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Semantic Search Advisory and Implementation for an Online Healthcare Information Provider

EK Team — Tue, 22 Jul 2025 14:13:12 +0000

The Challenge

The medical field is an extremely complex space, with thousands of concepts that are referred to by vastly different terms. These terms can vary across regions, languages, areas of practice, and even from clinician to clinician. Additionally, patients often communicate with clinicians using language that reflects their more elementary understanding of health. This complicates the experience for patients when trying to find resources relevant to certain topics such as medical conditions or treatments, whether through search, chatbots, recommendations, or other discovery methods. This can lead to confusion during stressful situations, such as when trying to find a topical specialist or treat an uncommon condition.

A major online healthcare information provider engaged with EK to improve both their consumer-facing and clinician-facing natural language search and discovery platforms in order to deliver faster and more relevant results and recommendations. Their consumer-facing web pages aimed to connect consumers with healthcare providers when searching for a condition, with consumers often using terms or phrases that may not be an exact match with medical terms. In contrast, the clinicians who purchased licenses to the provider’s content required a fast and accurate method of searching for content regarding various conditions. They work in time-sensitive settings where rapid access to relevant content could save a patient’s life, and often use synonymous acronyms or domain-specific jargon that complicates the search process. The client desired a solution which could disambiguate between concepts and match certain concepts to a list of potential conditions. EK was tasked to refine these search processes to provide both sets of end users with accurate content recommendations.

The Solution

Leveraging both industry and organizational taxonomies for clinical topics and conditions, EK architected a search solution that could take both the technical terms preferred by clinicians and the more conversational language used by consumers and match them to conditions and relevant medical information.

To improve search while maintaining a user-friendly experience, EK worked to:

Enhance keyword search through metadata enrichment;
Enable natural language search using large language models (LLMs) and vector search techniques, and;
Introduce advanced search features post-initial search, allowing users to refine results with various facets.

The core components of EK’s semantic search advisory and implementation included:

Search Solution Vision: EK collaborated with client stakeholders to determine and implement business and technical requirements with associated search metrics. This would allow the client to effectively evaluate LLM-powered search performance and measure levels of improvement. This approach focused on making the experience faster for clinicians searching for information and for consumers seeking to connect with a doctor. This work supported the long-term goal of improving the overall experience for consumers using the search platform. The choice of LLM and associated embeddings played a key role: by selecting the right embeddings, EK could improve the association of search terms, enabling more accurate and efficient connections, which proved especially critical during crisis situations.
Future State Roadmap: As part of the strategy portion of this engagement, EK worked with the client to create a roadmap for deploying the knowledge panel to the consumer-facing website in production. This roadmap involved deploying and hosting the content recommender, further expanding the clinical taxonomy, adding additional filters to the knowledge panel (such as insurance networks and location data), and search features such as autocomplete and type-ahead search. Setting future goals after implementation, EK suggested the client use machine learning methods to classify consumer queries based on language and predict their intent, as well as establish a way to personalize the user experience based on collected behavioral data/characteristics.
Keyword and Natural Language Search Enhancement: EK developed a gold standard template for client experts in the medical domain to provide the ideal expected search results for particular clinician queries. This gold standard served as the foundation for validating the accuracy of the search solution in pointing clinicians to the right topics. Additionally, EK used semantic clustering and synonym analysis in order to identify further search terms to add as synonyms into the client’s enterprise taxonomy. Enriching the taxonomy with more clinician-specific language used when searching for concepts with natural language improved the retrieval of more relevant search results.
Semantic Search Architecture Design and LLM Integration: EK designed and implemented a semantic search architecture to support the solution’s search features, EK connecting the client’s existing taxonomy and ontology management system (TOMS), the client’s search engine, and a new LLM. Leveraging the taxonomy stored in the TOMS and using the LLM to match search terms and taxonomy concepts based on similarity enriched the accuracy and contextualization of search results. EK also wrote custom scripts to evaluate the LLM’s understanding of medical terminology and generate evaluation metrics, allowing for performance monitoring and continuous improvement to keep the client’s search solution at the forefront of LLM technology. Finally, EK created a bespoke, reusable benchmark for LLM scores, evaluating how well a certain model matched natural language queries to clinical search terms and allowing the client to select the highest-performing model for consumer use.
Semantic Knowledge Panel: To demonstrate the value this technology would bring to consumers, EK developed a clickable, action-oriented knowledge panel that showcased the envisioned future-state experience. Designed to support consumer health journeys, the knowledge panel guides users through a seamless journey – from conversational search (e.g. “I think I broke my ankle”), to surfacing relevant contextual information (such as web content related to terms and definitions drawn from the taxonomy), to connecting users to recommended clinicians and their scheduling pages based on their ability to treat the condition being searched (e.g. An orthopedist for a broken ankle). EK’s prototype leveraged a taxonomy of tagged keywords and provider expertise, with a scoring algorithm that assessed how many, and how well, those tags matched the user’s query. This scoring informed a sorted display of provider results, enabling users to take direct action (e.g. scheduling an appointment with an orthopedist) without leaving the search experience.

