Enterprise Knowledge recently completed the first round of our new webinar series The Semantic Exchange, which offers participants an opportunity to engage in Q&A with EK’s Semantic Design thought leaders. Participants were able to engage with EK’s experts on topics such as the value of enterprise semantic architecture, best practices for generating buy-in for semantics across an organization, and techniques for semantic solution implementation. The series sparked thoughtful discussion on how to understand and address real-world semantic challenges. 

To view any of the recorded sessions and their corresponding published work – use the links below:

If you are interested in bringing semantics and data modeling solutions to your organization, contact us here!

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Enterprise Knowledge is pleased to introduce a new webinar series, The Semantic Exchange. We’re kicking off a five part series where we invite fellow practitioners to tune in and hear more about work we’ve published from the authors themselves. In these moderated sessions, we invite you to ask the authors questions in a short, accessible format. Think of the series as a chance for a little semantic snack! 

This session is designed for a variety of audiences, ranging from those working in the semantic space as taxonomists or ontologists – to folks who are just starting to learn about structured data and content, and how they may fit into broader initiatives around artificial intelligence or knowledge graphs. 

This 30-minute session invites you to engage with Bonnie Griffin’s infographic, Why Your Taxonomy Needs SKOS. Come ready to hear and ask about: 

• Why SKOS is the W3C-recommended format for taxonomies 
• How SKOS unlocks more value than a simple term list 
• What your organization misses out on with non-SKOS taxonomies

This webinar will take place on Wednesday June 25th, from 1:00 – 1:30PM EDT. Can’t make it? The session will also be recorded and published to our knowledge base following the session. View the recording of the first session here!

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Just as a semantic layer passes through degrees of maturity and complexity as it is developed and operationalized, so too does a taxonomy. While a taxonomy comprises only one facet of a fully realized Semantic Layer, every incremental increase in its granularity and scope can have a compounding effect in terms of unlocking additional solutions for the organization. While it can be tempting to assume that only a fully mature taxonomy is capable of delivering measurable value for the organization that developed it, each iteration of a taxonomy provides value that should be acknowledged, quantified, and celebrated to advocate for continued support of the taxonomy’s ongoing development.  

Taxonomy Maturity Stages

A taxonomy’s maturity can be measured across five levels: Basic, Foundational, Operational, Institutional, and Transformational. Taken as a snapshot from our full semantic layer maturity framework, the following diagram illustrates each of these levels in terms of their taxonomy components, technical manifestation, and what valuable outcomes can be expected from each at a high level. 

Basic Taxonomy

A basic taxonomy lacks depth, and is essentially a folksonomy (an informal, non-hierarchical classification system where users apply public tags). At this stage, a basic taxonomy is only inconsistently applied across departments. 

As an example, a single business unit (Marketing) may have begun developing a basic taxonomy that other business units (Sales) may be starting to integrate with their product taxonomy. 

Components and Technical Manifestation at this Level

• Basic taxonomies are only developed for limited, specific use cases, often for a particular team or subset of an organization.
• At this stage of maturity, a taxonomy expresses little granularity, and may have up to three levels of broader/narrower relationships. 
• A basic taxonomy is likely maintained in a spreadsheet, rather than a taxonomy management system (TMS). The taxonomy may be implemented in a rudimentary form, like being expressed in file structures. Taxonomy concepts are not yet tagged to assets. 
• At this stage, the taxonomy functions primarily as a proof of concept. The taxonomy has not yet been widely validated or socialized, and is likely only known by the team building it. It may represent an intentionally narrow scope that can then be scaled as the team builds buy-in with stakeholders. 

Outcomes and Value 

• The basic taxonomy provides an essential foundation to build upon. If it is well-designed, the work invested in this stage can serve as a model for other functional areas of the organization to adopt for their own use cases. 
• At this stage, the value is typically limited to providing a proof of concept to demonstrate what taxonomy is, and working towards establishing consistent terminology within a department.

