We’re excited to release these discussions for viewing, starting with Ben Clinch of Ortecha (who we also got the chance to speak with ahead of the event on Knowledge Cast).

Using Semantics to Reduce Hallucinations and Overcome Agentic Limits – Neuro-Symbolic AI and the Promise of Agentic AI

Speaker: Ben Clinch (Ortecha)

With the pace of change of AI being experienced across the industry and the constant bombardment of contradictory advice it is easy to become overwhelmed and not know where to start. The promise of LLMs have been undermined by vendor and journalistic hype and an inability to rely on quantitative answers being accurate. After all, what good would a colleague be (artificial or not) if you already need to know the answer to validate any question that you ask of them? This is further compounded by the exciting promise of Agentic AI but the relative immaturity of frameworks such as MCP. The promise of neuro-symbolic AI that combines two well established technologies (semantic knowledge graphs with machine learning) enable you to get more accurate LLM powered analytics and most importantly faster time to greater data value and when leveraged alongside solid data management foundations can mitigate and empower AI Agents while limiting the inherent risks in using them.

In this practical, engaging, and fun talk, Ben equips participants with the principles and fundamentals that never change but often go under-utilized to help you lay a solid foundation for the new age of agentic AI.

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The Anatomy of AI

Here is a conceptual analogy to help illustrate how taxonomies and ontologies support AI. While inspired by the human musculoskeletal system, this analogy is not intended to represent anatomical accuracy, but rather to illustrate how taxonomies provide foundational structure and ontologies enable flexible, contextual connections of knowledge assets within AI systems.

Just like the musculoskeletal system gives structure, support, and coherence to the human body, taxonomies and ontologies provide the structural framework that organizes and contextualizes knowledge assets for AI. Here is the analogy: the spine and the bones represent the taxonomies, in other words, the hierarchical, backbone structure for categorizing and organizing concepts that describe an organization’s core knowledge assets. Similarly, the joints, ligaments, and muscles represent the ontologies that provide the flexibility to connect related concepts across assets in an organization’s knowledge domain. 

Depending on the organization’s domain or industry, certain types of knowledge assets become more relevant or strategically important. In the case of a healthcare organization, key knowledge assets may include content such as patients’ electronic health records, clinical guidelines and protocols, multidisciplinary case reviews, and research publications, as well as data such as diagnostic data and clinical trial data. Taxonomies that capture and group together key concepts, such as illnesses, symptoms, treatments, outcomes, medicines, clinical specialties can be used to tag and structure these assets. Continuing with the same scenario, an ontology in a healthcare organization can incorporate those key concepts (entities) from the taxonomy, along with their properties and relationships, to enable alignment and consistent interpretation of knowledge assets across systems. Both taxonomies and ontologies in healthcare organizations make it possible to connect, for instance, a patient’s health record with diagnostic data and previous case reviews for other patients based on the same (or similar) conditions, including illnesses, symptoms, treatments, and medicines. As a result, healthcare professionals can quickly access the information they need to make well-informed decisions about a patient’s care.

Where AI is Failing

On multiple occasions, AI has repeatedly failed to provide reliable information to employees, customers, and patients, undermining their confidence in the AI supported system and sometimes leading to serious organizational consequences. You may be familiar with the case in which a chatbot of a medical association was unintentionally giving harmful advice to people with eating disorders. Or maybe you heard in the news about the bank with a faulty AI system that misclassified thousands of transactions as fraudulent due to a programming error, resulting in significant customer dissatisfaction and harming the organization’s reputation. There was also a case in which an AI-powered translation system failed to accurately assess asylum seekers’ applications, raising serious concerns about its fairness and accuracy, and potentially affecting critical life decisions for those applicants. In each of these cases, had the corresponding AI systems effectively aggregated both unstructured and structured knowledge assets, and reliably linked them to encoded expert knowledge and relevant business context, these cases would have produced very different and positive outcomes. By leveraging taxonomies and ontologies to aggregate key knowledge assets, the result of these cases would have been much more closely aligned with intended objectives, ultimately, benefiting the end users as it was initially intended. 

How Taxonomies And Ontologies Enable Explainable AI

When knowledge assets are consistently tagged with taxonomies and related via ontologies, AI systems can trace how a decision was made. This means that end users can understand the reasoning path, supported by defined relationships. This also means that bias and hallucinations can be more easily detected by auditing the semantic structure behind the results.

As illustrated in the healthcare organization example, diagnoses can be tagged with medical industry taxonomies, while ontologies can help create relationships among symptoms, treatments, and outcomes. This can help physicians tailor treatments to individual patient needs by leveraging past patient cases and the collective expertise from other physicians. Similarly, a retail organization can enhance its customer service by implementing a chatbot that is linked to structured product taxonomies and ontologies to help deliver consistent and explainable answers about products to customers. More consistent and trustworthy customer interactions result in streamlining end user support and strengthening brand confidence.

Do We Really Need Taxonomies and Ontologies to be Successful With AI?

The examples above illustrate that explainability in AI really matters. Whether end users are patients, bank customers, or any individuals requesting specific products or services, they all want more transparent, trustworthy, and human-centered AI experiences. Taxonomies and ontologies help provide structure and connectedness to content, documents, data, expert knowledge and overall business context, so that they all are machine readable and findable by AI systems at the moment of need, ultimately creating meaningful interactions for end users.  

Conclusion

Just like bones, joints, ligaments, and muscles in the human body, taxonomies and ontologies provide the essential structure and connection that allow AI systems to stand up to testing, be reliable, and perform with clarity. At EK we have extensive experience identifying key knowledge assets as well as designing and implementing taxonomies and ontologies to successfully support AI initiatives. If you want to improve the Knowledge Intelligence (KI) of your existing or future AI applications and need help with your taxonomy and ontology efforts, don’t hesitate to get in touch with us. 

The post How Taxonomies and Ontologies Enable Explainable AI appeared first on Enterprise Knowledge.