The EK Difference

EK’s expertise in semantic layer, solution architecture, artificial intelligence, and enterprise search came together to deliver a bespoke and unified solution that returned more accurate, context-aware information for clinicians and consumers. By collaborating with key medical experts to enrich the client’s enterprise taxonomy, EK’s semantic experts were able to share unique insights and knowledge on LLMs, combined with their experience with applying taxonomy and semantic similarity in natural language search use cases, to place the client in the best position to enable accurate search. EK also was able to upskill the client’s technical team on semantic capabilities and the architecture of the knowledge panel through knowledge transfers and paired programming, so that they could continue to maintain and enhance the solution in the future.

Additionally, EK’s solution architects, possessing deep knowledge of enterprise search and artificial intelligence technologies, were uniquely positioned to provide recommendations on the most advantageous method to seamlessly integrate the client’s TOMS and existing search engine with an LLM specifically developed for information retrieval. While a standard-purpose LLM could perform these tasks to some extent, EK helped design a purpose-built semantic search solution leveraging a specialized LLM that better identified and disambiguated user terms and phrases.

Finally, EK’s search experts were able to define and monitor key search metrics with the client’s team, enabling them to closely monitor improvement over time, identifying trends and suggesting improvements to match. These search improvements resulted in a solution the client could be confident in and trust to be accurate.

The Results

The delivery of a semantic search prototype with a clear path to a production, web-based solution resulted in the opportunity for greatly augmented search capabilities across the organization’s products. Overall, this solution allowed both healthcare patients and clinicians to find exactly what they are looking for using a wide variety of terms.

As a result of EK’s semantic search advisory and implementation efforts, the client was able to:

Empower potential patients to use web-based semantic search platform to search for specialists who can treat their conditions quickly and easily find care;
Streamline the content delivery process in critical, time-sensitive situations such as emergency rooms by providing rapid and accurate content that highlights and elaborates on potential diagnoses and treatments to healthcare professionals; and
Identify potential data and metadata gaps in the healthcare information database that the client relies on to populate its website and recommend content to users.

Looking to improve your organization’s search capabilities? Want to see how LLMs can power your semantic ecosystem? Learn more from our experience or contact us today.

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Integrating Search and Knowledge Graphs Series Part 1: Displaying Relationships

Chris Marino — Mon, 19 Oct 2020 14:21:09 +0000

I’ve spent many years helping clients implement enterprise search solutions and am constantly looking for new ways to improve a user’s search experience so that they can find relevant content as well as discover new content they may not have previously known existed. Recently, EK has spent time designing and building knowledge graphs which has given me ample opportunity to ponder some practical ways to integrate these two powerful technologies.