Foundational Taxonomy

The foundational taxonomy is not yet wholly standardized, but growing momentum helps to drive adoption and standardization across systems and business units. The taxonomy can support simple data enrichment by adding semantic context (like relevant location data, contact information, definitions, or subcategories) to an existing data set. Often, a dedicated taxonomy management solution (TMS) is procured at this stage, and it may be unscalable to proceed to the next level of maturity without one. 

Components and Technical Manifestation at this Level

• The taxonomy is imbued with semantic context such as definitions, scope notes, and alternative labels, along with the expected hierarchical relationships between concepts. A foundational taxonomy exhibits a greater level of granularity beyond the basic level. 
• The taxonomy is no longer only housed in a spreadsheet, and is maintained in a Taxonomy Management Solution (TMS). This makes it easier to ensure that the taxonomy’s format adheres to semantic web frameworks (such as SKOS, the Simple Knowledge Organization System). 
• The addition of this context serves the fundamental purpose of supporting and standardizing semantic understanding within an organization by clarifying and enforcing preferred terms while still capturing alternative terms.  
• Some degree of implementation has been realized – for instance, the tagging of a representative set of content or data assets.
• The taxonomy team actively engages in efforts to socialize and promote the taxonomy project to build awareness and support among stakeholders. 
• A taxonomy governance team has been established for ongoing validation, maintenance, and change management. 

Outcomes and Value

• At this stage, the taxonomy can provide more measurable benefits to the organization. For instance, a foundational taxonomy can support content audits for all content that has been auto-tagged. 
• The taxonomy can support more advanced data analytics – for instance, users can get more granular insights into which topics are the most represented in content. 
• The foundational taxonomy can be scaled to incorporate backlog use cases or other departments in the organization, and can be considered a product to be replicated and more broadly socialized.
• The taxonomy can be enhanced by adding linked models and/or concept mapping.

Operational Taxonomy

The operational taxonomy is standardized, used regularly and consistently across teams, and is integrated with other components or applications. 

At this stage, the taxonomy is integrated with key systems like a content management system (CMS), learning management system (LMS), or similar. Users are able to interact with the taxonomy directly through the system-powered apps they work in, because the systems consume the taxonomy.

Components and Technical Manifestation at this Level

• At this level of maturity, advanced integrations have been realized – for instance, the taxonomy is integrated into search for the organization’s intranet, or the taxonomy’s semantic context has been leveraged as training data for generative AI-powered chatbots.
• At the operational level, the taxonomy acts as a source of truth for multiple use cases, and has been expanded to cover multiple key areas of the organization, such as Customer Operations, Product, and Content Operations. 
• By this stage, content tagging has been seamlessly integrated into the content creation process, in which content creators apply relevant tags prior to publishing, or automatic tagging ensures content is applied to current and newly-published content. 
• A TMS has been acquired, and is implemented with key systems, such as the organization’s LMS, intranet, or CMS. 
• The taxonomy is subject to ongoing governance by a taxonomy governance team, and key stakeholders in the organization are informed of key updates or changes to the taxonomy.

Outcomes and Value 

• The taxonomy is integrated with essential data sources to provide or consume data directly. As a result, users interacting with the systems that are connected to the taxonomy are able to experience the additional structure and clarity provided by the taxonomy via features like search filters, navigational structures, and content tags. 
• The taxonomy can support enhanced data analytics, such as tracking the click-through rate (CTR) of content tagged with particular topics. 

Institutional Taxonomy

The institutional taxonomy is fully integrated into daily operations. Rigorous governance and change management capabilities are in place. 

By now, seamless integrations between the taxonomy and other systems have been established. Ongoing taxonomy maintenance work poses no disruption to day-to-day operations, and updates to the taxonomy are automatically pushed to all impacted systems.