Though the world is enthralled with the myriad of public AI solutions, many organizations struggle to make the leap to reliable AI within their organizations. A recent MIT report, “The GenAI Divide,” reveals a concerning truth: despite significant investments in AI, 95% of organizations are not seeing any benefits from their AI investments. 

One of the core impediments to achieving AI within your own organization is poor-quality content and data. Without the proper foundation of high-quality content and data, any AI solution will be rife with ‘hallucinations’ and errors. This will expose organizations to unacceptable risks, as AI tools may deliver incorrect or outdated information, leading to dangerous and costly outcomes. This is also why tools that perform well as demos fail to make the jump to production.  Even the most advanced AI won’t deliver acceptable results if an organization has not prepared their content and data.

This blog outlines seven top ways to ensure your content and data are AI-ready. With the right preparation and investment, your organization can successfully implement the latest AI technologies and deliver trustworthy, complete results.

1) Understand What You Mean by “Content” and/or “Data” (Knowledge Asset Definition)

While it seems obvious, the first step to ensuring your content and data are AI-ready is to clearly define what “content” and “data” mean within your organization. Many organizations use these terms interchangeably, while others use one as a parent term of the other. This obviously leads to a great deal of confusion. 

Leveraging the traditional definitions, we define content as unstructured information (ranging from files and documents to blocks of intranet text), and data as structured information (namely the rows and columns in databases and other applications like Customer Relationship Management systems, People Management systems, and Product Information Management systems). You are wasting the potential of AI if you’re not seeking to apply your AI to both content and data, giving end users complete and comprehensive information. In fact, we encourage organizations to think even more broadly, going beyond just content and data to consider all the organizational assets that can be leveraged by AI.

We’ve coined the term knowledge assets to express this. Knowledge assets comprise all the information and expertise an organization can use to create value. This includes not only content and data, but also the expertise of employees, business processes, facilities, equipment, and products. This manner of thinking quickly breaks down artificial silos within organizations, getting you to consider your assets collectively, rather than by type. Moving forward in this article, we’ll use the term knowledge assets in lieu of content and data to reinforce this point. Put simply and directly, each of the below steps to getting your content and data AI-ready should be considered from an enterprise perspective of knowledge assets, so rather than discretely developing content governance and data governance, you should define a comprehensive approach to knowledge asset governance. This approach will not only help you achieve AI-readiness, it will also help your organization to remove silos and redundancies in order to maximize enterprise efficiency and alignment.

2) Ensure Quality (Asset Cleanup)

We’ve found that most organizations are maintaining approximately 60-80% more information than they should, and in many cases, may not even be aware of what they still have. That means that four out of five knowledge assets are old, outdated, duplicate, or near-duplicate. 

There are many costs to this over-retention before even considering AI, including the administrative burden of maintaining this 80% (including the cost and environmental impact of unnecessary server storage), and the usability and findability cost to the organization’s end users when they go through obsolete knowledge assets.

The AI cost becomes even higher for several reasons. First, AI typically “white labels” the knowledge assets it finds. If a human were to find an old and outdated policy, they may recognize the old corporate branding on it, or note the date from several years ago on it, but when AI leverages the information within that knowledge asset and resurfaces it, it looks new and the contextual clues are lost.

Next, we have to consider the old adage of “garbage in, garbage out.” Incorrect knowledge assets fed to an AI tool will result in incorrect results, also known as hallucinations. While prompt engineering can be used to try to avoid these conflicts and, potentially even errors, the only surefire guarantee to avoid this issue is to ensure the accuracy of the original knowledge assets, or at least the vast majority of it.

Many AI models also struggle with near-duplicate “knowledge assets,” unable to discern which version is trusted. Consider your organization’s version control issues, working documents, data modeled with different assumptions, and iterations of large deliverables and reports that are all currently stored. Knowledge assets may go through countless iterations, and most of the time, all of these versions are saved. When ingested by AI, multiple versions present potential confusion and conflict, especially when these versions didn’t simply build on each other but were edited to improve findings or recommendations. Each of these, in every case, is an opportunity for AI to fail your organization.

Finally, this would also be the point at which you consider restructuring your assets for improved readability (both by humans and machines). This could include formatting (to lower cognitive lift and improve consistency) from a human perspective. For both humans and AI, this could also mean adding text and tags to better describe images and other non-text-based elements. From an AI perspective, in longer and more complex assets, proximity and order can have a negative impact on precision, so this could include restructuring documents to make them more linear, chronological, or topically aligned. This is not necessary or even important for all types of assets, but remains an important consideration especially for text-based and longer types of assets.

3) Fill Gaps (Tacit Knowledge Capture)

The next step to ensure AI readiness is to identify your gaps. At this point, you should be looking at your AI use cases and considering the questions you want AI to answer. In many cases, your current repositories of knowledge assets will not have all of the information necessary to answer those questions completely, especially in a structured, machine-readable format. This presents a risk itself, especially if the AI solution is unaware that it lacks the complete range of knowledge assets necessary and portrays incomplete or limited answers as definitive. 

Filling gaps in knowledge assets is extremely difficult. The first step is to identify what is missing. To invoke another old adage, organizations have long worried they “don’t know what they don’t know,” meaning they lack the organizational maturity to identify gaps in their own knowledge. This becomes a major challenge when proactively seeking to arm an AI solution with all the knowledge assets necessary to deliver complete and accurate answers. The good news, however, is that the process of getting knowledge assets AI-ready helps to identify gaps. In the next two sections, we cover semantic design and tagging. These steps, among others, can identify where there appears to be missing knowledge assets. In addition, given the iterative nature of designing and deploying AI solutions, the inability of AI to answer a question can trigger gap filling, as we cover later. 

