EK hosts a public knowledge base of over 700 articles on KM, Semantic Layer, and AI thought leadership, produces the top-rated KM podcast, Knowledge Cast, and has published the definitive book on KM benchmarking and technologies, Making Knowledge Management Clickable. 

In addition to the Top 100 List, EK was also recently recognized by KMWorld on their list of AI Trailblazers. You can read EK VP Lulit Tesfaye’s thoughts on that recognition here. These new areas of recognition come on the heels of Honda recognizing Enterprise Knowledge as one of their suppliers of the year, and Inc. Magazine listing EK as one of the best places to work in the United States.

The post EK Again Recognized as Leading Services Provider by KMWorld appeared first on Enterprise Knowledge.

Suppliers categorized as “indirect procurement” provide Honda companies with a wide variety of equipment, products, and services to support business operations and manufacturing facilities. Selected from a roster of over 6,000 suppliers, EK was one of only six organizations awarded in the New Suppliers category, which recognizes those who have demonstrated outstanding implementation practices and provided value-based products, services, or programs.

The theme of the 2025 conference, ‘Flexibility for Tomorrow,’ is of particular importance to the Honda supplier network, as the company is transforming nearly every aspect of its operations to support its ability to meet customers’ needs and changing market conditions. 

“‘Flexibility for Tomorrow’ means that Honda is designing supply chains and partnerships that aren’t just responsive, but resilient, nimble, scalable, reimagined, and able to adapt without losing momentum,” said Jake Schmidt, procurement strategy director for North American Indirect Procurement at Honda. “We’re excited by the possibilities ahead and we’re grateful to work with business partners who share this vision.” 

Honda’s own press release includes more details that can be viewed on their website.

“We at Enterprise Knowledge are deeply honored to receive this recognition, but moreover to be trusted as Honda’s partner in this work,” said Zach Wahl, CEO of Enterprise Knowledge. 

In addition to this recognition from Honda, EK was recently named one of the Top 100 Companies in Knowledge Management, and the KMWorld AI 100 for leadership in Artificial Intelligence. EK has consecutively won both awards for several years. This year, EK was also recognized by both Inc. Magazine and the Washington Business Journal as a best place to work, marking over a decade of similar recognition for employee satisfaction and corporate culture.

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The year marked our twelfth consecutive year of growth. This consistency is only possible because we’ve maintained the vast majority of existing clients while winning new clients and accounts. This is a simple formula, but one that has been the hallmark of our steady growth, and with that growth comes the ability to deliver enterprise-level engagements with massive business impact for our clients. A great deal has changed at EK since our inception in 2013 and that first “Year in Review” blog in 2016, but our commitment to our team members, our clients, and the industry as a whole has remained true, as has that simple formula for growth. 

As we now enter 2025, I feel equally enthusiastic and confident about what the year holds. We’ve long been accurately predicting the trends that shape our industry, which has allowed us to maintain our position of leadership and innovation in a highly dynamic set of fields. As we identified the Semantic Layer as the missing link in the AI Revolution, we’ve found ourselves leading and delivering at the intersections of Knowledge Management, Data Management, Information Management, and Information Technology. This translated to exceptional growth and engagement in the past year, resulting in record revenues, backlog of work, and overall headcount as we lead the way into 2025. 

As has now become tradition, I’ll use EK’s six guiding principles to detail the year in review.

People – Our number one asset is our people. We invest in them and ensure they possess the knowledge and resources to serve our clients to the highest degree possible.

I remember back in 2015 we convened the team to help define EK’s guiding principles. At the time we were a team of six, without an office of our own, so a client graciously lent a conference room for us to work in. The first principle we defined was the above – our commitment to our People, and our commitment to investing in them. I’ve always said that my most important responsibility at EK is to ensure every team member feels honored, supported, and set up to grow at EK. Our high retention and even higher levels of quality and performance are a testament to that.

