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.

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Semantic Layer Operating Model – The Components

Establishing a clear operating model is essential for translating the vision of a semantic layer into a practical, scalable reality. It enables teams to deliver value incrementally, define release scopes that are both feasible and impactful, and build repeatable frameworks that support consistent expansion over time. This also ensures the implementation work connects to the broader product vision and semantic layer strategy for an organization so that each decision directly contributes to long-term goals. 

At EK, we often structure our semantic layer operating model around two primary components that form the foundation of an MVP release: design releases and development releases. EK is currently putting these components into practice with a government agency in the intelligence analysis space as we transition from the definition of a semantic layer strategy into an MVP implementation phase for a user-facing semantic solution. Delivery is broken into iterative cycles that combine use case and data model expansion with user feature and service development. To ensure alignment across teams, it is critical to not only define the scope and content of these releases, but also establish a shared language for describing them and expected timeframes of completion. In collaboration with the agency, EK defined “use cases” as 1-month units of design releases, “pilots” as staggered 1-month units of development releases, and “MVP” as the culminating technical release that integrates multiple use cases and pilots into a cohesive solution over the course of 6 months.

Sample Structure for Semantic Layer Operating Model

It is important to note to that these are not hard and fast rules for what should constitute your semantic layer operating model, as components and timeframes are shaped by team structures and capacity, existing tooling and/or procurement timelines, business expectations for solution releases, and other organization or solution-specific requirements. The following definitions can serve as a guide to tailor an operating model that best fits your needs and constraints.

Design Release

Design releases focus on the strategic and conceptual definition of a semantic layer use case. They are the foundation for implementation, ensuring that each increment is grounded in clear user needs, meaningful data connections, and well-scoped schema design. A design release captures a focused slice of the broader vision, allowing for thoughtful expansion while maintaining alignment with the overall product vision and semantic layer strategy.

For example, a semantic layer design release or “use case” for a knowledge graph-based solution should:

Design releases help teams align on scope and semantic modeling needs, surface technical dependencies early, and create a shared understanding of the solution design priorities to enable technical development.

Development Release

Development releases translate design outputs into working technical components. These releases prioritize feasibility, rapid iteration, and incremental value, while maintaining a clear path toward scalability. Development releases often begin with limited-scope pilots that validate capability approaches and inform future automation and scale.

A semantic layer development release or “pilot” for a graph-based solution should:

Development releases help demonstrate tangible progress and mitigate risks for enterprise-level implementation to build towards the encompassing product vision in alignment with an organization’s semantic layer strategy.

MVP Release

The below image illustrates how design and development releases work together, with a generalized example from EK’s semantic layer implementation strategy with the government agency focused on intelligence analysis.

Sample Design and Development Release Scopes

Ultimately, these iterative design and development releases culminate in an MVP: a fully integrated release of the semantic layer-based solution that brings together multiple completed use cases and pilots into a unified, usable platform. It includes the implemented semantic models, integrated data sources, and functional technical components necessary to support a robust set of targeted user capabilities, and is ready for use in a broader context within the organization. The MVP manifests core elements of the product vision, demonstrating the full value of incremental delivery and providing a clear path for business adoption, continuous expansion, and long-term scalability of the solution as part of the semantic layer.

Conclusion

Establishing a well-defined operating model is critical for successfully developing and scaling a semantic layer solution. By structuring work around design and development releases, organizations can maintain clear alignment between technical implementation, business needs, and product strategy. This model enables teams to deliver incremental value, iterate based on lessons learned, and lay the groundwork for scalable, long-term solutions that drive more connected decision-making across the enterprise. Contact EK to learn more about defining a solution strategy and structuring an operating model for implementing a semantic layer at your organization.

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While an enterprise knowledge graph encompasses all data entities and actual data instances across an organization, a metadata knowledge graph specifically focuses on gathering and managing metadata, or information about data entities, attributes, relationships, and their context. Aggregating and surfacing contextual information in the form of metadata – from what data is collected to how it is consumed – ultimately supports data accessibility, findability, traceability, connectivity, governance, and understanding across the enterprise. In this blog, I will detail the kind of metadata that is useful to capture in a metadata knowledge graph and how leveraging this tool enhances an organization’s ability to extract value from their enterprise data.