Of course, once you’ve identified the gaps, the real challenge begins, in that the organization must then generate new knowledge assets (or locate “hidden” assets) to fill those gaps. There are many techniques for this, ranging from tacit knowledge capture, to content inventories, all of which collectively can help an organization move from AI to Knowledge Intelligence (KI).    

4) Add Structure and Context (Semantic Components)

Once the knowledge assets have been cleansed and gaps have been filled, the next step in the process is to structure them so that they can be related to each other correctly, with the appropriate context and meaning. This requires the use of semantic components, specifically, taxonomies and ontologies. Taxonomies deliver meaning and structure, helping AI to understand queries from users, relate knowledge assets based on the relationships between the words and phrases used within them, and leverage context to properly interpret synonyms and other “close” terms. Taxonomies can also house glossaries that further define words and phrases that AI can leverage in the generation of results.

Though often confused or conflated with taxonomies, ontologies deliver a much more advanced type of knowledge organization, which is both complementary to taxonomies and unique. Ontologies focus on defining relationships between knowledge assets and the systems that house them, enabling AI to make inferences. For instance:

<Person> works at <Company>

<Zach Wahl> works at <Enterprise Knowledge>

<Company> is expert in <Topic>

<Enterprise Knowledge> is expert in <AI Readiness>

From this, a simple inference based on structured logic can be made, which is that the person who works at the company is an expert in the topic: Zach Wahl is an expert in AI Readiness. More detailed ontologies can quickly fuel more complex inferences, allowing an organization’s AI solutions to connect disparate knowledge assets within an organization. In this way, ontologies enable AI solutions to traverse knowledge assets, more accurately make “assumptions,” and deliver more complete and cohesive answers. 

Collectively, you can consider these semantic components as an organizational map of what it does, who does it, and how. Semantic components can show an AI how to get where you want it to go without getting lost or taking wrong turns.

5) Semantic Model Application (Tagging)

Of course, it is not sufficient simply to design the semantic components; you must complete the process by applying them to your knowledge assets. If the semantic components are the map, applying semantic components as metadata is the GPS that allows you to use it easily and intuitively. This step is commonly a stumbling block for organizations, and again is why we are discussing knowledge assets rather than discrete areas like content and data. To best achieve AI readiness, all of your knowledge assets, regardless of their state (structured, unstructured, semi-structured, etc), must have consistent metadata applied against them. 

When applied properly, this consistent metadata becomes an additional layer of meaning and context for AI to leverage in pursuit of complete and correct answers. With the latest updates to leading taxonomy and ontology management systems, the process of automatically applying metadata or storing relationships between knowledge assets in metadata graphs is vastly improved, though still requires a human in the loop to ensure accuracy. Even so, what used to be a major hurdle in metadata application initiatives is much simpler than it used to be.

6) Address Access and Security (Unified Entitlements)

What happens when you finally deliver what your organization has been seeking, and give it the ability to collectively and completely serve their end users the knowledge assets they’ve been seeking? If this step is skipped, the answer is calamity. One of the express points of the value of AI is that it can uncover hidden gems in knowledge assets, make connections humans typically can’t, and combine disparate sources to build new knowledge assets and new answers within them. This is incredibly exciting, but also presents a massive organizational risk.

At present, many organizations have an incomplete or actually poor model for entitlements, or ensuring the right people see the right assets, and the wrong people do not. We consistently discover highly sensitive knowledge assets in various forms on organizational systems that should be secured but are not. Some of this takes the form of a discrete document, or a row of data in an application, which is surprisingly common but relatively easy to address. Even more of it is only visible when you take an enterprise view of an organization. 

For instance, Database A might contain anonymized health information about employees for insurance reporting purposes but maps to discrete unique identifiers. File B includes a table of those unique identifiers mapped against employee demographics. Application C houses the actual employee names and titles for the organizational chart, but also includes their unique identifier as a hidden field. The vast majority of humans would never find this connection, but AI is designed to do so and will unabashedly generate a massive lawsuit for your organization if you’re not careful.

If you have security and entitlement issues with your existing systems (and trust me, you do), AI will inadvertently discover them, connect the dots, and surface knowledge assets and connections between them that could be truly calamitous for your organization. Any AI readiness effort must confront this challenge, before your AI solutions shine a light on your existing security and entitlements issues.

7) Maintain Quality While Iteratively Improving (Governance)

Steps one through six describe how to get your knowledge assets ready for AI, but the final step gets your organization ready for AI. With a massive investment in both getting your knowledge assets in the right state for AI and in  the AI solution itself, the final step is to ensure ongoing quality of both. Mature organizations will invest in a core team to ensure knowledge assets go from AI-ready to AI-mature, including:

• Maintaining and enforcing the core tenets to ensure knowledge assets stay up-to-date and AI solutions are looking at trusted assets only;
• Reacting to hallucinations and unanswerable questions to fill gaps in knowledge assets; 
• Tuning the semantic components to stay up to date with organizational changes.

The most mature organizations, those wishing to become AI-Powered organizations, will look first to their knowledge assets as the key building block to drive success. Those organizations will seek ROCK (Relevant, Organizationally Contextualized, Complete, and Knowledge-Centric) knowledge assets as the first line to delivering Enterprise AI that can be truly transformative for the organization. 

If you’re seeking help to ensure your knowledge assets are AI-Ready, contact us at info@enterprise-knowledge.com. 

The post Top Ways to Get Your Content and Data Ready for AI appeared first on Enterprise Knowledge.

The discussion began with an overview of enterprise use cases for each approach, implementation best practices, and a live demo combining LPG and RDF frameworks. During a moderated discussion, panelists also tackled questions such as:

• What are the key benefits RDF graphs and LPGs provide?
• What are the important questions an enterprise architect should ask when designing a graph solution?
• How are recent developments in the AI space and new AI frameworks influencing when to use graph frameworks?

If your organization is exploring linked data capabilities, new AI frameworks, semantic model development, or is ready to kick off its next graph project, contact us here to help you get started.