We maintained and expanded all of the elements of our benefits programs, EK Balance, and EK Grow, all dedicated to delivering the triad of learning, engagement, and collaboration to our team members. This included our continued learning benefit of a guaranteed $3,000 per year per employee for external training and conferences, our year-long onboarding program called Kamp EK (to which we added new modules for Project Managers and People Managers), our Lifelong Learning benefit of $1,000 per year for non-work related development, Pitches and Pints (where EK leadership coaches employees on public speaking over dinner), and EK’s Learning Cohorts, where EK leadership and subject matter experts teach new joiners EK’s core approaches and methodologies to quickly upskill them. I am commonly asked which traits I value most in new employees, and a passion for learning is always one of my answers. We’ve now established a suite of ways to reward and foster that passion.

In the office, we hosted yoga classes, massage days, breakfasts served by EK leadership, make-your-own-sundae bars, wine tastings, and other events dedicated to foster collaboration and build community amongst our rapidly growing team. At the same time, we recognized that much of our twenty percent growth in headcount came through our nationwide recruiting, hiring fully remote employees outside our headquarters area. To support the coast-to-coast community-building of our team, we flew everyone to headquarters three times over the course of the year for our traditional events of the annual Gala, Purple Elephant, and Pirate Ship Cruise. 

We took advantage of each of these all-company gatherings to get to know each other better and celebrate, but also to conduct project-based working sessions and plan future EK initiatives in order to better deliver for our clients and better support and reward our employees. Preceding the recent Purple Elephant, our December holiday party, I asked the company to envision what EK would look like in 2027, and EK’s leadership team will take those visions into our planning for the years to come in order to deliver for the company, finding new ways to help the team continue to grow.

Thought Leadership – We serve as leaders in the industry, sharing our knowledge and expertise, guiding the development of knowledge, data, and information practices, and supporting the community.

Last year I celebrated a major milestone with our knowledge base: the publication of our 500th article. This year we blew past that, and now have over 600 blogs, white papers, case studies, slide presentations, videos, and podcast episodes in our knowledge base. I believe this makes us the largest single repository of Knowledge Management resources that are free and open to the public. On top of our knowledge base, our book, Making Knowledge Management Clickable, continued to receive attention and platitudes, and our podcast, “Knowledge Cast,” maintained its place as a top podcast in the field. 

Over the course of the year, we drove the conversation in the industry, identifying new trends and providing detailed discussions, articles, and presentations with a focus on concrete business value and outcomes. We spoke at over twenty different conferences and events, notably keynoting both the Henry Stewart Semantic Data Conference (twice), as well as the inaugural Knowledge Summit Dublin event. At KMWorld, we once again had over a dozen different EK team members present. More importantly, overall we had nearly forty different team members speak at over twenty different conferences around the world. This is particularly notable to me as it demonstrates EK’s depth of thought leadership. We’re not just one or two big names in the industry – we are a deep pool of industry professionals and thought leaders helping to shape the future of the field more and more each year.

Also of particular note, we produced our own thought leadership event in Europe this past year. Recognizing a gap in industry discourse, we convened the Semantic Layer Symposium in Munich, Germany. The event brought together a select group of thought leaders and practitioners in the field of Knowledge Management, Data Management, Information Management, Artificial Intelligence, and the field of Semantics for a sales-free day of learning and discussion. Stay tuned for an upcoming announcement about 2025’s event.

All of our work again helped us to be recognized by the industry. Again this year, KMWorld and Info Today recognized EK as one of the 100 Companies That Matter in KM, as well as one of the AI 100: The Companies Empowering Intelligent Knowledge Management. Though I am always proud of the recognition, what matters a great deal more is how I see the industry as a whole, following our words and actions. This has allowed us to continue to attract the greatest talent in the field, while simultaneously spurring us to innovate further. As the industry leaders, our job isn’t just to suggest what’s next, it’s to deliver what’s next, and that is what we’ve been doing year after year.

Transparency – We communicate clearly and openly, ensuring the highest level of quality and accountability for our company’s management, in our service to our clients, and with respect to our colleagues.

As I’ve shared in the past, we endeavor to run EK as openly as possible, sharing company goals, misses, challenges, and mistakes with the team throughout the year. We hold twice monthly all-company knowledge shares with the team to do just that, and it has created a culture of openness, which it is my job to foster. During each year’s all-hands meetings, I have introduced a list of both challenges and accomplishments. Looking back, I’m proud to see how many of the previous year’s listed challenges become accomplishments in the following year, as we’ve rallied as a company to face these challenges directly and take concrete action.

