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.

The post Enhancing Taxonomy Management Through Knowledge Intelligence appeared first on Enterprise Knowledge.

In this presentation, Majumder and Nozari explored the crucial role of AI in enhancing data governance through Role-Based Access Control (RBAC), and discussed how the Adaptable Rule Foundation (ARF) system uses metadata and labels to classify and manage data effectively across three levels: Core, Common, and Unique. This system allows domain experts to participate actively in the AI-driven governance processes without needing deep technical expertise, promoting a secure and collaborative development environment.

Check out the presentation below to learn how to:

• Implement AI to streamline RBAC processes, enhancing data security and operational efficiency.
• Understand the impact of real-time data control on organizational dynamics.
• Enable domain experts to contribute securely and effectively to the AI development process.
• Leverage the ARF system to adapt security measures tailored to the specific needs of various organizational units.

The post Beyond Content Management for Real Knowledge Sharing appeared first on Enterprise Knowledge.

This blog explores how the integration of secure access management within an enterprise AI framework can transform enterprise AI systems from simple automation tools into secure, context-aware knowledge platforms. We’ll discuss approaches for how modern Role-Based Access Control (RBAC), enhanced by AI capabilities, works to streamline and create a dynamic ecosystem where information flows securely to those who need it most.

Understanding Enterprise AI and Access Control

Enterprise AI represents a significant advancement in how organizations process and utilize their data, moving beyond basic automation to intelligent, context-aware systems. This awareness becomes particularly powerful when combined with sophisticated access management systems. Role-Based Access Control (RBAC) serves as a cornerstone of this integration, providing a framework for regulating access to organizational knowledge based on user roles rather than individual identities. Modern RBAC systems, enhanced by AI, go beyond static permission assignments to create dynamic, context-aware access controls that adapt to organizational needs in real time.

Key Features of AI-Enhanced RBAC

1. Dynamic Role Assignment: AI systems continuously analyze user behavior, responsibilities, and organizational context to suggest and adjust role assignments, ensuring access privileges remain current and appropriate.
2. Intelligent Permission Management: Machine learning algorithms help identify patterns in data usage and access requirements, automatically adjusting permission sets to optimize security while maintaining operational efficiency, thereby upholding the principles of least privilege in the organization.
3. Contextual Access Control: The system considers multiple factors including time, location, device type, and user behavior patterns to make real-time access decisions.
4. Automated Compliance Monitoring: AI-powered monitoring systems track access patterns and flag potential security risks or compliance issues, enabling proactive risk management.

This integration of enterprise AI and RBAC creates a sophisticated framework where access controls become more than just security measures – they become enablers of knowledge flow within the organization.

Secure Access Management for Enterprise AI

Integrating access management with enterprise AI creates a foundation for secure, intelligent knowledge sharing by effectively capturing and utilizing organizational expertise.

Modern enterprises require a thoughtful approach to incorporating domain expertise into AI processes while maintaining strict security protocols. This integration is particularly crucial where domain experts transform their tacit knowledge into explicit, actionable frameworks that can enhance AI system capabilities. The AI-RBAC framework embodies this principle through two key components that work in harmony:

1. Adaptable Rule Foundation (ARF) for systematic content classification
2. Expert-driven Organizational Role Mapping for secure knowledge sharing

While ARF provides the structure for explicit knowledge through content tagging, the role mapping performed by Subject Matter Experts (SMEs) injects critical domain intelligence into the organizational knowledge framework, creating a robust foundation for secure knowledge sharing. The ARF system exemplifies this integration by classifying and managing data across three distinct levels, while SMEs provide the crucial expertise needed to map these classifications to organizational roles. This combination ensures that organizational knowledge is not only properly categorized but also securely accessible to the right people at the right time, effectively bridging the gap between AI-driven classification and human expertise.

The Adaptable Rule Foundation (ARF) system exemplifies this integration by classifying and managing data across three distinct levels:

• Core Level: Includes fundamental organizational knowledge and critical business rules, defined with input from domain SMEs.
• Common Level: Contains shared knowledge assets and cross-departmental information, with SME guidance on scope.
• Unique Level: Manages specialized knowledge specific to individual departments or projects, as defined by SMEs.