Understanding the Metadata Knowledge Graph

A metadata knowledge graph acts as a unifying layer, integrating different types of data from various sources and capturing rich, descriptive metadata to create a holistic view of an organization’s knowledge ecosystem. For datasets in use across the enterprise, we can associate contextual metadata regarding data sourcing, ownership, versioning, access levels and entitlements, consuming systems and applications, and other relevant details in a single location. For example, EK is advising a global financial institution on modeling an extensible metadata knowledge graph as part of their semantic layer-driven data strategy. The firm’s metadata knowledge graph captures provenance and usage information for their numerous taxonomies, ontologies, knowledge graphs, and related data assets to enhance data awareness and understanding for regulatory compliance and reporting needs.

The kinds of metadata captured in a metadata knowledge graph commonly fall under the following high-level types:

• Business metadata gives data meaning in the context of an organization and its knowledge domain, as it includes information such as the business definitions of data elements, business rules that govern the data, and relationships between datasets. Capturing this metadata is key for understanding the business-specific context of data and how data should be leveraged to address business needs and use cases.
  • For the financial firm, their key business metadata includes names and descriptions or purpose statements for their published taxonomy and ontology models, as well as which business areas or systems currently manage and/or consume these models. This helps the firm understand the business context of what data is being used and shared across the organization.
• Technical metadata is information on technical data characteristics, such as data format, type, structure, and location. Technical metadata is important for data management and integration processes to enhance data accessibility and interoperability.
  • The firm’s metadata knowledge graph captures extensive technical metadata related to their semantic models, such as API details for integrating particular taxonomy data in various contexts. This facilitates data aggregation for their regulatory risk assessment and reporting processes.
• Operational metadata is information about how, when, and where data is created, processed, accessed, stored, and transformed. This metadata is leveraged to monitor the flow of data and maintain appropriate levels of data quality, consistency, and accessibility in support of effective data governance and change management practices.  
  • The financial firm’s metadata knowledge graph gathers operational metadata primarily in support of change management and governance needs. This metadata includes model versioning information, details on deprecated taxonomy terms or ontology classes, entitlement restrictions, and other usage and data quality metrics.

The Value of a Metadata Knowledge Graph

Building a metadata knowledge graph simplifies and streamlines data and metadata management practices. By capturing relevant business, technical, and operational metadata of an organization’s data assets, end users can quickly identify and leverage datasets relevant to their day-to-day needs or broader use cases with a deeper understanding of business context. Collecting comprehensive and consistent metadata in a metadata knowledge graph offers the following key benefits to empower data-driven processes and decision-making at all levels of an organization:

Upon implementation, metadata knowledge graphs can power a wide range of applications, such as dashboards and visualization tools, semantic search, and reporting and analytics platforms. These tools enable intuitive user interactions with enterprise data, streamlining abilities to surface, relate, and aggregate siloed information. This reduces burdensome manual efforts in managing disparate data and provides users a holistic view of information assets to answer key business questions. EK supported the financial firm in modeling their metadata knowledge graph to enable semantic search and discovery across their enterprise data models with a single user interface. This offers a user-friendly, centralized approach for firm employees to quickly locate, understand, and connect information for complex financial regulatory reporting at their time of need. 

Conclusion

By consolidating business, technical, and operational metadata into a unified framework with a metadata knowledge graph, organizations can enhance data findability and accessibility, foster a deeper understanding of business data assets, promote alignment with data governance practices, and facilitate seamless data integration across diverse systems. The metadata knowledge graph empowers organizations to eliminate data silos and leverage their data assets more effectively for consistent knowledge sharing and context-driven decision-making across the enterprise. Contact EK to learn more about modeling and implementing a metadata knowledge graph for your semantic layer.

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Understanding the Semantic Layer

The semantic layer is a standardized framework that serves as a bridge between raw data and user-facing applications by organizing, abstracting, and connecting data and knowledge from structured, unstructured, and semi-structured formats. It encompasses components such as taxonomies, ontologies, business glossaries, knowledge graphs, and related tooling to provide organizations with a unified and contextualized view of their data and information. This enables intuitive user interactions and empowers analysis and decision-making informed by business context. 

