. This careful sampling also highlights the need to address potential overlaps in relationships, which leads us into the next critical consideration: Link Leakage.

Link Leakage

Business Value: Random or node-based splitting can place connected nodes across sets, allowing information to leak via edges. Edge-based splitting prevents information leakage between training and test sets, preserving the integrity of graph relationships and delivering reliable, unbiased predictions.

Link leakage occurs when connections between nodes in the training data indirectly reveal information about the test data. Traditional ML doesn’t face this issue because data points are independent, but in graph ML, relationships between nodes can lead to unintended overlap between the training and test sets. To mitigate this, consider splitting the data by edges to ensure that the test set remains independent of the training set’s connections. Splitting on edges maintains the graph’s inherent relational information, which is a crucial advantage of graph data. This method allows the model to learn from the complex interdependencies within the graph, leading to more accurate predictions in real-world applications like fraud detection or recommendation systems. It also helps avoid biases that may arise from overlapping connections, enhancing the overall reliability of the model. This edge-based approach is vital in graph ML, where traditional methods fall short in addressing these complex dependencies. With a robust solution for link leakage in place, we are now ready to transition into the next major phase: Step 3, Model Training.

Step 3: Model Training

Business Value: Conventional ML models treat instances independently and can’t model dependencies across entities, so graph-specific algorithms capture complex dependencies and relationships that traditional ML models often overlook, enabling deeper insights and more accurate predictions for tasks driven by connections.

Using algorithms designed specifically for graph data allows you to fully leverage the unique relationships and structures present in graph data, such as the connections between nodes, the importance of specific relationships, and the overall topology of the graph. Traditional ML models, such as decision trees or linear regression, assume that data points are independent and often fail to capture complex dependencies. In contrast, graph algorithms—like node classification, edge prediction, community detection, and anomaly detection—are built to capture the interdependencies between nodes, edges, and their neighbors. These algorithms can uncover patterns and dependencies that are hidden from traditional approaches, such as identifying key influencers in a network or detecting anomalies based on unusual connections between entities.

By utilizing graph algorithms, you can gain deeper insights and make more accurate predictions, especially in tasks where relationships between entities play a critical role, such as fraud detection, recommendation systems, or social network analysis. These insights, driven by the relational data that graph models are designed to exploit, give graph ML a clear advantage when interactions between entities drive outcomes. Following model training, it is essential to evaluate the performance of these specialized models.

Step 4: Model Evaluation

Business Value: Standard evaluation metrics measure prediction independently and ignore graph structure. However, graph-specific metrics offer a more nuanced assessment of graph model performance, capturing structural relationships that traditional metrics overlook.

While common performance metrics apply broadly to most graph ML use cases, there are also specialized metrics for graph ML—such as Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Modularity. Traditional ML evaluation metrics like accuracy or F1-score work well for independent data points, but they don’t fully capture the nuances of graph structures, such as community detection or link prediction. Graph-specific performance metrics provide a more nuanced evaluation of models, ensuring that the unique aspects of graph structures are effectively measured and optimized. When you are evaluating a graph ML model, you are able to determine model performance with enhanced structural awareness, contextual evaluation, and imbalanced data handling—areas where traditional ML metrics often fall short.

Graph ML Solution Components

To implement Graph ML successfully, an organization needs a cohesive set of features that support the entire graph workflow. At a minimum, you must have: 

(1) a scalable graph storage layer that can ingest and index heterogeneous data sources (including batch and streaming updates) while enforcing a flexible schema;

(2) a pre-processing engine capable of automatically extracting and managing entity and relationship attributes (e.g., generating node and edge-level features); 

(3) integrated support for generating and storing graph embeddings and/or handcrafted graph features (such as centrality scores, community assignments, or path-based statistics);

(4) a library of graph algorithms and GNN (Graph Neural Network) frameworks that can train on large-scale graphs, ideally with GPU-acceleration and distributed compute options;

(5) real-time inference capabilities that preserve graph connectivity (so predictions like link-forecasting or node classification remain aware of the surrounding network);

(6) visualization and exploration tools that let data teams inspect subgraphs, feature distributions, and model explainability outputs; and

(7) robust security, access controls, and lineage tracking to ensure data governance across the graph pipeline.