SMEs play a crucial role in adjusting the scope and definitions of the Core, Common, and Unique levels to inject their domain expertise into the ARF framework. This ensures the classification system aligns with real-world organizational knowledge and needs.

This three-tiered approach, powered by AI, enables organizations to:

• Automatically classify incoming data based on sensitivity and relevance
• Dynamically apply appropriate access controls using expert-driven organizational role mapping
• Enable domain experts to contribute knowledge securely without requiring technical expertise
• Adapt security measures in real-time based on organizational changes

The ARF system’s intelligence goes beyond traditional access management by understanding not just who should access information, but how that information fits into the broader organizational knowledge ecosystem. This contextual awareness ensures that security measures enhance, rather than hinder, knowledge sharing.

The Future of Enterprise AI

As organizations continue to leverage AI capabilities, the interaction between access management and enterprise AI becomes increasingly crucial. This integration ensures that AI systems serve as secure, intelligent platforms for knowledge sharing and decision-making. The combination of dynamic access controls and enterprise AI framework creates an environment where:

• Security becomes an enabler rather than a barrier to innovation
• Domain expertise naturally flows into AI systems through secure channels
• Organizations can adapt quickly to changing knowledge needs while maintaining security
• AI systems become more contextually aware and organizationally aligned

If your organization is looking to enhance AI capabilities while ensuring robust data security, our enterprise AI access management framework offers a powerful solution. Contact us to learn how to transform your organization’s knowledge infrastructure into a secure, intelligent ecosystem that drives innovation and growth.

The post Enterprise AI Meets Access and Entitlement Challenges: A Framework for Securing Content and Data for AI appeared first on Enterprise Knowledge.

This infographic serves as your roadmap to understanding and uncovering dark data within your organization, showcasing practical interventions to secure and capitalize on your most underutilized assets. At Enterprise Knowledge, we specialize in transforming complex data landscapes into structured, actionable intelligence. If you’re ready to enhance your data management strategies and mitigate risks associated with dark data, reach out to our experts today for tailored solutions that empower your data governance efforts. 

For information on dark data discovery, explore this infographic on Unlocking Dark Data: AI Strategies for Enhanced Data Governance.

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In this engaging session, Nozari and Majumder explored the challenges and opportunities presented by the rapid evolution of Large Language Models (LLMs) and the exponential growth of unstructured data within enterprises. They also addressed the critical intersection of technology and data governance necessary for managing AI responsibly in an era dominated by data breaches and privacy concerns. 

Check out the presentation below to learn more about: 

• A comprehensive framework to define and identify dark data
• Innovative AI solutions to secure data effectively
• Actionable insights to help organizations enhance data privacy and achieve regulatory compliance within the AI-driven data ecosystem

The post Mastering the Dark Data Challenge: Harnessing AI for Enhanced Data Governance and Quality appeared first on Enterprise Knowledge.

Introduction

Large Language Models (LLMs) have been trained on diverse and extensive datasets containing billions of words to understand, generate, and interact with human language in a way that is remarkably coherent and contextually relevant. Knowledge Graphs (KGs) are a structured form of information storage that utilizes a graph database format to connect entities and their relationships. KGs translate the relationships between various concepts into a mathematical and logical format that both humans and machines can interpret. The purpose of this paper is to explore the synergetic relationship between LLMs and KGs, showing how their integration can revolutionize data processing, knowledge extraction, and artificial intelligence (AI) capabilities. We explain the complexities of LLMs and KGs, showcase their strengths, and demonstrate how their combination can lead to more efficient and comprehensive knowledge processing and improved performance in AI applications.