In this three-part series, I’ll lead you through a phased approach for integrating knowledge graphs with your current search solution. The series will begin with simple, practical steps to help you get started quickly and conclude with an exploration of more advanced functionalities that truly harnesses the semantic capabilities provided by a knowledge graph:

Part 1: Showing Relationships – Expose the relationships in your content via search results.
Part 2: Enhance Results – Enhance search results by presenting facts, not just returning documents.
Part 3: Provide Context – Leverage the power of the knowledge graph to provide context to your search and improve relevancy.

What is a Knowledge Graph?

A knowledge graph consists of nodes (entities) and edges (relationships), and provides a means to model your domain and expose the rich relationships found within your content. Imagine a tool that lets you show the people, places, and things that make up your domain and the myriad ways each piece of content is connected to another.

Showing Relationships to Enhance the Search Experience

One of the simplest ways to begin integrating your knowledge graph with search is exposing the relationships between content when displaying search results. This increases the discoverability elements of your search results page by not only displaying the results that matched your search, but also valuable, connected content. To accomplish this, find the existing relationships stored in your Knowledge Graph and add them as fields to your search records.

A real world example helps illustrate the necessary steps. Take, for instance, the case of a legal publisher whose content consists of a connected web of statutes, regulations, cases, legal guidance, commentary articles, and a variety of other knowledge assets. The publisher’s knowledge graph contains a node for each piece of content with applicable properties like Title, Published Date, Author, and Code section. Further, a series of edges show the varied relationships between the nodes such as:

– Regulation A derives its authority from Statue B

– Court Case C involves interpretation of Regulation A

– Article E contains references to Court Case C

– Legal Guidance G was mentioned in Article E

Prioritizing Which Relationships Enhance Search

The first step in the integration process is to design the look of the individual search results – which relationships do you want to show for each result. While it’s desirable to show more detail than just the document title as a link, there is still limited screen space available – don’t show all possible relationships. Instead, focus on those that convey additional context or meaning to the user. What are the items that would lead them to follow these links to discover new content.

At EK, we are big proponents of “action-oriented” search which emphasizes understanding what action a user wants to take with the search results. In an Action Oriented Search Workshop, we design how the result should appear on the page.

A common scenario would be that a user wants to see related documents to the result they’re currently viewing. Referring to the example from the previous section, a user viewing Regulation A as a result may very well want to see related statutes, or court cases which reference “Regulation A.” Instead of presenting a static list of links, the search results can easily be enhanced to display this related content, providing the user with the ability to discover and investigate information they may not have known existed.

Integrating Relationships Into the Search Engine

Now that we know which relationships we want to display, the next step involves integrating the relationships from our knowledge graph into our search engine. Since we’re only using the relationships for display purposes at this point, all we need to do is store these as additional fields in our search record.

At this point, there are two approaches to integrating the content in your knowledge graph into your search application. One method is to add logic to the search application code which queries the knowledge graph for each search result to obtain the required information. However, this requires a call to a second system (the knowledge graph) which is not as performant as a highly-tuned search engine. The resulting call can have a negative impact on your search performance which can easily lead to user frustration as they wait for the results to render.

The preferred approach is to integrate content from the knowledge graph during the indexing process. Indexing a record involves accessing a piece of content from a source system, processing it into a format that the search engine understands, and then pushing it to the search index for querying. The component that handles this processing is called a “connector.” Normally, we develop our own connectors using custom code to ensure they meet the exact needs of our clients.

In this approach, the connector code would not only query the source system for content, but also query the knowledge graph to obtain the relevant relationships for that piece of content. These multiple sets of information would be merged together in order to add these relationships as fields to the current record. Further, the indexing process is “off-line” so it’s acceptable if it takes a little longer going into the search engine. Once indexed, the search engine will return results at blazing speed.

For example, let’s say the current record was a specific regulation – “Section 23.2(c)(ii).” By querying the knowledge graph for this particular section, you discover the following relationships exist:

“Regulation 23.2(c)(ii)” derives from “Statute 123.456.”
The article “Common Issues related to New Requirements” mentions “Regulation 23.2(c)(ii).”
The court case “Wyatt v. Hilger, March 7, 2020” interprets “Regulation 23.2(c)(ii).”