The post Webinar: Semantic Graphs in Action – Bridging LPG and RDF Frameworks appeared first on Enterprise Knowledge.

The Challenge

Enterprise Knowledge (EK) recently worked with a Federally Funded Research and Development Center (FFRDC) that was having difficulty retrieving relevant content in a large volume of archival scientific papers. Researchers were burdened with excessive search times and the potential for knowledge loss when target documents could not be found at all. To learn more about the client’s use case and EK’s initial strategy, please see the first blog in the Optimizing Historical Knowledge Retrieval series: Standardizing Metadata for Enhanced Research Access.

To make these research papers more discoverable, part of EK’s solution was to add “about-ness” tags to the document metadata through a classification process. Many of the files in this document management system (DMS) were lower quality PDF scans of older documents, such as typewritten papers and pre-digital technical reports that often included handwritten annotations. To begin classifying the content, the team first needed to transform the scanned PDFs into machine-readable text. EK utilized an Optical Character Recognition (OCR) tool, which can “read” non-text file formats for recognizable language and convert it into digital text. When processing the archival documents, even the most advanced OCR tools still introduced a significant amount of noise in the extracted text. This frequently manifested as:

• A table, figure, or handwriting in the document being read in as random symbols and white space.
• Inserting random punctuation where a spot or pen mark may have been on the file, breaking up words and sentences.
• Excessive or misplaced line breaks separating related content.
• Other miscellaneous irregularities in the text that make the document less comprehensible.

The first round of text extraction using out-of-the-box OCR capabilities resulted in many of the above issues across the output text files. This starter batch of text extracts was sent to the classification model to be tagged. The results were assessed by examining the classifier’s evidence within the document for tagging (or failing to tag) a concept. Through this inspection, the team found that there was enough clutter or inconsistency within the text extracts that some irrelevant concepts were misapplied and other, applicable concepts were being missed entirely. It was clear from the negative impact on classification performance that document comprehension needed to be enhanced.

Auto-Classification Auto-Classification (also referred to as auto-tagging) is an advanced process that automatically applies relevant terms or labels (tags) from a defined information model (such as a taxonomy) to your data. Read more about Enterprise Knowledge’s auto-tagging solutions here: 4 Steps to Content Auto-Classification with High Accuracy Expert Analysis: When should my organization use auto-tagging? Part One Knowledge AI: Content Recommender and Chatbot Powered by Auto-Tagging and an Enterprise Knowledge Graph A Guide to Selecting the Right Auto-Tagging Approach

The Solution

To address this challenge, the team explored several potential solutions for cleaning up the text extracts. However, there was concern that direct text manipulation might lead to the loss of critical information if blanket applied to the entire corpus. Rather than modifying the raw text directly, the team decided to leverage a client-side Large Language Model (LLM) to generate additional text based on the extracts. The idea was that the LLM could potentially better interpret the noise from OCR processing as irrelevant and produce a refined summary of the text that could be used to improve classification.

The team tested various summarization strategies via careful prompt engineering to generate different kinds of summaries (such as abstractive vs. extractive) of varying lengths and levels of detail. The team performed a human-in-the-loop grading process to manually assess the effectiveness of these different approaches. To determine the prompt to be used in the application, graders evaluated the quality of summaries generated per trial prompt over a sample set of documents with particularly low-quality source PDFs. Evaluation metrics included the complexity of the prompt, summary generation time, human readability, errors, hallucinations, and of course – precision of  auto-classification results.

The EK Difference

Through this iterative process, the team determined that the most effective summaries for this use case were abstractive summaries (summaries that paraphrase content) of around four complete sentences in length. The selected prompt generated summaries with a sufficient level of detail (for both human readers and the classifier) while maintaining brevity. To improve classification, the LLM-generated summaries are meant to supplement the full text extract, not to replace it. The team incorporated the new summaries into the classification pipeline by creating a new metadata field for the source document. The new ‘summary’ metadata field was added to the auto-classification submission along with the full text extracts to provide additional clarity and context. This required adjusting classification model configurations, such as the weights (or priority) for the new and existing fields.

Large Language Models (LLMs) A Large Language Model is an advanced AI model designed to perform Natural Language Processing (NLP) tasks, including interpreting, translating, predicting, and generating coherent, contextually relevant text. Read more about how Enterprise Knowledge is leveraging LLMs in client solutions here: What is a Large Language Model (LLM)? Choosing the Right Approach: LLMs vs. Traditional Machine Learning for Text Summarization The Role of Semantic Layers with LLMs

The Results

By including the LLM-generated summaries in the classification request, the team was able to provide more context and structure to the existing text. This additional information filled in previous gaps and allowed the classifier to better interpret the content, leading to more precise subject tags compared to using the original OCR text alone. As a bonus, the LLM-generated summaries were also added to the document metadata in the DMS, further improving the discoverability of the archived documents.

By leveraging the power of LLMs, the team was able to clean up noisy OCR output to improve auto-tagging capabilities as well as further enriching document metadata with content descriptions. If your organization is facing similar challenges managing and archiving older or difficult to parse documents, consider how Enterprise Knowledge can assist in optimizing your content findability with advanced AI techniques.

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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!

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As part of our work, we’ve endeavored to help our clients understand the expansive nature of their knowledge, content, and data. The complete range of these materials can be considered based on several different spectra. They can range from tacit to explicit, knowledge to information, structured to unstructured, digital to analog, internal to external, and originated to generated. Before we go deeper into the definition of knowledge assets, let’s first explore each of these variables to understand how vast the full collection of knowledge assets can be for an organization.