Components and Technical Manifestation at this Level

• The taxonomy, or taxonomies, are fully integrated into daily operations across teams and functional areas – for instance, the taxonomy supports dynamic content delivery for customer support workers, the customer-facing product taxonomy facilitates faceted search for online shopping, and so on. 
• The organization’s use cases are supported by the taxonomy, which supports core goals such as ensuring a shared understanding of key concepts and their meaning, providing a consistent framework for the representation of data across systems, or representing the fundamental components of an organization across systems. 
• Governance roles, policies, and procedures are fully established and follow a regular cadence. 

Outcomes and Value

• At this stage of maturity, the taxonomy has been scaled to the extent that it can be considered an enterprise taxonomy; it covers all foundational areas, is utilized by all business units, and is poised to support key organizational operations. At this stage, the taxonomy drives a key enterprise-level use case. 
• Data connectivity is supported across the organization; the taxonomy unifies language across teams and systems, reducing errors and data discrepancies. 
• Internal as well as external users benefit from taxonomy-enhanced search in the form of query expansion. 

Transformational Taxonomy

The transformational taxonomy drives data classification and advanced analytics, informing and enhancing AI-driven processes. At this stage, the taxonomy provides significant functionality supporting an integrated semantic layer. 

Components and Technical Manifestation at this Level

• The taxonomy can support the delivery of personalized, dynamic content for internal or external users for more impactful customer support or marketing outreach campaigns.
• The taxonomy is inextricably tied to other key components of the semantic layer’s operating model. The taxonomy provides data for the knowledge graph, provides a hierarchy for the ontology, categorizes the data in the data catalog, and enriches the business glossary with additional semantic context. These connections help power semantic search, analytics, recommendation systems, discoverability, and other semantic applications. 
• Taxonomy governance roles are embedded in functional groups. Feedback on the taxonomy is shared regularly, introductory taxonomy training is widely available, and there is common understanding of how to both use the taxonomy and provide feedback. 
• Taxonomies are well-supported by defined metrics and reporting and, in turn, provide a source of truth to power consistent reporting and data analytics.  

Outcomes and Value 

• At this stage, the taxonomy (within the broader semantic layer) drives multiple enterprise-level use cases. For instance, this could include self-service performance monitoring to support strategic planning, or facilitating efficient data analytics across previously-siloed datasets. 
• Taxonomy labeling of structured and/or unstructured data powers Machine Learning (ML) and Artificial Intelligence (AI) development and applications. 

Taxonomy Use Cases 

Low Maturity Example

In many instances, EK partners with clients to help develop taxonomies in their earliest stages. Recently, a data and AI platform company engaged EK to lead a taxonomy workshop covering best practices in taxonomy design, validation activities, taxonomy governance, and developing an implementation roadmap. Prior to EK’s engagement, the company was in the process of developing a centralized marketing taxonomy. As the taxonomy was maintained in a shared spreadsheet, lacked a defined governance process, and lacked consistent design guidelines, it met the basic level of maturity. However, after the workshop, the client’s taxonomy design team left with a refreshed understanding of taxonomy design best practices, clarified user personas, an appreciation of the value of semantic web standards, a clear taxonomy development roadmap, and a scaled-down focus on prioritized pilots to build a starter taxonomy. 

By clarifying and narrowing their use cases, identifying their key stakeholders and their roles in taxonomy governance, and reworking the taxonomy to reflect design principles grounded in semantic standards, the taxonomy team was equipped to elevate their taxonomy from a basic level of maturity to work towards becoming foundational. 

High Maturity Example 

EK’s collaboration with a major international retailer illustrates an example of the evolution towards a highly-mature semantic layer supported by a robust taxonomy. EK partnered with the retailer’s Learning Team to develop a Learning Content Database to enable an enterprise view of their learning content. Initially, the organization’s learning team lacked a standardized taxonomy. This made it difficult to identify obsolete content, update outdated content, or address training gaps. Without consistent terminology or content categorization, it was especially challenging to search effectively and identify existing learning content that could be improved, forcing the learning team to waste time creating new content. As a result, store associates struggled to search for the right instructional resources, hindering their ability to learn about new roles, understand procedures, and adhere to compliance requirements. 