Moreover, during these all-hands meetings, I also establish our goals for growth in the year to come. In a year where many services companies struggled, and where our space encountered greater competition, I was particularly proud to share with the team that we beat all of our growth and performance targets for the year.

This year, we officially established EK’s Project Management arm under a new Director in order to better measure EK’s performance, ensure quality on the larger and larger enterprise engagements with which we’re being trusted by our clients, and develop new project managers and project management standards to keep pace with the highly technical and complex nature of our work.

Recognizing the increasingly hybrid nature and growing size of our project teams, we also established a new project excellence tradition dedicated to building team identity and collaboration. Starting this year, we defined a standard of bringing all enterprise project teams together in the early stages of a new project to ensure the team understands their roles, is poised to be successful, and is dedicated to serving their client’s needs. This is done at EK’s expense, not the client’s, again, with a focus on quality, performance, and collaboration.

Though we’ve embraced remote and hybrid work in a post-Covid world, we’ve also found greater community at our headquarters, as more and more team members choose to spend more time in the office. Bucking the industry trend, we’re taking on more office space and building it out to suit a hybrid workforce, while still creating a range of spaces for in-person collaboration and celebration. This was partly made necessary by the fact that we brought in our largest class of new college and university hires ever and will continue that in 2025. Our investment in individuals at the beginning of their careers continues to reap dividends, infusing EK with new ideas and energy, and is another facet of our commitment to the community and the industry.

Partnership – We partner with our clients, building meaningful relationships founded on a sustained commitment to mutual success.

With our clients, our industry, and our community, we’ve continued our commitment to partnership and impact. I’ve already shared in the previous sections the greatest outcome of our spirit of partnership with our clients. They choose to remain with us year after year, returning to seek our help, re-engaging on new initiatives, and seeking our guidance on their latest challenges. We’ve cheered on clients as they’ve been promoted or taken on new opportunities, and it is the greatest compliment we could ask for that even when they move organizations, they choose to bring EK along with them.

As our clients have consistently placed trust in us and treated us as partners, we’ve also sought ways to partner with the community of which we’re a part. This past year, EK sponsored over a dozen different conferences. I was particularly proud to have EK sign up as the first sponsor for the new Knowledge Summit in Dublin, once again showcasing our willingness to put money back into the industry and help fill gaps.

We also engaged with our community philanthropically. Notably, we continued our long-standing support for the Wolf Trap Institute for Early Learning Through the Arts, helping to ensure that kids in our area will have access to arts and music education. Through our annual Know Shave Knowvember, we had our own employees choose the causes that matter most to them, making donations to a broad array of local, national, and global organizations selected by our team members. With our paid volunteer time benefit, still other teams at EK were free to volunteer their time, supporting, amongst others, some of the DC area’s soup kitchens. This is a great example of something that’s good for EK, good for our team members, and good for the community. In 2025, we’ll continue to seek opportunities for partnership at all of these levels.

Integration – We provide our customers with the full range of EK’s expertise, integrating all of our services and resources to ensure the most significant business value.

The guiding principle of integration has continued to take on greater meaning for us and our clients, in a way becoming a description of what EK delivers, not just what we do. We founded EK with the goal of being the world’s largest and most recognized provider of Knowledge Management services. We’ve achieved that, and have also become much more by fulfilling every organization’s goal of integrating their knowledge assets, regardless of type, location, form, or repository. This is the merging of Knowledge Management, Data Management, Information Management, and Content Management via the Semantic Layer and Artificial Intelligence. It is where we lead, where we’re seeing the greatest growth, and where we’re making the most impact for our clients.

One of the great outcomes of this integration of services is growth not just for EK as a whole, but for our average project size, which grew a whopping forty percent over the last year. With this growth has come greater project complexity, which we’ve risen to address via the new Project Management Office, new standards, and new positions. We’ve relied on EK’s consistent culture of community and kindness, asking thought leaders in each of these disparate fields to come together and learn from each other. I don’t believe we would have been successful scaling as gracefully as we’ve done without the foundation of our culture to drive that collaboration. As a result, we’ve smashed together disparate fields to create something new, and far more valuable than its collective parts.