• Tacit and Explicit – Tacit content is held in people’s heads. It is inferred instead of explicitly encoded in systems, and does not exist in a shareable or repeatable format. Explicit content is that which has been captured in an independent form, typically as a digital file or entry. Historically, organizations have been focused on converting tacit knowledge to explicit so that the organization could better maintain and reuse it. However, we’ll explain below how the complete definition of a knowledge asset shifts that thinking somewhat.
• Knowledge and Information – Knowledge is the expertise and experience people acquire, making it extremely valuable but hard to convert from tacit to explicit. Information is just facts, lacking expert context. Organizations have both, and documents often mix them.
• Structured and Unstructured – Structured information is machine-readable and system-friendly and unstructured information is human-readable and context-rich. Structured data, like database entries, is easy for systems but hard for humans to understand without tools. Unstructured data, designed for humans, is easier to grasp but historically challenging for machines to process. 
• Digital to Analog – Digital information exists in an electronic format, whereas analog information exists in a physical format. Many global organizations are sitting on mountains of knowledge and information that isn’t accessible (or perhaps even known) to most people in the organization. Making things more complex, there’s also formerly analog information, the many old documents that have been digitized but exist in a middle state where they’re not particularly machine-readable, but are electronic.
• Internal to External – Internal content targets employees, while external content targets customers, partners, or the public, with differing tones and styles, and often greater governance and overall rigor for external content. Both types should align, but are treated differently. You can also consider the content created by your organization versus external content purchased, acquired, or accessed from external sources. From this perspective, you have much greater control over your organization’s own content than that which was created or is owned externally.
• Originated and Generated – Originated content already exists within the organization as discrete items within a repository or repositories, authored by humans. Explicit content, for example, is originated. It was created by a person or people, it is managed, and identified as a unique item. Any file you’ve created before the AI era falls into this category. With Generative AI becoming pervasive, however, we must also consider generated information, derived from AI. These generated assets (synthetic assets) are automatically created based on an organization’s existing (originated) information, forming new content that may not possess the same level of rigor or governance.

If we were to go no further than the above, most organizations would already be dealing with petabytes of information and tons of paper encompassing years and years. However, by thinking about information based on its state (i.e. structured or unstructured, digital or analog, etc), or by its use (i.e. internal or external), organizations are creating artificial barriers and silos to knowledge, as well as duplicating or triplicating work that should be done at the enterprise level. Unfortunately, for most organizations, the data management group defines and oversees data governance for their data, while the content management group defines and oversees content governance for their content. This goes beyond inefficiency or redundancy, creating cost and confusion for the organization and misaligning how information is managed, shared, and evolved. Addressing this issue, in itself, is already a worthy challenge, but it doesn’t yet fully define a knowledge asset or how thinking in terms of knowledge assets can deliver new value and insights to an organization.

If you go beyond traditional digital content and begin to consider how people actually want to obtain answers, as well as how artificial intelligence solutions work, we can begin to think of the knowledge an organization possesses more broadly. Rather than just looking at digital content, we can recognize all the other places, things, and people that can act as resources for an organization. For instance, people and the knowledge and information they possess are, in fact, an asset themselves. The field of KM has long been focused on extracting that knowledge, with at best mixed results. However, in the modern ecosystem of KM, semantics, and AI, we can instead consider people themselves as the asset that can be connected to the network. We may still choose to capture their knowledge in a digital form, but we can also add them to the network, creating avenues for people to find them, learn from them, and collaborate with them while mapping them to other assets.

In the same way, products, equipment, processes, and facilities can all be considered knowledge assets. By considering all of your organizational components not as “things,” but as containers of knowledge, you move from a world of silos to a connected and contextualized network that is traversable by a human and understandable by a machine. We coined the term knowledge assets to express this concept. The key to a knowledge asset is that it can be connected with other knowledge assets via metadata, meaning it can be put into the organization’s context. Anything that can hold metadata and be connected to other knowledge assets can be an asset.

Another set of knowledge assets that are quickly becoming critical for mature organizations are components of AI orchestration. As organizations build increasingly complex systems of agents, models, tools, and workflows, the logic that governs how these components interact becomes a form of operational knowledge in its own right. These orchestration components encode decisions, institutional context, and domain expertise, meaning they are worthy of being treated as first-class knowledge assets. To fully harness the value of AI, orchestration components should be clearly defined, governed, and meaningfully connected to the broader knowledge ecosystem.

Put into practice, a mature organization could create a true web of knowledge assets to serve virtually any use case. Rather than a simple search, a user might instead query their system to learn about a process. Instead of getting a link to the process documentation, they get a view of options, allowing them to read the documentation, speak to an expert on the topic, attend training on the process, join a community of practice working on it, or visit an application supporting it. 

A new joiner to your organization might be given a task to complete. Currently, they may hunt around your network for guidance, or wait for a message back from their mentor, but if they instead had a traversable network of all your organization’s knowledge assets, they could begin with a simple search on the topic of the task, find a past deliverable from a related task, which would lead them to the author of that task from whom they could seek guidance, or instead to an internal meetup of professionals deemed to have expertise in that task.

If we break these silos down, add context and meaning via metadata, and begin to treat our knowledge assets holistically, we’re also creating the necessary foundations for any AI solutions to better understand our enterprise and deliver complete answers. This means that we’re building the better answer for our organization immediately, while also enabling our organization to leverage AI capabilities faster, more consistently, and more reliably than others.

The idea of knowledge assets will be a shift both in mindset and strategies, with impacts potentially rippling deeply through your org chart, technologies, and culture. However, the organizations that embrace this concept will achieve an enterprise most closely resembling how humans naturally think and learn and how AI is best equipped to deliver.

If you’re ready to take the next big step in organizational knowledge and maturity, contact us, and we will bring all of our knowledge assets to bear in support. 

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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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To address these challenges, over the last few years, the semantic layer has emerged as a framework and solution to support a wide range of use cases, including content and data organization, integration, semantic search, knowledge discovery, data governance, and automation. By connecting disparate data sources, a semantic layer enables richer queries and supports programmatic knowledge extraction and modernization.