To address these issues, EK first partnered with the learning team to develop a standardized taxonomy. The taxonomy crystallized brand-approved language which was then tagged to learning content. Next, EK developed a tailored governance plan to ensure the ongoing maintenance of the taxonomy, and provided guidance around taxonomy implementation to ensure optimal outcomes around reducing time spent searching for content and simplifying the process of tagging content with metadata. With the taxonomy at a sufficient stage of maturity, EK was then able to build the Learning Content Database, which enabled users to locate learning content across previously disparate, disconnected systems, now in a central location. 

Conclusion

Every taxonomy – from the basic starter taxonomy to the highly-developed taxonomy with robust semantic context connected to an ontology – can provide value to its organization. As a taxonomy grows in maturity, each next level of development unlocks increasingly complex solutions. From driving alignment around key terms for products and resources, supporting content audits, enabling complex data analytics across systems, or powering semantic search, the progressive advancement of a taxonomy’s complexity and semantic richness translates to tangible business value. These advancements can also act as a flywheel, where each improvement makes it easier to continue to drive buy-in, secure necessary resources, and achieve greater enhancements. 

If you are looking to learn more about how other organizations have benefitted from advanced taxonomy implementations, read more from our case studies. If you want additional guidance on how to take your organization’s taxonomy to the next level, contact us to learn more about our taxonomy design services and workshops.

The post Semantic Layer Maturity Framework Series: Taxonomy appeared first on Enterprise Knowledge.

The Evolution of Taxonomy Management

Traditional taxonomy management has long relied on Subject Matter Experts (SME) manually curating terms, relationships, and hierarchies. While this time-consuming approach ensures accuracy, it struggles with scale. Modern organizations generate millions of documents across multiple languages and domains, and manual curation simply cannot keep pace with the large variety and velocity of organizational data while maintaining the necessary precision. Even with well-defined taxonomies, organizations must continuously analyze massive amounts of content to verify that their taxonomic structures accurately reflect and capture the concepts present in their rapidly growing data repositories.

In the scenario above, traditional AI tools might help classify new documents, but an expert-guided recommender brings intelligence to the process.

KI-Driven Taxonomy Management

KI represents a fundamental shift from traditional AI systems, moving beyond data processing to true knowledge understanding and manipulation. As Zach Wahl explains in his blog, From Artificial Intelligence to Knowledge Intelligence, KI enhances AI’s capabilities by making systems contextually aware of an organization’s entire information ecosystem and creating dynamic knowledge systems that continuously evolve through intelligent automation and semantic understanding.

At its core, KI-driven taxonomy management works through a continuous cycle of enrichment, validation, and refinement. This approach integrates domain expertise at every stage of the process:

1. During enrichment, SMEs guide AI-powered discovery of new terms and relationships.

2. In validation, domain specialists ensure accuracy and compliance of all taxonomy modifications.

3. Through refinement, experts interpret usage patterns to continuously improve taxonomic structures.

By systematically injecting domain expertise into each stage, organizations transform static taxonomies into adaptive knowledge frameworks that continue to evolve with user needs while maintaining accuracy and compliance. This expert-guided approach ensures that AI augments rather than replaces human judgement in taxonomy development.

Enrichment: Augmenting Taxonomies with Domain Intelligence

When augmenting the taxonomy creation process with AI, SMEs begin by defining core concepts and relationships, which then serve as seeds for AI-assisted expansion. Using these expert-validated foundations, systems employ Natural Language Processing (NLP) and Generative AI to analyze organizational content and extract relevant phrases that relate to existing taxonomy terms. 