Overall, this new approach to integrated services and delivery of organization-shifting solutions has resulted in greater maturity for us, as we’ve rapidly grown from startup, to scaleup, to a fully realized and enterprise-ready organization in just a decade, and are now helping a retinue of the world’s largest and most recognizable brands to transform their organizations.

Energy – We share our enthusiasm with our clients and colleagues, leveraging our excitement to achieve meaningful change.

With consistent, yet extraordinary growth, new, larger projects, and new ways to deliver for the enterprise, I am carrying a great deal of energy and excitement into the year. More importantly, I’m seeing this same energy and excitement mirrored by individuals at all levels of EK in a way that feels fresh, and actually reminiscent of our early days as a startup. We have more exciting announcements to share as the year comes into focus, so keep following along as we set the conversation, deliver for the enterprise, and integrate everything we have, for everything you need.

On behalf of Enterprise Knowledge, Happy New Year and best wishes for 2025!

The post EK’s Year in Review – 2024 appeared first on Enterprise Knowledge.

Understanding the Semantic Layer

Before we explore the role of content in the semantic layer, a quick refresher on what a semantic layer is. In a recent post by EK’s Lulit Tesfaye, Partner and the VP for Knowledge & Data Services and Engineering, she proposes this definition:

A semantic layer is a standardized framework that organizes and abstracts organizational data (structured, unstructured, semi-structured) and serves as a data connector for data and knowledge. Larger than a data fabric, which is more focused on structured data, a semantic layer connects all organizational knowledge assets via a well-defined and standardized semantic framework, including content items, data, files, and media. It allows organizations to represent organizational knowledge and domain meaning to systems and applications, defining the relationship between content and data. 

Beyond the data fabric, a semantic layer that incorporates content makes it possible to organize and enrich content with semantic meaning, empowering consuming systems and end users with advanced content management, discovery, and analytical capabilities.

Enriching Content in the Semantic Layer

The semantic layer provides contextual understanding to content by capturing the meaning, relationships, and domain-specific knowledge embedded within it by incorporating data about the content. Through techniques such as semantic tagging and entity extraction, the content is enriched with metadata that describes its context, topics, and entities. This contextual understanding enables users to interpret and analyze content and other integrated data sources in a more meaningful way, leading to deeper insights and better decision-making.

When content is broken down into a dynamic content model, it becomes possible to realize the benefits of the semantic layer at a granular level. Dynamic content lets you use your content as data and realize the full value of your business assets by making those structured content elements available to various consuming systems and experiences. When those structured content elements are individually enriched with metadata like tags, categories, and keywords, they become building blocks that can be targeted and assembled at the point of need, creating contextually relevant content experiences. The semantic layer allows you to find the content that has an answer and directs you to the answer itself. Combining dynamic content and the semantic layer creates opportunities for improved content operations processes through the content lifecycle. Examples of content that can be managed in this structured manner include rules and regulations, employee handbooks, technical documentation, sales enablement and product marketing copy, and learning content.

Content Operations and the Semantic Layer

Content integrated into a semantic layer supports the capabilities of content operations systems in several ways. The content can be better structured and organized in a content or digital asset repository for more efficient and relevant access. Recommendation engines can use the data to deliver content that is aligned with user interests and needs. The Entity recognition and topic modeling facilitates content analysis to inform strategy and decision-making. When fed into consuming systems, the combination of the content and the semantic layer facilitates effective data management, knowledge discovery, and decision-making processes within an organization.

When content is incorporated into a semantic framework, organizations have a powerful tool for unlocking the full potential of their content investment as it is integrated with the full range of organizational data and knowledge.

Use Cases for Content in the Semantic Layer

The need for actionable insights and efficient information retrieval can be supported by the integration of a content graph within a semantic layer, consolidating disparate content sources into a cohesive knowledge repository. This integration process incorporates standardized metadata and establishes mappings of the relationships and dependencies between different concepts and entities. It facilitates the automation of content generation processes, leveraging advanced algorithms and structured data to produce tailored content at scale.