Case Studies – Adapt and Overcome

Bioscience Technology Provider Case Study 

With the methodology now established, let’s take a look at a real-world situation. A leading bioscience technology provider’s e-commerce platform struggled to connect its 70,000 products and related educational content–each siloed across more than five different systems–using only keyword search, so we applied the same GraphML workflow outlined above to bridge those gaps. We ingested data from their various platforms into an in-memory knowledge graph, generated vector embeddings to capture content relationships, and trained a custom link-prediction model (sampling known product-content links rather than enforcing link-leakage controls) to infer new connections. The resulting similarity-index and link-classifier views were delivered via an interactive dashboard, validated through human-in-the-loop sessions, and backed by comprehensive documentation and a repeatable AI validation framework. While we skipped graph-specific metrics (favoring standard ML measure like AUC, precision, and recall) to accelerate delivery, this guideline-driven approach demonstrates how the techniques in this blog can be pragmatically adapted to real-world constraints.

Occupational Safety Government Agency

Another applied use case revolves around an occupational safety government agency. Enterprise Knowledge prototyped a semantic recommender that could infer potential workplace hazards from diverse site features–taxonomy values, structured historical datasets, and thousands of unstructured incident reports–so planners could rapidly assess risks and compliance requirements. We began by designing a custom taxonomy and ontology to model construction site elements and regulations, then processed data with zero-shot NLP on a distributed GPU cluster and loaded everything into an RDF knowledge graph. From there we generated vector embeddings and trained a custom edge-prediction classifier to link scenarios to likely risks, deploying the results in a cloud-hosted web app. Guided by performance metrics on a held-out test set, each step was iteratively refined through user feedback and expert workshops. EK maintained continuous collaboration with agency experts through regular design sessions and concluded with a detailed technical walkthrough to ensure transparency and client buy-in. Backed by detailed documentation and a clear roadmap for expanding feedback loops and analysis dimensions, this solution underscores that the GraphML lifecycle is a flexible framework–teams should tailor or simplify steps as needed to align with real-world constraints like timelines, data availability, and resource limits.

Conclusion

Graph machine learning offers a transformative approach to working with complex, interconnected datasets. By leveraging graph-specific techniques like feature extraction, compression, and graph algorithms, you can unlock deeper insights that go beyond what traditional ML can achieve. Whether it’s in community detection, fraud prevention, or recommendation systems, graph ML provides a way to model relationships and structures that traditional methods often miss. As we move toward a future where graph technologies are increasingly integrated into data workflows, it’s clear that understanding and applying these methods can lead to more accurate predictions, better decision-making, and ultimately, a competitive edge. If you’re interested in unlocking the potential of graph ML for your organization, contact EK to learn more!

The post Beyond Traditional Machine Learning: Unlocking the Power of Graph Machine Learning appeared first on Enterprise Knowledge.

Taxonomy for the Semantic Layer

Taxonomies are controlled vocabularies that organize terms or concepts into a hierarchy based on their relationships, serving as key knowledge organization systems within the semantic layer to promote consistent naming conventions and a common understanding of business concepts. Categorizing concepts in a structured and meaningful format via hierarchy clarifies the relationships between terms and enriches their semantic context, streamlining the navigation, findability, and retrieval of information across systems.

Taxonomies are often a foundational component in RDF-based graph development used to structure and classify data for more effective inference and reasoning. As graph technologies evolve, the application of taxonomy is gaining relevance beyond RDF, particularly in the realm of LPGs, where it can play a crucial role in data classification and connectivity for more flexible, scalable, and dynamic graph analytics.

The Role of Taxonomy in LPGs

Even in the flexible world of LPGs, taxonomies help introduce a layer of semantic structure that promotes clarity and consistency for enriching graph analytics:

Taxonomy Labels for Semantic Standardization

Taxonomy offers consistency in how node and edge properties in LPGs are defined and interpreted across diverse data sources. These standardized vocabularies align labels for properties like roles, categories, or statuses to ensure consistent classification across the graph. Taxonomies in LPGs can dynamically evolve alongside the graph structure, serving as flexible reference frameworks that adapt to shifting terminology and heterogeneous data sources. 