Storing these relationships in the search index allows us to render a much richer result type to the user.

Summary

Leveraging the stored relationships for display purposes with your search results is a simple, effective first step to integrating your knowledge graph with your search application. The relationships from your knowledge graph are captured as additional fields in your search records to render intuitive, action-oriented results.

In summary, follow these steps:

Determine which relationships add the most value to search;
Design the search UX to display those relationships; and
Integrate the relationships into the search engine.

In the next part of the series, we’ll investigate how you can enhance your search results page even further by combining facts stored in the Knowledge Graph with the documents stored in your search engine.

In the meantime, contact us for any assistance with your search and knowledge graph needs.

The post Integrating Search and Knowledge Graphs Series Part 1: Displaying Relationships appeared first on Enterprise Knowledge.

EK Listed on KMWorld’s AI 50 Leading Companies

EK Team — Tue, 07 Jul 2020 15:54:34 +0000

Enterprise Knowledge (EK) has been listed on KMWorld’s inaugural list of leaders in Artificial Intelligence, the AI 50: The Companies Empowering Intelligent Knowledge Management. KMWorld developed the list to help shine a light on innovative knowledge management vendors that are incorporating AI and cognitive computing technologies into their offerings.

As a services provider and thought leader in Enterprise AI, Knowledge Management, and Semantic Search, EK is one of the few dedicated services organizations included on the list. EK was uniquely recognized for our leadership in this area, including our AI Readiness Benchmark and range of functional demos that harness knowledge graphs, natural language processing, ontologies, and machine learning tools.

“As the drive for digital transformation becomes an imperative for companies seeking to compete and succeed in all industry sectors, intelligent tools and services are being leveraged to enable speed, insight, and accuracy,” said Tom Hogan, Group Publisher at KMWorld. “To showcase organizations that are incorporating AI and an assortment of related technologies—including natural language processing, machine learning, and computer vision—into their offerings, KMWorld created the “AI 50: The Companies Empowering Intelligent Knowledge Management.”

Lulit Tesfaye, EK’s Practice Leader for Data and Information Management stated, “We are thrilled for this recognition and extremely proud of the cutting edge solutions we’re able to deliver for organizations looking to optimize their data and Knowledge AI initiatives. This recognition demonstrates EK’s ability to leverage our real-world experience and define the enterprise success factors for maturity and readiness for AI, bringing the focus back to business values, and the tangible applications of AI for the enterprise. Allowing organizations to go past the common AI limitations is what helps us show where we are leading.”

EK CEO Zach Wahl added, “Thanks to KMWorld for this recognition and congratulations to my amazing colleagues for their thought leadership. Alongside our recognition as one of the top 100 Companies That Matter in Knowledge Management for the sixth year in a row, this demonstrates EK’s leadership position at the nexus of KM and AI.”

About Enterprise Knowledge

Enterprise Knowledge (EK) is a services firm that integrates Knowledge Management, Information Management, Information Technology, and Agile Approaches to deliver comprehensive solutions. Our mission is to form true partnerships with our clients, listening and collaborating to create tailored, practical, and results-oriented solutions that enable them to thrive and adapt to changing needs.

About KMWorld

KMWorld is the leading information provider serving the Knowledge Management systems market and covers the latest in Content, Document and Knowledge Management, informing more than 21,000 subscribers about the components and processes – and subsequent success stories – that together offer solutions for improving business performance.

KMWorld is a publishing unit of Information Today, Inc.

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Project and Expert Finder

EK Team — Wed, 06 May 2020 17:03:01 +0000

The Challenge

A federally funded engineering research center has an extensive “project library” where technical documents, certifications, and reports related to various engineering projects are stored. Many of these documents are often scanned versions of handwritten notes or files and, consequently, have little metadata attributed to them and are very difficult to surface in search. Additionally, when employees start working on new projects, it’s difficult to ascertain what was done on previous projects, who did the work, and when that work occurred.