Topic modeling, a set of algorithms that discover abstract themes within collections of documents, further enhances this enrichment process. Topic modeling techniques like BERTopic, which uses transformer-based language models to create coherent topic clusters, can identify concept hierarchies within organizational content. The experts evaluate these AI-generated suggestions based on their specialized knowledge, ensuring that automated discoveries align with industry standards and organizational needs. This human-AI collaboration creates taxonomies that are both technically sound and practically useful, balancing precision with accessibility across diverse user groups.

Validation: Maintaining Compliance Through Structured Governance

What sets the KI framework apart is its unique ability to maintain strict compliance while enabling taxonomy evolution. Every suggested change, whether generated through user behavior or content analysis, goes through a structured governance process that includes:

• Automated compliance checking against established rules;
• Human expert validation for critical decisions;
• Documentation of change justifications; and
• Version control with complete audit trails.

Organizations implementing KI-driven taxonomy management see transformative results including improving search success rates and decreasing the time required for taxonomy updates. More importantly, taxonomies become living knowledge frameworks that continuously adapt to organizational needs while maintaining compliance standards.

Refinement: Learning From Usage to Improve Taxonomies

By systematically analyzing how users interact with taxonomies in real-world scenarios, organizations gain invaluable insights into potential improvements. This intelligent system extends beyond simple keyword matching—it identifies emerging patterns, uncovers semantic relationships, and bridges gaps between formal terminology and practical usage. This data-driven refinement process:

• Analyzes search patterns to identify semantic relationships;
• Generates compliant alternative labels that match user behavior;
• Routes suggestions through appropriate governance workflows; and
• Maintains an audit trail of changes and justifications.

The refinement process analyzes the conceptual relationship between terms, evaluates usage contexts, and generates suggestions for terminological improvements. These suggestions—whether alternative labels, relationship modifications, or new term additions—are then routed through governance workflows where domain experts validate their accuracy and compliance alignment. Throughout this process, the system maintains a comprehensive audit trail documenting not only what changes were made but why they were necessary and who approved them. 

Case Study: KI in Action at a Global Investment Bank

To show the practical application of the continuous, knowledge-enhanced taxonomy management cycle, in the following section we describe a real-world implementation at a global investment bank.

Challenge

The bank needed to standardize risk descriptions across multiple business units, creating a consistent taxonomy that would support both regulatory compliance and effective risk management. With thousands of risk descriptions in various formats and terminology, manual standardization would have been time-consuming and inconsistent.

Solution

Phase 1: Taxonomy Enrichment

The team began by applying advanced NLP and topic modeling techniques to analyze existing risk descriptions. Risk descriptions were first standardized through careful text processing. Using the BERTopic framework and sentence transformers, the system generated vector embeddings of risk descriptions, allowing for semantic comparison rather than simple keyword matching. This AI-assisted analysis identified clusters of semantically similar risks, providing a foundation for standardization while preserving the important nuances of different risk types. Domain experts guided this process by defining the rules for risk extraction and validating the clustering approach, ensuring that the technical implementation remained aligned with risk management best practices.

Phase 2: Expert Validation

SMEs then reviewed the AI-generated standardized risks, validating the accuracy of clusters and relationships. The system’s transparency was critical so experts could see exactly how risks were being grouped. This human-in-the-loop approach ensured that:

• All source risk IDs were properly accounted for;
• Clusters maintained proper hierarchical relationships; and
• Risk categorizations aligned with regulatory requirements.

The validation process transformed the initial AI-generated taxonomy into a production-ready, standardized risk framework, approved by domain experts.

Phase 3: Continuous Refinement

Once implemented, the system began monitoring how users actually searched for and interacted with risk information. The bank recognized that users often do not know the exact standardized terminology when searching, so the solution developed a risk recommender that displayed semantically similar risks based on both text similarity and risk dimension alignment. This approach allowed users to effectively navigate the taxonomy despite being unfamiliar with standardized terms. By analyzing search patterns, the system continuously refined the taxonomy with alternative labels reflecting actual user terminology, and created a dynamic knowledge structure that evolved based on real usage.

