The semantic layer framework acts as a critical bridge between raw, unstructured data and modern knowledge platforms, enabling seamless integration, organization, and retrieval of information. By leveraging structured metadata, AI-supplemented auto-classification, and knowledge graphs, this framework enhances the discoverability and usability of dispersed content while enabling inference-based relationships to be surfaced. Beyond its technical role, it also provides a strategic foundation for knowledge management by guiding the implementation of process and governance models that ensure consistency, accessibility, and long-term sustainability. In the following section, we will explore two real-world cases that illustrate how this approach has been successfully implemented to address the business challenges outlined above.

Case 1

Challenge: Identifying experts in specific research areas with experience on past projects is complex and time-consuming, requiring extensive institutional knowledge, and relying on informal networks and word of mouth.

Solution: Project and Expert Finder

A large, federally-funded engineering research and development center relied heavily on personal networks and institutional memory to find individuals with specific skills and experience. They managed extensive data sources and content repositories, but it was still difficult to leverage organizational knowledge and onboard new employees efficiently. They were not linking critical entities within both structured and unstructured data and content–such as people, projects, roles, and materials. This gap hindered effective project staffing, planning, and research. The issue was further exacerbated by a retiring workforce, leading to a loss of valuable tacit knowledge.

EK implemented a scalable, adaptable semantic layer framework to develop a knowledge graph that connects people, projects, engineering components, and technical topics. This institutional knowledge graph aggregates data across 40 applications, eliminating the need for discrete data connectors, reducing costs, and serving as a centralized resource. Integrating with the enterprise search system, it enhances browsability and discoverability, providing a comprehensive view of relationships within the organization. Beyond improving access to information, the unified data within the knowledge graph can also act as a powerful input to artificial intelligence algorithms, enabling predictions and discovery of previously unknown relationships. This enhanced intelligence not only supports decision-making but also drastically reduces the time required to locate critical information–from three weeks to just five minutes–accelerating research, publication processes, and internal collaboration.

Case 2

Challenge: Disorganized and siloed content prevents seamless searchability across repositories, hinders the ability to trace the digital thread responsibly, and complicates long-term information preservation. As a result, answering critical questions about the impact of past and current work on project decisions and deliverables becomes nearly impossible.

Solution: Internal Research Platform

A federally funded energy research and development center relies on past project outcomes and experimental data to advance scientific innovation. However, researchers struggled to find relevant reports due to unstructured metadata and ineffective search capabilities, with manual metadata entry being time-consuming, error-prone, and inconsistent across systems. Analog content, inconsistent metadata standards, and fragmented information systems have created significant barriers to knowledge discovery, records management, and long-term information preservation. This exacerbates information silos, leading to knowledge loss, inefficiencies, and decision-making risks from limited access to reliable research data.

To address these challenges, EK supported the development of 5 metadata and taxonomy models tailored to tag 80,000+ documents with high precision. This was achieved through a custom auto-classification pipeline, which integrates multiple gold data sources with a TOMS (Taxonomy and Ontology Management System). By enriching both analog and native digital documents with semantic metadata, the solution facilitates enhanced content and research material discovery through a knowledge graph underlying a front-end search interface. Standardizing metadata and taxonomy models not only automates classification but also digitizes analog content and integrates several systems, significantly improving searchability and accessibility across the organization. To ensure organization-wide scalability, adoption, and sustainability, governance models were developed with strategies spanning tactical, strategic, operational, and technical domains. These models address key facets necessary for a successful long-term implementation, ensuring the framework remains adaptable and effective over time.

Conclusion

In the research and development space, ensuring that information such as expertise, project history, and past research is easily findable is critical for driving innovation, reducing inefficiencies, and managing knowledge transfer amidst retirement in a highly specialized workforce. Research organizations also benefit from using semantic layers to manage research-focused data products more consistently, leveraging graph data to find patterns and insights. Additionally, AI-driven knowledge discovery, automated metadata tagging, and interoperability across systems further enhance research efficiency and collaboration.

At EK, we specialize in delivering tailored solutions to help your organization overcome its unique data and knowledge management challenges–from strategy to implementation. Explore our Knowledge Base to learn more about our expertise and semantic layer solutions, and contact us to discuss how we can support your specific needs.

The post How a Semantic Layer Transforms Engineering Research Industry Challenges appeared first on Enterprise Knowledge.

In this blog, two of our technical consultants, Jade McDaniels and James Midkiff, answer common questions about Expert Finders, focusing on how to build and leverage them in an organization.

What defines an expert at my organization, and how do I generate that information?