Let’s look at how this capability can enable applications in knowledge discovery, automated content generation, and content recommendations.

Knowledge Discovery

Represented through ontologies, taxonomies, and knowledge graphs, the content within the semantic layer embodies organizational knowledge and domain expertise. This rich knowledge representation facilitates exploration and discovery, allowing users to uncover valuable insights and patterns hidden within interconnected content.

When that content is modeled and structured in the source systems, it can be enriched with metadata at the component level. The structure of content in a content repository plays a crucial role in facilitating data organization and abstraction within the semantic layer regardless of its original format or source. By incorporating the content metadata into a common semantic framework with other types of data, the semantic layer enables integration and interoperability between different data types, allowing users to access and leverage all relevant information from a single source. That source is then optimized for the kind of system that consumes it, whether a web content management system, a learning management system, a marketing campaign management system, or another use case.

In a recent project, EK worked with a federal space research institute to develop an enterprise knowledge graph to connect the dots between people, projects, engineering components, and engineering topics. Leveraging and enriching an existing taxonomy and ontology at the organization, EK automatically extracted key entities from a repository of unstructured documents, adding structured metadata to these text files. That knowledge graph was then incorporated into a semantic search platform to enable faceted search and navigation across individuals, projects, and unstructured text documents, significantly reducing time spent finding information.

Automated Content Generation

Managing content within a semantic layer can also facilitate content generation for highly structured content types such as those used in industries such as pharma (e.g., rules, regulations, definitions, reports). By organizing content elements and their relationships in a structured format, the semantic layer provides the foundation for understanding the context and meaning of data. This contextual understanding enables intelligent algorithms to analyze existing content, identify patterns, and generate new content autonomously.

Using Natural Language Processing (NLP) techniques, algorithms can extract insights from structured content and generate human-readable text based on predefined templates or rules, such as articles, reports, or summaries. By leveraging machine learning models trained on vast amounts of data, these algorithms can adapt and improve over time, producing increasingly accurate and relevant content.

Additionally, the semantic layer facilitates the reuse of existing content components in a structured content environment, such as paragraphs, sections, or entire documents, to create new content. Automated systems can dynamically assemble tailored content pieces by identifying and retrieving relevant content based on semantic relationships and metadata.

The combination of structured content management in a semantic layer and advanced algorithms for automated content generation opens up possibilities for streamlining content creation processes, reducing manual effort, and delivering personalized and timely content at scale.

EK recently worked with a pharmaceutical company to automate regulatory report generation. We created an ontological data model and knowledge graph to map experiment management and results data across the product development lifecycle. By extracting and connecting information in the semantic layer, analysts can quickly generate reports needed for regulatory filings and use templatized content models to embed those reports seamlessly.

Content Recommendations

The semantic layer enriches content with additional metadata and annotations to enhance its discoverability, relevance, and usability. By analyzing content for relevant keywords, topics, and sentiment, the semantic layer enables targeted content recommendations and tailored user experiences. Leveraging ontologies and knowledge graphs, content can be organized within the semantic layer, making it easier to surface relevant content to users based on their interests and browsing history. Through continuous analysis of user interactions and feedback, content recommenders can adapt and refine their recommendations in real time, ensuring relevance and timeliness.

With content integrated into the semantic layer, organizations can implement next-generation content recommenders, offering hyper-personalized experiences by leveraging enriched metadata and advanced algorithms to support targeted content delivery and recommend similar content. Personalized content recommendations increase user satisfaction and drive higher engagement metrics such as click-through rates, time spent on site, and conversion rates.

Semantic layers can identify related content items by analyzing the similarity between pieces of content. This similarity analysis enables the creation of features such as “You May Also Like” recommendations or relevant course materials based on user identity and behavior, enhancing engagement and content discovery.

EK recently worked with an organization that provides online healthcare compliance training solutions to build a cloud-based recommendation engine microservice supported by a semantic data layer that unifies, relates, and organizes the source data (course titles, descriptions, tags, and more). The engine generates recommendations for compliance terms for tagging content to appear in recommended courses for customers to ensure that they comply with regulations critical to their role, setting, and jurisdiction.