This case study demonstrates the power of knowledge-enhanced taxonomy management, combining domain expertise with AI capabilities through a structured cycle of enrichment, validation, and refinement to create a living taxonomy that serves both regulatory and practical business needs.

Taxonomy Standards

For taxonomies to be truly effective and scalable in modern information environments, they must adhere to established semantic web standards and follow best practices developed by information science experts. Modern taxonomies need to support enterprise-wide knowledge initiatives, break down data silos, and enable integration with linked data and knowledge graphs. This is where standards like the Simple Knowledge Organization System (SKOS) become essential. By using universal standards like SKOS, organizations can:

• Enable interoperability between systems and across organizational boundaries
• Facilitate data migration between different taxonomy management tools
• Connect taxonomies to ontologies and knowledge graphs
• Ensure long-term sustainability as technology platforms evolve

Beyond SKOS, taxonomy professionals should be familiar with related semantic web standards such as RDF and SPARQL, especially as organizations move toward more advanced semantic technologies like ontologies and enterprise knowledge graphs. Well-designed taxonomies following these standards become the foundation upon which more advanced Knowledge Intelligence capabilities can be built. By adhering to established standards, organizations ensure their taxonomies remain both technically sound and semantically precise, capable of scaling effectively as business requirements evolve.

The Future of Taxonomy Management

The future of taxonomy management lies not just in automation, but in intelligent collaboration between human expertise and AI capabilities. KI provides the framework for this collaboration, ensuring that taxonomies remain both precise and practical. 

For organizations considering this approach, the key is to start with a clear understanding of their taxonomic needs and challenges, and to ensure their taxonomy efforts are built on solid foundations of semantic web standards like SKOS. These standards are essential for taxonomies to effectively scale, support interoperability, and maintain long-term value across evolving technology landscapes. Success comes not from replacement of existing processes, but from thoughtful integration of KI capabilities into established workflows that respect these standards and best practices.

Ready to explore how KI can transform your taxonomy management? Contact our team of experts to learn more about implementing these capabilities in your organization.

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The post Why Your Taxonomy Needs SKOS  appeared first on Enterprise Knowledge.

As the Knowledge and Information Management field moves towards adopting semantic technologies like ontologies and enterprise knowledge graphs, taxonomists and taxonomy managers need to know about W3C semantic web standards including RDF, SKOS, SPARQL because data is becoming more interconnected and complex, and we need to move beyond the traditional hierarchical taxonomy relationships in order to truly model our knowledge domains. Taxonomies can also use these standards to extend into ontologies, which increase the value of a taxonomist’s work by supporting AI initiatives and features. As my colleagues have defined in previous blogs,

Knowing, and being able to leverage these semantic standards, will not only increase a Taxonomist or Taxonomy Manager’s ability to maintain and enhance taxonomy designs, but will also ensure that taxonomies are built to last as a source of truth for their domain and to serve as the building blocks to an ontology. 

What a Taxonomist needs to know about RDF, SKOS, and SPARQL

The W3C, or World Wide Web Consortium, is an international standards organization that develops open standards to ensure the growth and longevity of the world wide web. Among these are the standards and recommendations for RDF, SKOS, and SPARQL. RDF stands for Resource Description Framework and is used to describe and model information for web resources or knowledge management systems. RDF consists of “triples” or statements that resemble a sentence. If we think back to elementary school English classes and sentence diagramming, we build sentences or triples that contain a subject, predicate, and object. 

SKOS is built on RDF, and stands for Simple Knowledge Organization System and is another W3C recommendation for how taxonomies should be structured and represented.

SPARQL is pronounced “sparkle” and is a recursive acronym for “SPARQL Protocol and RDF Query Language”, which is a set of specifications from the W3C. SPARQL allows you to query one or more triples and return varied results based on the type of information we are looking for from our taxonomy or graph database. All that is needed to leverage SPARQL is 1) data that is represented in RDF format and (2) an endpoint inside an enterprise taxonomy/ontology management tool, or a publicly available endpoint like Wikidata.