A semantic layer functions by utilizing metadata and taxonomies to create structure, business glossaries to align on the meaning of terms, ontologies to define relationships, and a knowledge graph to uncover hidden connections and patterns within content and data. This combination allows organizations to understand their information better and unlock greater value from their knowledge assets. Moreover, AI is tapping into this structured knowledge to generate contextual, relevant, and explainable answers.

So, what are the specific problems and use cases organizations are solving with a semantic layer? The case studies and use cases highlighted in this article are drawn from our own experience from recent projects and lessons learned, and demonstrate the value of a semantic layer not just as a technical foundation, but as a strategic asset, bridging human understanding with machine intelligence.

Semantic Layer Advancing Search and Knowledge Discovery: Getting Answers with Organizational Context

Over the past two decades, we have completed 50-70 semantic layer projects across a wide range of industries. In nearly every case, the core challenges revolve around age-old knowledge management and data quality issues—specifically, the findability and discoverability of organizational knowledge. In today’s fast-paced work environment, simply retrieving a list of documents as ‘information’ is no longer sufficient. Organizations require direct answers to discover new insights. Most importantly, organizations are looking to access data in the context of their specific business needs and processes. Traditional search methods continue to fall short in providing the depth and relevance required to make quick decisions. This is where a semantic layer comes into play. By organizing and connecting data with context, a semantic layer enables advanced search and knowledge discovery, allowing organizations to retrieve not just raw files or data, but answers that are rich in meaning, directly tied to objectives, and action-oriented. For example, supported by descriptive metadata and explicit relationships, semantic search, unlike keyword search, understands the meaning and context of our queries, leading to more accurate and relevant results by leveraging relationships between entities and concepts across content, rather than just matching keywords. This powers enterprise search solutions and question-answering systems that can understand and answer complex questions based on your organization’s knowledge. 

Case Study: For our clients in the pharmaceuticals and healthcare sectors, clinicians and researchers often face challenges locating the most relevant medical research, patient records, or treatment protocols due to the vast amount of unstructured data. A semantic layer facilitates knowledge discovery by connecting clinical data, trials, research articles, and treatment guidelines to enable context-aware search. By extracting and classifying entities like patient names, diagnoses, medications, and procedures from unstructured medical records, our clients are advancing scientific discovery and drug innovation. They are also improving patient care outcomes by applying the knowledge associated with these entities in clinical research. Furthermore, domain-specific ontologies organize unstructured content into a structured network, allowing AI solutions to better understand and infer knowledge from the data. This map-like representation helps systems navigate complex relationships and generate insights by clearly articulating how content and data are interconnected. As a result, rather than relying on traditional, time-consuming keyword-based searches that cannot distinguish between entities (e.g., “drugs manufactured by GSK” vs. “what drugs treat GSK”?), users can perform semantic queries that are more relevant and comprehend meaning (e.g., “What are the side effects of drug X?” or “Which pathways are affected by drug Y?”), by leveraging the relationships between entities to obtain precise and relevant answers more efficiently.

Semantic Layer as a Data Product: Unlocking Insights by Aligning & Connecting Knowledge Assets from Complex Legacy Systems

The reality is that most organizations face disconnected data spread across complex, legacy systems. Despite well-intended investments and efforts in enterprise knowledge and data management efforts, typical repositories often remain outdated, including legacy applications, email, shared network drives, folders, and information saved locally on desktops or laptops. Global investment banks, for instance, struggle with multiple outdated record management, risk, and compliance tracking systems, while healthcare organizations continue to contend with disparate electronic health record (EHR) systems and/or Electronic Medical Records (EMRs). These challenges hinder the ability to communicate and share data with newer, more advanced systems, are typically not designed to handle the growing demands of modern data, and result in businesses grappling with siloed information in legacy systems that make regulatory reporting onerous, manual, and time-consuming. The solution to these issues lies in treating the semantic layer as an abstracted data product itself whereby organizations employ semantic models to connect fragmented data from legacy systems, align shared terms across these systems, provide descriptive metadata and meaning, and connect data to empower users to query and access data with additional context, relevance, and speed. This approach not only streamlines decision-making but also modernizes data infrastructure without requiring a complete overhaul of existing systems.

Case Study: We are currently working with a global financial firm to transform their risk management program. The firm manages 21 bespoke legacy applications, each handling different aspects of their risk processes where compiling a comprehensive risk report typically took up to two months, and answering key questions like, “What are the related controls and policies relevant to a given risk in my business?” was a complex, time-consuming task to tackle. The firm engaged with us to augment their data transformation initiatives with a semantic layer and ecosystem. We began by piloting a conceptual graph model of their risk landscape, defining core risk taxonomies to connect disparate data across the ecosystem. We used ontologies to explicitly capture the relationships between risks, controls, issues, policies, and more. Additionally, we leveraged large language models (LLMs) to summarize and reconcile over 40,000 risks, which had previously been described by assessors using free text.

This initiative provided the firm with a simplified, intuitive view where users could quickly look up a risk and find relevant information in seconds via a graph front-end. Just 1.5 years later, the semantic layer is powering multiple key risk management tools, including a risk library with semantic search and knowledge panels, four recommendation engines, and a comprehensive risk dashboard featuring threshold and tolerance analysis. The early success of the project was due to a strategic approach: rather than attempting to integrate the semantic data model across their legacy applications, the firm treated it as a separate data product. This allowed risk assessors and various applications to use the semantic layer as modular “Lego bricks,” enabling flexibility and faster access to critical insights without disrupting existing systems.

Semantic Layer for Data Standards and Interoperability: Navigating the Dynamism of Data & Vendor Limitations 

Various data points suggest that, today, the average tenure of an S&P 500 technology company has dropped dramatically from 85 years to just 12-15 years. This rapid turnover reflects the challenges organizations face with the constant evolution of technology and vendor solutions. The ability to adapt to new tools and systems, while still maintaining operational continuity and reducing risk, is a growing concern for many organizations. One key solution to this challenge is using frameworks and standards that are created to ensure data interoperability, offering the flexibility to navigate data organization and abstracting data from system and vendor limitations. A proper semantic layer employs universally adopted semantic web (W3C) and data modeling standards to design, model, implement, and govern knowledge and data assets within organizations and across industries. 