Summary

Structuring and organizing content within the semantic layer empowers organizations to extract valuable insights, standardize metadata, identify relationships, and enhance searchability. This facilitates the seamless integration of diverse content sources into applications such as recommendation engines, dashboards, and end-user-facing content. This integration empowers users to access and leverage all relevant information from a single source, driving better decision-making and knowledge discovery.

The post Adding Context to Content in the Semantic Layer appeared first on Enterprise Knowledge.

Enterprise Knowledge (EK) announced the creation of an External Advisory Board (EAB) of globally recognized leaders in the fields of Knowledge Management, Information Management, Content Management, and Data Management. The EAB will have multiple responsibilities with EK, including advising EK’s Leadership Team on the latest trends in the field, collaborating with EK subject matter experts to produce new thought leadership content, and teaming with EK consultants to advise and offer learning opportunities to EK’s clients.

The formal creation of the EAB has been several years in the making, with EK’s Leadership Team vetting the inaugural members to ensure the greatest caliber of expertise, innovation, and collaboration to mirror EK’s own standing and expectations within the industry. EAB membership will work on a rolling basis, allowing EK’s Leadership Team to onboard new members when they see fit, ensuring the company continues to capture the perspectives of the leading voices in the field.

“Having collaborated directly with each member of the EAB, I’m excited to formally welcome them to the Board. This is a group of some of the strongest and most innovative voices in the field, and I welcome their ideas in order to make EK more effective, and to better support our clients at every stage,” shares Zach Wahl, CEO of EK. 

The current members of the EAB are Malcolm Hawker, a former Chief Product Officer and Gartner analyst with over 25 years of experience across the fields of Data Strategy, Master Data Management (MDM), and Data Governance; Mohammed Aaser, Chief Data Officer (CDO) of Domo and former CDO of McKinsey and Company; and Polly Alexander, Director of Metadata and Taxonomy for WebMD Ignite, with expertise bridging the fields of Knowledge Management, AI, and Machine Learning.

EAB members are presently supporting EK’s Semantic Layer webinar series, and several members will be speakers at the Semantic Layer Day event planned for October in Munich, Germany.

To learn more about the EAB, visit EK’s Executive Advisory Board page.

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Division Director of Advanced Content Solutions, Rebecca Wyatt and Content Engineering Consultant, Emily Crockett were recently featured in the Spring 2024 edition of Training Industry Magazine. In their featured article, Improve L&D Efficiency with Reusable Learning Content, discuss the benefit of a Content Reuse Strategy for learning organizations and break down the steps including auditing your content, defining a reusable content model, building the reusable content, and designing the training and learning experiences. 

“Learning and performance improve when learning assets are presented in a meaningful context. A Content Reuse Strategy makes this contextualized presentation possible in a way that is sustainable – without creating duplicate content that is difficult to manage,” said Wyatt.

Training Industry Magazine serves as an expert resource for learning professionals seeking innovative approaches to developing effective training. Training Industry offers learning leader certifications based on their Training Manager Competency Model. Resources include their website, the magazine, webinars, events, and courses.

The post Rebecca Wyatt and Emily Crockett Featured in Training Industry Magazine appeared first on Enterprise Knowledge.

Webinar participants will learn the purpose of taxonomies in data management, the advantages of using Semantic Layers to leverage organizational taxonomies, the common approaches to creating a Semantic Layer, and how Progress Semaphore extends taxonomies into Semantic Layers. 

Register now to make sure you don’t miss this exciting presentation!

The post Heather Hedden to Present Webinar on the Role of Taxonomy and Ontology in Semantic Layers appeared first on Enterprise Knowledge.