Similarly, project managers struggled to discern which employees possessed specific skill sets or experience when they were staffing new projects. The specificity of these needs was not easily captured in structured formats and often covered one-off, rather than repetitive, use cases (e.g., “who worked on this project and what role did they play?”). The best option for locating someone with a specific ability or experience was to rely on personal networks or institutional memory, making it difficult to leverage institutional knowledge or onboard new employees effectively.

The Solution

To help connect the dots between people, projects, engineering components, and engineering topics, Enterprise Knowledge (EK) developed a proof of concept enterprise knowledge graph. Leveraging and enriching an existing taxonomy and ontology at the organization, EK was able to automatically extract key entities from a repository of unstructured documents, using the documents as the “glue” to form connections between the aforementioned entities.

The benefits of semantic technology allowed EK to expand the knowledge graph by incorporating existing structured information about individuals at the organization, creating a more holistic understanding of who these people are, what they’ve worked on, and how to contact them.

Finally, EK incorporated the knowledge graph into a semantic search platform to enable faceted search and navigation across individuals, projects, and the unstructured text documents (e.g., the scanned, handwritten notes and files). This search platform allows users to drill down to a small group of individuals that worked on a specific project and/or have experience with a particular engineering topic or component, reducing time in finding a qualified individual from weeks to a few minutes.

The EK Difference

To achieve the above solution, it was necessary to develop a plethora of integrations between graph databases, relational databases, content management systems, taxonomy management tools, and data pipeline (ETL) tools. Through our expertise in designing and implementing the top semantic tools in the industry, EK was able to develop a scalable solutions architecture while leveraging web standards and providing guidance on integration best practices to deliver timely, effective, and scalable integration solutions.

Additionally, EK leveraged advanced capabilities to drive enhanced and automated tagging and classification of content, making the process more efficient, while mitigating the possibility of human error. We further partnered with the organization’s key subject matter experts and leveraged an Agile methodology that allowed us to quickly show results and provide incremental value to the organization.

The Results

The foundational knowledge graph and semantic search interface allow users to browse documents by person, project, and topic, providing supporting links to source data so that they can analyze the relationships between people and projects directly. End users can now easily browse and discover relationships that were previously uncaptured or buried in disparate data repositories, reaching the ‘goal’ information in a fraction of the time. Further, the flexibility of the knowledge graph solution allows for greater agility to modify and improve data flows without making extensive changes to architectures or schemas, resulting in quick turn-around times for updates as project staff change and requirements evolve.

Expert-Finder-Case-Study Download

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What is the Roadmap to Enterprise AI?

Lulit Tesfaye — Wed, 18 Dec 2019 14:00:57 +0000

Artificial Intelligence technologies allow organizations to streamline processes, optimize logistics, drive engagement, and enhance predictability as the organizations themselves become more agile, experimental, and adaptable. To demystify the process of incorporating AI capabilities into your own enterprise, we broke it down into five key steps in the infographic below.

If you are exploring ways your own enterprise can benefit from implementing AI capabilities, we can help! EK has deep experience in designing and implementing solutions that optimizes the way you use your knowledge, data, and information, and can produce actionable and personalized recommendations for you. Please feel free to contact us for more information.

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The 5 Key Components of a Semantic Search Experience

EK Team — Wed, 06 Nov 2019 19:16:46 +0000

Semantic Search extends meaning and context to your otherwise run-of-the-mill search results. This future-ready phase of search seeks to apply machine-driven understanding of user intent, query context, and the relationships between words. We broke down the primary elements that make search ‘semantic’ in the following infographic to shed some light on the varying concepts and principles in play.

Applying any of the principles identified in the above infographic can upgrade your search strategy to a future-ready, semantic experience. Whether you think your search needs a simple update or is ready for a serious upgrade, we can help. EK offers a range of search-specific services that will produce actionable recommendations. Please feel free to contact us for more information.

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