What is the Value of RDF and SPARQL?

When metadata about concepts within a taxonomy is stored using RDF (Last modified date, created by, approval status, etc.) taxonomists can use SPARQL to interact with and ask questions about your taxonomy design in many different ways, including: to update the taxonomy, pull concrete values from the data, or even track changes for governance. A query could pull all concepts in draft status, or all concepts edited by a specific person in the last 30 days. We can also use SPARQL to explore our data by querying unknown relationships to discover new connections. We’ve received questions from clients that have prompted the need for SPARQL queries to do basic reporting on a taxonomy structure or to return a subset of the project data for updating another system. Consider if we only want a portion of the enterprise taxonomy that is used for the intranet and Content Management System (CMS) for the Digital Asset Management system (DAM). We can use a SPARQL query to pull only the concepts that live under a certain tree or broader concept to then import into the DAM.

The primary value of RDF is in the triples that allow us to make statements and connect different concepts beyond broader and narrower relationships, building a flexible and interoperable taxonomy. Specifically, RDF adds value in three main ways, all of which are related to the idea of use and reuse of information.

• URIs (Uniform Resource Identifiers): do exactly what they sound like – identify resources with unique IDs without being specific to the resource’s location or use so that it can be reused. 
• Linked Open Data: Openly available data (triples) or models (taxonomies/ontologies) that can be sourced and used to enhance a custom taxonomy, or to negate the need to design a taxonomy that already exists (e.g. DBPedia)
• Interoperability: The idea that by using semantic standards, all vocabularies or models built using those standards can be integrated and used with each other, and with other systems or applications.

Even though our business or enterprise taxonomies may be highly specific, internal vocabularies, we can still leverage RDF and SKOS to ensure interoperability behind our firewalls. Specifically, the use and reuse of the taxonomy in multiple systems so that all the systems, and all those users, are speaking the same language. This is also key for the development and implementation of knowledge graphs that will leverage RDF and SPARQL to pull information from the disparate systems together for greater usability.

What Kind of Information Can I Query?

When writing a SPARQL query you are typically saying “I want X information from Y data that meets Z conditions.” The conditions are written as triple patterns, which are similar to RDF triples but may include variables to add flexibility in how they match against the data. For example, if we have a taxonomy where all terms should have preferred labels in both English and French, and we need to get a list of terms from our taxonomy that still need French translations, we can use a SPARQL query using their scheme/top concept so that we can send terms to the appropriate SMEs to translate. This SPARQL query would follow the pattern above and ask “I want all concepts that are narrower terms of a Concept A that do not have a French prefLabel.” It might look like this:

Some SPARQL queries might be as simple as identifying how many concepts are under a parent concept in our taxonomy. Many taxonomy management tools will provide statistics on the total number of concepts within the taxonomy but may not provide those statistics at the remaining lower levels of the taxonomy hierarchy. 

We have also used SPARQL to support the approval workflow and update process from the taxonomy management system to a second, custom application for tagging data. In this case, we needed a query that would return all the draft concepts and all their related triples (the information that makes up the concept) so the second application could be updated with the new concepts, leaving existing concepts as they were.

Conclusion

SKOS, RDF, and SPARQL work together to ensure interoperability and usability of your organization’s data and information by standardizing the way taxonomists design and manage taxonomies and streamlining the path toward ontologies and knowledge graphs. Leveraging what I’ve described in this blog, with the appropriate designs and implementations, can translate to Enterprise AI readiness for an organization and overall, better visibility and usage of your organization’s data and information.

Whether you are just beginning the process of designing a taxonomy, or are focused on implementation, semantic standards should be a primary consideration to ensure longevity, usability, and interoperability with many systems and tools. We are here to help you utilize these standards and implement them efficiently. Contact us. 

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