Jade McDaniels

Experts are typically recognized as individuals who possess knowledge, skills, and experience regarding a subject area beyond that of a knowledgeable audience. Experts demonstrate a deep understanding of a specific field of study and are capable of wielding that knowledge to educate others, solve problems, and discover new opportunities for skill application. Within an organization, a few general markers to identify an Expert are:

• project participation records,
• contribution to content in their subject field knowledge base,
• certifications; and,
• the types of institutional groups they engage.

An important point is that these markers, which can be leveraged to identify an Expert, are not all indicators of what people have learned (i.e. certifications). Many of the indicators of expertise are performance-based (i.e. participation in projects that align with the topic).

Most, if not all, of the information that describes expertise and that will fuel the Expert Finder already exists within your organization and does not need to be created. For example, enterprise HR Systems hold person-specific details on employees, including their name, role, and records for past project participation and the capacity in which they served on the project. You can leverage wikis and document management systems like Confluence, Sharepoint, Smartsheet, etc., to aggregate metadata supporting expert-specific criteria. Expert Finders exist as the integration point between HR systems and enterprise knowledge repositories and demonstrate strong capabilities when aligning an expert with their sphere of influence and work produced – saving the cost of manual effort when building that data from scratch.

James Midkiff

For most organizations, the biggest hurdle to Expert Finders is identifying who is an expert in a given subject matter. Experts are rarely self-proclaimed and often need to be identified by their experiences. Someone may be considered an expert if they have;

• Demonstrated related learning such as:
  • received a degree in a given topic,
  • taken or led training courses, or
  • earned certificates related to the subject matter.
• Demonstrated related performance such as:
  • presented or authored a blog, book, or other publication on a subject,
  • contributed to the organization’s knowledge base of communities of practice on the subject, or
  • work or have worked on a relevant project at the organization.

There are many use cases when determining expertise, and we need to consider all of them when generating a list of experts. Initially, we can aggregate information by pulling metadata from the project or client management software, querying relevant data points from the enterprise data warehouse, or performing a project document analysis. Then, we leverage the aggregated information to generate confidence scores between individuals and subjects to measure how likely an individual is an expert in a subject. This approach may involve a custom data model (ontology) to enable our understanding of how an individual relates to topics and gives us precise methods to identify relationships. Confidence scores are how we can fine-tune our calculation of expertise and Expert Finder results.

Why do we need an Expert Finder?

Jade McDaniels

Expert Finders are essential to a unified view of an organization’s knowledge assets. The information existing in professionals’ heads, also known as tacit knowledge, is valuable and should be captured in a way that is easy to find and connect with for peers and leaders. Experts represent pillars of knowledge and confirm the strong points that exist within business units. The ability to recognize knowledge authorities and their areas of expertise optimizes opportunities for collaboration, creating innovative solutions, and knowledge retention.

Experts also maximize learning and performance outcomes in communities of practice, mentoring, and other peer-to-peer learning frameworks. While anyone at any level can contribute to a knowledge space, experts are the leaders as they’ve procured knowledge and experiences by applying knowledge. This voice of experience bolsters performance outcomes.

James Midkiff

Expert Finders are critical to helping people grow. Expertise is a source of knowledge, and connecting people over a shared or new area of interest enables individuals to learn from their peers in a natural way. It is even more important to intentionally support Social Learning with the exponential increase of hybrid work environments, enabling virtual colleagues, potentially new to the organization, to meet and socialize with peers with shared interests.

For team management, Expert Finders enable project managers to build teams with the right knowledge, skills, and abilities for success. Experts are easily located and given opportunities to share their wealth of knowledge and experience with their team members. Team members can identify peer mentors to help in their own individual growth.

Expert Finders are a natural extension to existing search tools that let individuals search for expertise, whether they’re looking for information or individuals. Depending on the system, we recommend integrating an Expert Finder with pre-existing search portals to help raise awareness of expertise at an organization.

Do I have to buy an expert finder software/system? What tools are available for Expert Finders?

Jade McDaniels

In the technology marketplace, there are a few existing out-of-the-box (OOTB) tools to leverage when searching for people within an organization. However, it’s important to note that the features advertised by available systems may only address some of the use cases and business needs of your institution. If you decide on a ready-made solution, you should align your decision around which system most meets your user requirements and any business goals that you wish to achieve.

Do you have to buy an Expert Finder system? The short answer is no. Off-the-shelf software offers many advantages, including rapid implementation and no/low maintenance support costs. Nonetheless, ready-made software is often limited in scalability and customization opportunities. Additionally, these systems are not guaranteed to integrate seamlessly with standing technological infrastructure. Instead, you can build your own Expert Finder to meet your business’s unique needs in terms of scalability, user requirement satisfaction, and integration with existing systems.