Case Study: A few years ago, one of our clients faced a significant challenge when their graph database vendor was acquired by another company, leading to a sharp increase in both license and maintenance fees. To mitigate this, we were able to swiftly migrate all of their semantic data models from the old graph database to a new one within less than a week (the fastest migration we’ve ever experienced). This move saved the client approximately $2 million over three years. The success of the migration was made possible because their data models were built using semantic web standards (RDF-based), ensuring standards based data models and interoperability regardless of the underlying database or vendor. This case study highlights a fundamental shift in how organizations approach data management. 

Semantic Layer as the Framework for a Knowledge Portal 

The growing volume of data, the need for efficient knowledge sharing, and the drive to enhance employee productivity and engagement are fueling a renewed interest in knowledge portals. Organizations are increasingly seeking a centralized, easily accessible view of information as they adopt more data-driven, knowledge-centric approaches. A modern Knowledge Portal consolidates and presents diverse types of organizational content, ranging from unstructured documents and structured data to connections with people and enterprise resources, offering users a comprehensive “Enterprise 360” view of related knowledge assets to support their work effectively.

While knowledge portals fell out of favor in the 2010s due to issues like poor content quality, weak governance, and limited usability, today’s technological advancements are enabling their resurgence. Enhanced search capabilities, better content aggregation, intelligent categorization, and automated integrations are improving findability, discoverability, and user engagement. At its core, a Knowledge Portal comprises five key components that are now more feasible than ever: a Web UI, API layers, enterprise search engine, knowledge graph, and taxonomy/ontology management tools—half of which form part of the semantic layer.

Case Study: A global investment firm managing over $250 billion in assets partnered with us to break down silos and improve access to critical information across its 50,000-employee organization. Investment professionals were wasting time searching for fragmented, inconsistent knowledge stored across disparate systems, often duplicating efforts and missing key insights. We designed and implemented a Knowledge Portal integrating structured and unstructured content, AI-powered search, and a semantic layer to unify data from over 12 systems including their primary CRM (DealCloud), additional internal/external systems, while respecting complex access permissions and entitlements. A big part of the portal involved a semantic layer architecture which included the rollout of metadata and taxonomy design, ontology and graph modeling and storage, and an agile development process that ensured high user engagement and adoption. Today, the portal connects staff to both information and experts, enabling faster discovery, improved collaboration, and reduced redundancy. As a result, the firm saw measurable gains in their productivity, staff and client onboarding efficiency, and knowledge reuse. The company continues to expand the solution to advanced use cases such as semantic search applications and robust global use cases.

Semantic Layer for Analytics-Ready Data 

For many large-scale organizations, it takes weeks, sometimes months, for analytics teams to develop “insights” reports and dashboards that fulfill data-driven requests from executives or business stakeholders. Navigating complex systems and managing vast data volumes has become a point of friction between established software engineering teams managing legacy applications and emerging data science/engineering teams focused on unlocking analytics insights or data products. Such challenges persist as long as organizations work within complex infrastructures and proprietary platforms, where data is fragmented and locked in tables or applications with little to no business context. This makes it extremely difficult to extract useful insights, handle the dynamism of data, or manage the rising volumes of unstructured data, all while trying to ensure that data is consistent and trustworthy. 

Picture this scenario and use case from a recent engagement: a global retailer, with close to 40,000 store locations across the globe had recently migrated its data to a data lake in an attempt to centralize their data assets. Despite the investment, they still faced persistent challenges when new data requests came from their leadership, particularly around store performance metrics. Here’s a breakdown of the issues:

• Each time a leadership team requested a new metric or report, the data team had to spin up a new project and develop new data pipelines.
• 5-6 months was required for a data analyst to understand the content/data related to these metrics—often involving petabytes of raw data.
• The process involved managing over 1500 ETL pipelines, which led to inefficiencies (what we jokingly called “death by 2,000 ETLs”).
• Producing a single dashboard for C-level executives cost over $900,000.
• Even after completing the dashboard, they often discovered that the metrics were being defined and used inconsistently. Terms like “revenue,” “headcount,” or “store performance” were frequently understood differently depending on who worked on the report, making output reports unreliable and unusable. 

This is one example of why organizations are now seeking and investing in a coherent, integrated way to bridge these gaps and understand their vast data ecosystems. Because organizations often work with complex systems, ranging from CRMs and ERPs to data lakes and cloud platforms, extracting meaningful insights from this data requires a coherent, integrated view that can bridge these gaps. This is where the semantic layer serves as a pragmatic tool that enables organizations to bridge these gaps, streamline processes, and transform how data is used across departments. Specifically for these use cases, semantic data is gaining significant traction across diverse pockets of the organization as the standard interpreter between complex systems and business goals. 

Semantic Layer for Delivering Knowledge Intelligence 

Another reality many organizations are grappling with today is that basic AI algorithms trained in public data sets may not work well on organization and domain-specific problems, especially in domains where industry preferences are relevant. Thus, organizational knowledge is a prerequisite for success, not just for generative AI, but for all applications of enterprise AI and data science solutions. This is where experience and best practices in knowledge and data management lend the AI space effective and proven approaches to sharing domain and institutional knowledge. Especially for technical teams that are tasked with making AI “work” or provide value for their organization, they are looking for programmatic ways for explicitly modeling relationships between various data entities, providing business context to tabular data, and extracting knowledge from unstructured content, ultimately delivering what we call Knowledge Intelligence.