AI Techniques in the Semantic Layer

The following AI techniques are useful tools for the curation of semantic layer input and powering downstream applications:

1. Named Entity Recognition

Named entity recognition (NER) is a natural language processing (NLP) technique that involves identifying and categorizing entities within text, such as people, organizations, or locations. By leveraging NER, organizations can automate the extraction process of common entities from large amounts of unstructured textual data to quickly identify key information. Identifying, extracting, and labeling common entities consistently across different datasets streamlines the normalization of data in varied formats from disparate sources for seamless data integration to the semantic layer. These enriched semantic representations of data enable organizations to connect information and surface contextual insights from vast amounts of complex data. 

For a federally funded engineering research center, EK leveraged taxonomy and ontology models to automatically extract key entities from a vast repository of unstructured documents and add structured metadata, ultimately building an enterprise knowledge graph for the organization’s semantic layer. This supported a semantic search platform for users to conduct natural language searches and intuitively browse and navigate through documents by key entities such as person, project, and topic, reducing time spent searching from several days to 5 minutes.

2. Clustering and Similarity Algorithms

Clustering algorithms (e.g., K-means, DBSCAN) partition datasets by creating distinct groups of similar objects to identify patterns and find commonalities between unlabeled or uncategorized data elements, whereas similarity algorithms (e.g., cosine similarity, Euclidean distance, Jaccard similarity) are used to measure the similarity or dissimilarity between two objects or sets of objects. Clustering and similarity algorithms are crucial in various semantic layer-driven use cases and applications, such as chatbots, recommendation engines, and semantic search.

As part of a global financial institution’s semantic layer implementation for their risk management initiatives, EK led taxonomy development efforts by implementing a semi-supervised clustering algorithm to group a large volume of inconsistent and unwieldy free-text risk descriptions based on their semantic similarity. The firm’s subject matter experts used the results to identify common and relevant themes that informed the design of a standard risk taxonomy that will significantly streamline their risk identification and assessment processes.

Identifying groups and patterns within and across datasets is also beneficial for advanced analytics and reporting needs. EK leveraged these AI techniques for a biotechnology company to aggregate and normalize disparate data from multiple legacy systems, providing a full-scale view for comprehensive analysis. EK incorporated this data into the semantic layer and, in turn, automated the generation of regulatory reports and detailed process analytics.

3. Link Detection

Link detection algorithms identify relationships and connections between entities or concepts within a dataset. Uncovering these links enables the construction of semantic networks or graphs, providing a structured representation of an organization’s knowledge domain. Link detection surfaces connections and patterns across data to enhance navigation and semantic search capabilities, ultimately facilitating comprehensive knowledge discovery and efficient information retrieval.

For a global scientific solutions and services provider, EK utilized link detection and prediction algorithms to develop a context-based recommendation system. The semantic layer established links between product data and product marketing content, expediting content aggregation and enabling personalization in the recommender interface for an intuitive and tailored user experience. 

4. Categorization

Categorization involves automatically classifying data or text into predefined categories or topics based on their content or features. Auto-tagging and classification are powerful techniques to organize and typify content from multiple sources that can then be fed into a single repository within the semantic layer. This streamlines information management processes for enhanced data access, connectivity, findability, and discoverability. 

EK leverages AI-based categorization to enable our clients to consistently organize data based on defined access and security requirements applicable within their organizations. For example, a leading federal research organization struggled with managing large amounts of unstructured data across various platforms, resulting in inefficiencies in data access and an increased risk of sensitive data exposure. EK automated content categorization based on predefined sensitivity rules and built a dashboard powered by the semantic layer to streamline the identification and remediation of access issues for overshared sensitive content. With the initial proof of concept, EK successfully automated the scanning and analyzing of about 30,000 documents to identify disparities in sensitivity labels and access designations without burdensome manual efforts.

Conclusion

AI techniques can be used to facilitate data aggregation, standardization, and semantic enrichment for curated input into the semantic layer, as well as to build upon the semantic layer for advanced downstream applications from semantic search to recommendation engines. By harnessing the combined power of AI and semantic layer components, organizations can accelerate the digital transformation of their knowledge and data landscapes and establish truly data-driven processes across the enterprise. Contact EK to see if your organization is ready for AI and learn how you can get started with your own AI-driven semantic layer initiatives. 

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