James Midkiff

Investing in an Expert Finder is important, whether OOTB or through custom development. There are a handful of available tools that provide the ability to search for people, including Glean and IBM Watson, or human resource platforms like Sift, Workday, and WorkForce Now. It is valuable to look into these options to see if any tools can provide all the features necessary to fit your use case. However, building your own Expert Finder will enable the most flexibility in how you model and curate expertise.

Designing an ontology will give you the best understanding of how to generate individual expertise. Once you develop the model, I recommend pulling in a sample of the data to instantiate the ontology and leverage a search engine to quickly retrieve experts. The data pipelines generating metadata about an individual and the relevance query used in the search engine are completely open for you to tune to fit your expertise needs. Free Lucene-based search engines (like Elasticsearch or Solr) are easy to get up and running. If you’re feeling adventurous, you can experiment with a vector search engine (like Milvus, Qdrant, or Weaviate) since they enable more semantic search capabilities and the ability to understand more about the input search query.

How do I get started building an Expert Finder?

Jade McDaniels

The first step in building an Expert Finder is determining whether or not your organization would benefit from implementing one. Before expending resources on a ready-made platform or investing in a custom code solution, confirm that an Expert Finder aligns with the problems you want to alleviate, behaviors you want to enable, and business values you wish to promote.

From there, approach building your Expert Finder from 4 different perspectives.

• Source System(s): Consider if and where the data that determines expertise lives within your organization. Recognizing source data repositories will aid in the process of determining the technology you need to integrate with said systems.
• Data Processing: This point focuses on how the system will process information to infer the relationship between individuals and an area of expertise and determine who is an expert. Establishing these relationships requires the implementation of data modeling and, thus, a tool that can facilitate those connections and processes.
• Search: The purpose of an Expert Finder is to support finding information and individuals. It’s essential to understand how a user will search for said information and individuals and confirm that the utilized search application complements their search patterns.
• Design: Effectively connecting searchers to information or people requires the thoughtful delivery of those details. Consider how knowledge should be shaped and delivered as a search result for optimal engagement and action by end users.

James Midkiff

I tend to like lists, so here are my 5 steps to build an Expert Finder.

1. Brainstorm Expert Finder Use Cases: Ideate who could benefit from expert information and how they might interact with an Expert Finder. What business value does an Expert Finder have for your use case?
2. Identify Expertise Data: Determine the data (generated or made available for processing) to leverage for determining expertise in a subject area.
3. Design the Interactions: Once you have brainstormed the use cases and identified supporting information, try designing what that use case could look like if it were implemented.
4. Architect the Integrations: Now that we know the goal, take a step back and determine the location of supporting information and how to pull that data into one location for the Expert Finder.
5. Implement the Use Case: After documenting the use cases (and business values), the designs are in place, and the architecture is ready to go, start putting the pieces together.

Make sure to start small, prioritizing architecture implementation to enhance search relevance. Iteratively update the data pipelines and search queries to ensure expertise searches match expectations.

Conclusion

Expert Finders are search interfaces that leverage enterprise learning and performance data to make expertise findable across your organization. Expert Finders enable individuals to find mentors, communities of practice to find SME leads, and business leaders to identify emerging organizational leaders. The decision to build an Expert Finder from scratch or utilize an OOTB solution is largely based on your business’s needs. If you are looking for help determining which solution is best for your organization, please contact us.

The post Expert Analysis: How Do I Build an Expert Finder? appeared first on Enterprise Knowledge.

• Returning contextual relationships with search results;
• Displaying relevant topics in a knowledge panel; or
• Powering an expert finder.

These advanced use cases enable an organization to provide more domain context and organizational information to users, reducing user time spent searching and improving a user’s ability to discover new content through recommendations. The five steps that EK recommends to implement search with a knowledge graph are as follows.

1. Analyze the Search Content
2. Develop an Ontology for the Knowledge Graph
3. Design the User Search Experience
4. Ingest the Data
5. Implement and Iterate

Depending on your workflow, these steps may not occur in a waterfall order, so keep in mind that, for example, step 3 could be started while step 2 is still in progress. Also, these steps are analogous to the steps necessary to implement a semantic architecture.

Step One: Analyze the Search Content

The first step to a successful knowledge graph search implementation is to analyze the information available for users. If you are just starting a search effort, start small and analyze a handful of data sources that contain key information that end-users always need. This step often involves interviews with business and technical data source owners as well as users to answer the following questions. At the end of this step, you will have a collection of information about data source content with answers to what, where, and how the information can be leveraged.

What information is available?

We want to identify each type of information available from a data source. If we are analyzing a content management system, it may contain deliverables and reports. However, do not stop there. Continue asking questions to dive deeper into what is available.

• What metadata fields exist on a report?
• Can we segment the deliverables at all? i.e. Can we retrieve or link to the pages separately?
• What users worked on this document?