Understanding Generative Large Language Models

LLMs can generate text that closely mimics human writing. They can compose essays, poems, and technical articles, and even simulate conversation in a remarkably human-like manner. LLMs use deep learning, specifically a form of neural network architecture known as transformers. This architecture allows the model to weigh the importance of different words in a sentence, leading to a better understanding of language context and syntax. One of the key strengths of LLMs is their ability to understand and respond to context within a conversation or a text. This makes them particularly effective for applications like chatbots, content creation, and language translation. However, despite the many capabilities of LLMs, they have limitations. They can generate incorrect or biased information, and their responses are influenced by the data they were trained on. Moreover, they do not possess true understanding or consciousness; they simply simulate this understanding based on patterns in data.

Exploring Knowledge Graphs

KGs are a powerful way to represent and store information in a structured format, making it easier for both humans and machines to access and understand complex datasets. They are used extensively in various domains, including search engines, recommendation systems, and data integration platforms. At their core, knowledge graphs are made up of entities (nodes) and relationships (edges) that connect these entities.  This structure allows for the representation of complex relationships between different pieces of data in a way that is both visually intuitive and computationally efficient. KGs are often used to integrate structured and unstructured data from multiple sources. This integration provides a more comprehensive understanding of the data by providing a unified view. One of the strengths of KGs is the ease with which they can be queried. Technologies like SPARQL (a query language for graph databases) enable users to efficiently extract complex information from a knowledge graph. KGs find applications in various fields, including search engines (like Google’s Knowledge Graph), social networks, business intelligence, and artificial intelligence.

Enhancing Knowledge Graph Creation with LLMs

KGs make implicit human knowledge explicit and allow inferences to be drawn from the information they are provided. The ontology, or graph model, serves as anchors or constraints to these inferences. Once created and validated, KGs can be trusted as a source of truth, they make inferences based on the semantics and structure of their model (ontology). Because of this element of human intervention, humans can ensure that the interpretation of information is correct for the given context, in particular alleviating the ‘garbage in – garbage out’ phenomenon. However, because of this human intervention, they can also be fairly labor-intensive to create. KGs are created using one of a couple types of graph database frameworks, they are generally dependent on some form of human intervention and are generated by individuals with a specialized skill set and/or specialized software. To access the information in a Knowledge Graph they must be stored in an appropriate graph database platform and require the use of specialized query languages to query the graph. Because of these specialized skills and a high degree of human intervention, knowledge graphs can be time-consuming and labor-intensive to create. 

Enhancing KG Creation with LLMs through Ontology Prompting

Through a technique known as ontology prompting, LLMs can effectively parse through vast amounts of unstructured text, accurately identify and extract pertinent entities, and discern the intricate relationships between these entities. By understanding and leveraging the context in which data appears, these models are not only capable of recognizing diverse entity types (such as people, places, organizations, etc.) but can also delineate the nuanced relationships that connect these entities. This process significantly streamlines the creation and enrichment of KGs, transforming raw, unstructured data into a structured, interconnected web of knowledge that is both accessible and actionable. The integration of LLMs into KG construction not only enriches the data but also significantly augments the utility and accuracy of the knowledge graphs in various applications, ranging from semantic search and content recommendation to advanced analytics and decision-making support.

Improving LLM Performance with Knowledge Graphs

The integration of KGs into LLMs offers substantial performance improvements, particularly in enhancing contextual understanding, reducing biases, and boosting accuracy. KGs inject a semantic layer of contextual depth into LLMs, enabling these models to grasp and process language with a more nuanced understanding of the subject matter. This interaction significantly enhances the comprehension capabilities of LLMs, as they become more adept at interpreting and responding to complex queries with enhanced precision. Moreover, the structured nature of KGs aids in mitigating biases inherent in LLMs. By providing a balanced and factual representation of information, KGs help neutralize skewed perspectives and promote a more objective and informed generation of content. Finally, the incorporation of KGs into LLMs has been instrumental in enhancing the accuracy and reliability of the output generated by LLMs.

The validated data from KGs serve as a solid foundation, and reduce ambiguities and errors in the information processed by LLMs, thereby ensuring a higher quality of output that is trustworthy, traceable, and contextually coherent.