For instance, a professional networking graph may encounter job titles like “HR Manager,” “HR Director,” or “Human Resources Lead.” As new titles emerge or organizational structures change, a controlled job title taxonomy can be updated and applied dynamically, mapping these variations to a preferred label (e.g., “Human Resources Professional”) without requiring schema changes. This enables ongoing accurate grouping, querying, and analysis. This taxonomy-based standardization is foundational for maintaining clarity in LPG-driven analytics.

Taxonomy as Reference Data Modeled in an LPG

LPGs can also embed taxonomies directly as part of the graph itself by modeling them as nodes and edges representing category hierarchies (e.g. for job roles or product types). This approach enriches analytics by treating taxonomies as first-class citizens in the graph, enabling semantic traversal, contextual queries, and dynamic aggregation. For example, consider a retail graph that includes a product taxonomy: “Electronics” → “Laptops” → “Gaming Laptops.” When these categories are modeled as nodes, individual product nodes can link directly to the appropriate taxonomy node. This allows analysts to traverse the category hierarchy, aggregate metrics at different abstraction levels, or infer contextual similarity based on proximity within the taxonomy. 

EK is currently leveraging this approach with an intelligence agency developing an LPG-based graph analytics solution for criminal investigations. This solution requires consistent data classification and linkage for their analysts to effectively aggregate and analyze criminal network data. Taxonomy nodes in the graph, representing types of roles, events, locations, goods, and other categorical data involved in criminal investigations, facilitate graph traversal and analytics.

In contrast to flat property tags or external lookups, embedding taxonomies within the graph enables LPGs to perform classification-aware analysis through native graph traversal, avoiding reliance on fixed, rigid rules. This flexibility is especially important for LPGs, where structure evolves dynamically and can vary across datasets. Taxonomies provide a consistent, adaptable way to maintain meaningful organization without sacrificing flexibility.

Taxonomy in the Context of LPG-Driven Analytics Use Cases

Taxonomies introduce greater structure and clarity for dynamic categorization of complex, interconnected data. The flexibility of taxonomies for LPGs is particularly useful for graph analytics-based use cases, such as recommendation engines, network analysis for fraud detection, and supply chain analytics.

For recommendation engines in the retail space, clear taxonomy categories such as product type, user interest, or brand preference enable an LPG to map interactions between users and products for advanced and adaptive analysis of preferences and trends. These taxonomies can evolve dynamically as new product types or user segments emerge for more accurate recommendations in real-time. In fraud detection for financial domains, LPG nodes representing financial transactions can have properties that specify the fraud risk level or transaction type based on a predefined taxonomy. With risk level classifications, the graph can be searched more efficiently to detect suspicious activities and emerging fraud patterns. For supply chain analysis, applying taxonomies such as region, product type, or shipment status to entities like suppliers or products allows for flexible grouping that can better accommodate evolving product ranges, supplier networks, and logistical operations. This adaptability makes it possible to identify supply chain bottlenecks, optimize routing, and detect emerging risks with greater accuracy.

Conclusion

By incorporating taxonomy in Labeled Property Graphs, organizations can leverage structure while retaining flexibility, making the graph both scalable and adaptive to complex business requirements. This combination of taxonomy-driven classification and the dynamic nature of LPGs provides a powerful semantic foundation for graph analytics applications across industries. Contact EK to learn more about incorporating taxonomy into LPG development to enrich your graph analytics applications.

The post The Role of Taxonomy in Labeled Property Graphs (LPGs) & Graph Analytics appeared first on Enterprise Knowledge.

The Challenge

Technology is increasingly used as both a force for good and as a means to exploit vulnerabilities that greatly damage organizations – whether financially, reputationally, or through the release of classified information. Consequently, efforts to combat this fraud must evolve to become more sophisticated with each passing day. The field of fraud analytics is rapidly emerging and, over the past 10 years, has expanded to include graph analytics as a critical method for detecting suspicious behavior. 