Challenges

While LLMs are a powerful means of interfacing with an organization’s information, the effectiveness of LLMs is often hampered by the complexity and disorganization of the data they rely on. The challenge lies not only in processing vast amounts of information but also in ensuring that this information is accurate, relevant, and structured in a way that the models can effectively learn from. If LLMs are trained on information and data that lack those characteristics, they will produce low-quality results. Furthermore, without a model of how entities within a subject area — such as finance — relate to one another, the LLM may default to an inaccurate, generalist approach to responses based on the training data, causing it to miss relevant information and references. Finally, even in the case where these two problems are solved, there is the issue of hallucinations: the phenomena wherein an LLM will produce false or divergent answers that are unsupported by the underlying training data. Given the range of errors that can crop up when using an LLM, how can an organization prepare their LLM to be trustworthy enough for enterprise use?

Solution

This is where semantic layers come into play. A semantic layer is a standardized framework that organizes and abstracts organizational data. A semantic layer also solves the fundamental disconnect that businesses face between collecting data and turning that data into actionable information by providing standard models and a consumption architecture for handling and connecting structured and unstructured organizational data. In doing so, the specific domain knowledge and expertise of the enterprise is captured in a way that is both machine and human readable, enabling better decision-making and insight generation. This interoperability allows a semantic layer to act as the bridge between your raw data and the sophisticated analytical capabilities of LLMs by structuring the underlying data to improve the coherence and explainability of an LLM’s outputs.

Benefits of a Semantic Layer for LLMs

1: Data Quality and Accessibility

A semantic layer organizes and abstracts organizational data across formats, making it accessible for both humans and machines. Within a semantic layer, data and high quality models can be tagged for LLM training and consumption. This means training on data that is not only high-quality but also rich in contextual and conceptual relationships. This improved data accessibility accelerates the training process and enhances the model’s ability to understand and generate nuanced, informed text. 

For example, consider a healthcare LLM designed to provide diagnostic suggestions based on patient symptoms. With a semantic layer, patient data, medical histories, and research articles are organized and tagged with contextual relationships, such as symptoms associated with specific conditions. This way of organizing information allows the LLM to access a rich, interconnected dataset during training and operation, enabling it to recognize subtle nuances in patient symptoms and suggest diagnoses that reflect a deeper understanding of medical conditions and their manifestations. As a result, the LLM’s suggestions are not only relevant but also grounded in a comprehensive view of available medical knowledge, demonstrating the semantic layer’s role in enhancing the quality and reliability of its outputs.

By providing a standardized framework for data interpretation, semantic layers enable LLMs to access higher-quality data, leading to improved decision-making, enhanced customer experiences, and more accurate generated content. For businesses, this means being able to leverage data assets more effectively and reduce time spent looking for accurate information. This improved data discovery both accelerates the training process and enhances the model’s ability to understand and generate nuanced text.

2: Contextual Understanding

A semantic layer is not unique in its ability to organize and make available data across formats. A data catalog or a data fabric can be an effective means of delivering high quality data to consumers and machine learning models. However, semantic layers pull away from the competition in their ability to capture heterogeneous sources of data and enrich them with semantics and contextual information. The flexible data models, standardized vocabularies, quality metadata, and business context captured as a part of a semantic layer allows for LLMs and other computer applications to understand a business domain on a foundational level. 

For example, imagine a multinational corporation that utilizes an LLM to streamline its customer service. This corporation operates in various countries, each with its unique set of products, services, and customer interactions. A semantic layer can organize customer feedback, service tickets, and product descriptions, enriching this data with contextual information such as geographical location, cultural nuances, and language variations. By using this semantically rich dataset, the LLM can understand not just the explicit content of customer queries but also the implicit context, such as regional product preferences or local market trends. As a result, the LLM can provide more accurate, context-aware responses to customer inquiries, reflecting an understanding that goes beyond words to grasp the subtleties of global business operations.

When a semantic layer serves as a backbone for the LLM’s data consumption it ensures that training data is coming from trusted, high-quality sources that are enriched with domain context. This foundational context empowers LLMs to generate outputs based on a more comprehensive understanding of the subject matter. By capturing and connecting content based on business or domain meaning and value, LLMs can produce more accurate and relevant outputs, tailored to specific industry needs or knowledge domains.