Case Studies and Applications

The integration of LLMs and KGs is making significant advances across various industries and transforming how we process and leverage information. For instance, in the finance sector, LLMs combined with KGs are used for risk assessment and fraud detection. These systems analyze transaction patterns, detect anomalies, and understand the relationships between different entities, helping financial institutions mitigate risks and prevent fraudulent activities.  Another example is personalized recommendation systems. E-commerce platforms like Amazon utilize KGs and LLMs to understand customer preferences, search histories, and purchase behaviors. This integration allows for highly personalized product and content recommendations, improving customer experience and increasing sales and engagement. In the legal industry LLMs and KGs are used to analyze legal documents, case laws, and statutes. They help in summarizing legal documents, extracting relevant clauses, and conducting research, thereby saving time for legal professionals and improving the accuracy of legal advice. The potential of LLM and KG integrations is unlimited, promising transformative advancements across sectors. For example, leveraging LLMs and KGs can transform educational platforms, guiding learners through tailored and personalized educational journeys. In healthcare, the innovation in sophisticated virtual assistants is revolutionizing telemedicine, offering preventive care and preliminary diagnoses. Urban planning and management stand to gain immensely from this technology, enabling smarter city planning through the analysis of diverse data sources, from traffic patterns to social media sentiments. Moreover, the research and development are set to accelerate, with LLMs and KGs synergizing to automate literature reviews, foster novel research ideas, and predict experimental outcomes. 

Challenges and Considerations

While the integration of LLMs and KGs is promising, it is accompanied by a set of significant challenges and considerations. From a technical perspective, merging LLMs with KGs needs sophisticated algorithms capable of handling the complexity of KG structures and the nuances of natural language processed by LLMs. For example, ensuring data compatibility, maintaining real-time data synchronization, and managing the computational load are difficult tasks that require advanced solutions and ongoing innovation. Moreover, ethical and privacy concerns are one of the top challenges of this integration. The use of LLMs and KGs involves processing vast amounts of data some of which may be sensitive or personal. Ensuring that these technologies adhere to privacy laws and regulations, maintain data confidentiality, and make ethically sound decisions is a continuous challenge.  There’s also the risk of perpetuating biases present in the training data of LLM that require meticulous oversight and implementation of bias-mitigation strategies. Furthermore, the sustainability of these advanced technologies cannot be overlooked. The energy consumption associated with training and running large-scale LLMs and maintaining extensive KGs poses significant environmental concerns. As the demand for these technologies grows, finding ways to minimize their carbon footprint and developing more energy-efficient models is important. Addressing these technical, ethical, and sustainability challenges is crucial for the responsible and effective implementation of LLM and KG integrations.

Conclusion

In this white paper, we explored the dynamic interplay between LLMs and KGs, unraveling the profound impact of their integration on various industries. We delved into the transformative capabilities of LLMs in enhancing the creation and enrichment of KGs, highlighting automated data extraction, contextual understanding, and data enrichment. Conversely, we discussed how KGs can improve LLM performance by imparting contextual depth, mitigating biases, enabling source traceability, and increasing accuracy and reliability. We also showcased the practical benefits and revolutionary potential of this synergy. In conclusion, the combination of LLMs and KGs stands at the forefront of technological advancement and directs us toward an era of enhanced intelligence and informed decision-making. However, fostering continued research, encouraging interdisciplinary collaboration, and nurturing an ecosystem that prioritizes ethical considerations and sustainability is important.

Want to jumpstart your organization’s use of LLMs? Check out our Enterprise LLM Accelerator and contact us at info@enterprise-knowledge.com for more information! 

About this article

This is an article within a linked series written to provide a straightforward introduction to getting started with language models (LLMs) and knowledge graphs (KGs). You can find the next article in the series here.

The post Synergizing Knowledge Graphs with Large Language Models (LLMs): A Path to Semantically Enhanced Intelligence appeared first on Enterprise Knowledge.

If your organization is seeking innovative ways to enhance data governance and mitigate the risk of dark data, EK is here to guide you. With extensive expertise in crafting and deploying strategies that refine how you manage and utilize your data, we are ready to provide you with tailored, actionable insights.  

For a customized consultation and to learn more about how we can assist you, check out our Enterprise AI Services and contact us for more information!

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