In one such application, a national agency overseeing insurance claims engaged EK to advise on developing and implementing graph-based analytics to support fraud detection. The agency had a capable team of data scientists, program analysts, and engineers focused on identifying suspicious activity among insurance claims, such as:

• Personal information being reused across multiple claims;
• Claims being filed under the identities of deceased individuals; or
• Individuals claiming insurance from multiple locations. 

However, they were reliant on relational databases to accomplish these tasks. This made it difficult for program analysts to identify subtle connections between records in tabular format, with data points often differing by just a single digit or character. Additionally, while the organization was effective at flagging anomalies and detecting potentially suspicious behavior, they faced challenges relating to legacy software applications and limited traditional data analytics processes. 

EK was engaged to provide the agency with guidance on standing up graph capabilities. This graph-based solution would transform claim information into interconnected nodes, revealing hidden relationships and patterns among potentially fraudulent claims. In addition, EK was asked to build the agency’s internal expertise in graph analytics by sharing the methods and processes required to uncover deeper, previously undetectable patterns of suspicious behavior.

The Solution

To design a solution suitable for the agency’s production environment, EK began by assessing the client’s existing data infrastructure and analytical capabilities. Their initial cloud solution featured a relational database, which EK suggested extending with a graph database through the same cloud computing platform vendor for easy integration. Additionally, to identify suspicious connections between claims in a visual format, EK recommended an approach for the agency to select and integrate a link analysis visualization tool. These tools are crucial to a link analysis solution and allow for the graphical visualization of entities alongside behavior detection features that identify data anomalies, such as timeline views of relationship formation. EK made this recommendation using a custom and proprietary tooling evaluation matrix that facilitates informed decision-making based on a client’s priority factors. Once the requisite link analysis components were identified, EK delivered a solution architecture with advanced graph machine learning functionality and an intuitive user experience that promoted widespread adoption among technical and nontechnical stakeholders alike.

EK also assessed the agency’s baseline understanding of graphical link analysis and developed a plan for upskilling existing data scientists and program analysts on the foundations of link analysis. Through a series of primer sessions, EK’s subject matter experts introduced key concepts such as knowledge graphs, graph-based link analysis for detecting potentially suspicious behavior, and the underlying technology architecture required to instantiate a fully functional solution at the agency.

Finally, EK applied our link analysis experience to address client challenges by laying out a roadmap and implementation plan that detailed challenges along with proposed solutions to overcome them. This took the form of 24 separate recommendations and the delivery of bespoke materials meant to serve as quick-start guides for client reference.

The EK Difference

A standout feature of this project is its novel, generalizable technical architecture:

During the course of the engagement, EK relied on its deep expertise in unique domains such as knowledge graph design, cloud-based SaaS architecture, graph analytics, and graph machine learning to propose an easily implementable solution. To support this goal, EK developed an architecture recommendation that prompted as few modifications to existing programs and processes as possible. With the proposed novel architecture utilizing the same cloud platform that already hosted client data, the agency could implement the solution in production with minimal effort.

Furthermore, EK adapted a link analysis maturity benchmark and tool evaluation matrix to meet the agency’s needs and ensure that all solutions were aligned with the agency’s goal. Recognizing that no two clients face identical challenges, EK delivered a customized suite of recommendations and supporting materials that directly addressed the agency’s priorities, constraints, and long-term needs for scale.

The Results

Through this engagement, EK provided the agency with the expertise and tools necessary to begin constructing a production-ready solution that will:

• Instantiate claims information into a knowledge graph;
• Allow users to graphically explore suspicious links and claims through intuitive, no-code visualizations;
• Alert partner agencies and fraud professionals to suspicious activity using graph-based machine learning algorithms; and
• Track changes in data over time by viewing claims through a temporal lens.

In parallel, key agency stakeholders gained practical skills related to knowledge graphs, link analysis, and suspicious behavior detection using graph algorithms and machine learning, significantly enhancing their ability to address complex insurance fraud cases and support partner agency enforcement efforts.

Interested in strengthening your organization’s fraud detection capabilities? Want to learn what graph analytics can do for you? Contact us today!

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