A well-implemented semantic layer abstracts the complexities of underlying systems and presents a unified, business-friendly view of data. It transforms raw data into understandable concepts and relationships, as well as organizes and connects unstructured data. This makes it easier for both data teams and business users to query, analyze, and understand their data, while making this organizational knowledge machine-ready and readable. The semantic layer standardizes terminology and data models across the enterprise, and provides the required business context for the data. By unifying and organizing data in a way that is meaningful to the business, it ensures that key metrics are consistent, actionable, and aligned with the company’s strategic objectives and business definitions.

Case Study: With the aforementioned global retailer, as their data and analytics teams worked to integrate siloed data and unstructured content, we partnered with them to build a semantic ecosystem that streamlined processes and provided the business context needed to make sense of their vast data. Our approach included: 

• Standardized Metadata and Vocabularies: Developed standardized metadata and vocabularies to describe their key enterprise data assets, especially for store metrics like sales performance, revenue, etc. This ensured that everyone in the organization used the same definitions and language when discussing key metrics. 
• Explicitly Defined Concepts and Relationships: We used ontologies and graphs to define the relationships between various domains such as products, store locations, store performance, etc. This created a coherent and standardized model that allowed data teams to work from a shared understanding of how different data points were connected.
• Data Catalog and Data Products: We helped the retailer integrate these semantic models into a data catalog that made data available as “data products.” This allowed analysts to access predefined, business-contextualized data directly, without having to start from scratch each time a new request was made.

This approach reduced report generation steps from 7 to 4 and cut development time from 6 months to just 4-5 weeks. Most importantly, it enabled the discovery of previously hidden data, unlocking valuable insights to optimize operations and drive business performance.

Semantic Layer as a Foundation for Reliable AI: Facilitating Human Reasoning and Explainable Decisions

Emerging technologies (like GenAI or Agentic AI) are democratizing access to information and automation, but they also contribute to the “dark data” problem—data that exists in an unstructured or inaccessible format but contains valuable, sensitive, or bad information. While LLMs have garnered significant attention in conversational AI and content generation, organizations are now recognizing that their data management challenges require more specialized, nuanced, and somewhat ‘grounded’ approaches that address the gaps in explainability, precision, and the ability to align AI with organizational context and business rules. Without this organizational context, raw data or text is often messy, outdated, redundant, and unstructured, making it difficult for AI algorithms to extract meaningful information. The key step to addressing this AI problem involves the ability to connect all types of organizational knowledge assets, i.e., using shared language, involving experts, related data, content, videos, best practices, lessons learned, and operational insights from across the organization. In other words, to fully benefit from an organization’s knowledge and information, both structured and unstructured information, as well as expert knowledge, must be represented and understood by machines. A semantic layer provides AI with a programmatic framework to make organizational context, content, and domain knowledge machine-readable. Techniques such as data labeling, taxonomy development, business glossaries, ontology, and knowledge graph creation make up the semantic layer to facilitate this process. 

Case Study: We have been working with a global foundation that had previously been through failed AI experiments as part of a mandate from their CEO for their data teams to “figure out a way” to adopt LLMs to evaluate the impact of their investments on strategic goals by synthesizing information from publicly available domain data, internal investment documents, and internal investment data. The challenge for previously failed efforts lay in connecting diverse and unstructured information to structured data and ensuring that the insights generated were precise, explainable, reliable, and actionable for executive stakeholders. To address these challenges, we took a hybrid approach that leveraged LLMs that were augmented through advanced graph technology and a semantic RAG (Retrieval Augmented Generation) agentic workflow. To provide the relevant organizational metrics and connection points in a structured manner, the solution leveraged an Investment Ontology as a semantic backbone that underpins their disconnected source systems, ensuring that all investment-related data (from structured datasets to narrative reports) is harmonized under a common language. This semantic backbone supports both precise data integration and flexible query interpretation. To effectively convey the value of this hybrid approach, we leveraged a chatbot that served as a user interface to toggle back and forth between the basic GPT model vs. the graph RAG solution. The solution consistently outperformed the basic/naive LLMs for complex questions, demonstrating the value of semantics for providing organizational context and alignment and ultimately, delivering coherent and explainable insights that bridged structured and unstructured investment data, as well as provided a transparent AI mapping that allowed stakeholders to see exactly how each answer was derived.

Closing 

Now more than ever, the understanding and application of semantic layers are rapidly advancing. Organizations across industries are increasingly investing in solutions to enhance their knowledge and data management capabilities, driven in part by the growing interest to benefit from advanced AI capabilities. 

If you are looking to learn more about how organizations are approaching semantic layers at scale or are you seeking to unstick a stalled initiative, you can learn more from our case studies or contact us if you have specific questions.

The post Top Semantic Layer Use Cases and Applications (with Real World Case Studies)   appeared first on Enterprise Knowledge.

We have one of the largest pools of certified on-the-job experts who bring hands-on experience delivering advanced information and data science, machine learning, LLM, ontology, graph, and AI solutions. We will partner with your organization to provide advisory and consulting services, augmenting your teams’ expertise to support with the following activities: 

• Full lifecycle support, from use case definition and model development to AI program oversight.
• Data preparation using standard data modeling and AI approaches, such as the CRISP-DM process model.
• Transition of experimental models into production.
• Implementation of automatic drift modeling to prevent stale models.
• Development of AIOps and MLOps – cloud as well as on-prem infrastructures. 
• Modular component development with scalability and extensibility in mind. 
• Program facilitation & executive presentations.
• Facilitation of knowledge transfer sessions with your in-house AI/ML engineers.

By the end of the Expertise Augmentation for Full Lifecycle AI/ML Operations engagement, your organization will have:

• Full support from experts and certified professionals with proven experience devoted to expanding your AI capabilities.
• Direct access to a library of technical components, including tested models, frameworks, architecture, and scripts to catapult your AI/ML model journey. 
• Program management, oversight, and quality control support that guarantees an Agile approach to successful delivery, consistent alignment with business goals, and sustainability throughout the model lifecycle.
• Comprehensive pair-programming and role-based training to develop your teams’ expertise.

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