3: Explainable Results

Even with high-quality data and business domain understanding, “hallucinations” are still a concern when trying to use an LLM as a trustworthy source of information. LLMs hallucinate due to many reasons, including a lack of sufficient context or specific tagging in their training data. When the data lacks robust contextual information and nuanced tagging, the LLM can have a limited understanding of the relationships between different data points. This limitation can lead to the generation of outputs that are not grounded in factual information or logical inference, as the model attempts to ‘fill in the gaps’ without a robust framework to guide its responses.

The incorporation of a semantic layer can help to cut down on the prevalence of hallucinations and improve output quality by enriching the LLM’s training environment with deeply contextualized and well-tagged data. As we have seen by now, semantic layers ensure that data is not only of high quality but also embedded with lots of contextual information and relationships between data that keep the model more grounded in reality. Furthermore, an LLM trained with the aid of a semantic layer can be prompted to include explanations of its outputs, detailing the data sources it pulled from when generating output and the contextual reasons behind the selection of these sources. This level of transparency allows users to evaluate the validity of the generated content, distinguishing between well-founded information and potential hallucinations. 

Hallucinations will always remain a potential issue with LLMs due to the nature of how they creatively generate output, but semantic layers offer a way to reduce the likelihood of hallucinations by providing better training data and enhancing the trustworthiness and reliability of LLM outputs through explainability.

Conclusion

In this article, we have touched on some of the potential pitfalls of using an LLM, as well as how a semantic layer can be used in concert with an LLM to mitigate those issues and improve the quality of their output. For issues of data quality, contextual business understanding, and explainability of results, semantic layers stand out as a comprehensive solution to the most pressing challenges of LLMs. Semantic layers empower LLMs to serve not just as text generators but as sophisticated tools for knowledge discovery, decision-making, and automated reasoning. Through their components including ontologies and knowledge graphs, semantic layers enrich LLMs with the ability to understand complex relationships and concepts, paving the way for advanced applications in areas such as legal analysis, medical research, and financial forecasting. In short, integrating semantic layers with LLMs presents a strategic advantage, allowing businesses to not only overcome the challenges of data complexity, but also to maximize the full potential of AI for competitive gain while minimizing risk. 

If you want to learn more about how your business can take the next step in building a semantic layer, leveraging LLMs, and developing enterprise AI, contact us to get started today!

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Join a collection of the world’s most prominent voices in data science, information management, and artificial intelligence to discuss the role of Semantic Layers, what they are, and what value they offer organizations in the quickly changing world being shaped by the AI Revolution. In this webinar, Enterprise Knowledge Vice President Lulit Tesfaye will facilitate a spirited conversation with Malcom Hawker, Polly Alexander, Mohammed Aaser, and Jeff Jonas. Over the course of the webinar, the expert panelists will share their views on the state of the industry and discuss real-world applications and implementations of a Semantic Layer.

Malcolm Hawker is a former Chief Product Officer and Gartner analyst with over 25 years of experience across the fields of Data Strategy, Master Data Management (MDM), and Data Governance. Polly Alexander is Director of Metadata and Taxonomy for WebMD Ignite, with expertise bridging the fields of Knowledge Management, AI, and Machine Learning. Mohammed Aaser is Chief Data Officer (CDO) of Domo and former CDO of McKinsey and Company. Jeff Jonas is Founder and CEO of Senzing and a former IBM Fellow with acclaimed expertise in data science with a specific focus on entity resolution.

This session will be held on Monday, April 22, from 3:00-4:00 pm EST. Register for the webinar at https://attendee.gotowebinar.com/register/3743095360256423520

The post Industry Panel: Different Applications of a Semantic Layer appeared first on Enterprise Knowledge.

Urmi and Fernando will present a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Attendees will gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within their organizations.

About Enterprise Data World Conference

Enterprise Data World is a five-day data management conference held in Orlando, FL that includes tutorials and several tracks of educational content driven by leading industry experts. Presenters will share their case studies, experiences, and knowledge by presenting on various topics applicable to data management, such as Data Strategy, Data Literacy, Data Architecture, and more.

The post EK’s Urmi Majumder and Fernando Aguilar Islas Speaking at Enterprise Data World 2024 appeared first on Enterprise Knowledge.