As you dive deep into the content, you will surface key pieces of information that can be put together to solve user needs.

Where is the information and how do we get it?

These two questions inform the development process later on and ensure that information is actually available for use. We find it key to meet with data source technical owners as they will be able to figure out where information lives within a system, how it is generated, and, most importantly, how we can extract the information for use in the knowledge graph. It is best to start this conversation early as often there may be security concerns or development steps that need to be taken in order to build out an integration point.

How is the information related to other information?

Once you know what information is available, facilitate a conversation with the business owners to determine where the information originates and how the information relates to other data sources. With this question, we are hoping to surface concepts like

• Content lifecycle processes that could be tracked to add more context to search;
• Opportunities to combine information from multiple data sources together; or
• New data sources that we should analyze in the future.

Knowledge graphs are great at representing and querying interconnected data as well as providing means to infer additional relationships. We want to take advantage of this feature as much as possible since it helps drive the search user interface design (that we will talk about later).

By collecting the answers to these questions, you are making it easier to take the next steps in implementing search. If step one is still unclear, think of it like designing a content type and consider that our main goal is to create custom search results that utilizes all information at your organization. Understanding not only where information is but where it comes from and how it will change over time is crucial to the next step of modeling the information.

Step Two: Develop an Ontology for the Knowledge Graph

At the end of the first step, we have a large amount of data describing the information contained in all of our data sources and how they relate to each other. The next step is to figure out how we can leverage the information to answer user questions and build a model to support them. This model, academically referred to as an ontology, is the data model of the knowledge graph that we will be piecing together in step four.

Define the User Questions

We strongly believe that the best way to ensure any solution’s success is to gather requirements from the users. EK usually runs a search workshop to facilitate a session with end-users and business stakeholders to elicit feature requirements and determine what information users find helpful. In step one, you collected a lot of data describing the types of information available. Use this data to ask pointed questions, gauging user interest in the data you uncovered. Work with the group to determine how they would like to see information displayed and what questions they would ask of the data. This is an opportunity for users and stakeholders to think outside the box and come up with their ideal solution, no matter how out of scope it may seem at the time. Every idea may be used later while iterating on the solution or to influence the creation of similar features.

Determine the Classes, Attributes, and Relationships

Almost all information can be represented using classes, their attributes, and the relationships between them. Once you know the questions that users want to ask and the requirements for the solution, you can begin to break down the data from step one into classes. For this process, you can follow the following questions.

• What types of information does search need to display?
  (e.g. employees, deliverables)
• For each type, what properties are necessary to display the information in an intuitive way for users?
  (e.g. do end-users need to see the employee’s email?)
• For each type, what relationships exist to other types of information?
  (e.g. are employees related to deliverables at all?)

A majority of these questions will leverage the data collected in step one, but the data is now tuned to match the needs of the users and stakeholders. Use ontology design best practices to validate the reusability and scalability of the data model. The selected classes (types), properties (attributes), and relationships form the initial ontology.

Map the Data Sources to the Ontology

It is critical to keep a mapping of the data source information to the ontology so that you can maintain and upgrade the ontology in future iterations. Keep track of where each type of information originates, how attributes are calculated, and what steps are taken to extrapolate relationships within the information. While developing the mapping, pull a sample set of information from the data sources and mock up some data. Use this mocked data to validate the data types that should be used for each attribute with a technical member of the team. This ensures that the mapping has realistic inputs and outputs that can be leveraged when creating the data pipelines in step four.

Use the knowledge you already have to create complete views of your organization’s information, including people and clients.

Step Three: Design the User Search Experience

In steps one and two, we put our full attention on the data sources, interpreting the available information into a data model that will enable us to populate a knowledge graph. In this step, we want to shift our focus to the end-users and make sure we build a search solution that will solve user needs through an intuitive interface, leveraging the full capabilities of a knowledge graph.

Define the Search User Stories

Work with the application stakeholders and users to define user stories that will help guide the user interface design. Here’s a blog we have written about three key benefits of user stories.

Perspective		Define the search and interface requirements (not features!) from the view of a user. What does a user need from the search solution?
Purpose		Determine why requirements are needed and the benefits they bring. This enables the team to brainstorm and build the best feature to meet the requirements.
Priority		Work with users and stakeholders to order the requirements. A prioritized backlog of requirements ensures the team delivers high interest items first.

When defining the user stories, keep an eye out for use cases that could be solved through an action-oriented search result. We want to note what data points are important to users so that we can best leverage them in the design process to enable users to take immediate action.

Design using Search Best Practices

Start simple and include your basic search features, the search bar, results, and facets. These basic features ensure that anyone, regardless of their background, can find and discover information within search. Facilitate design workshop sessions with users and stakeholders to design search results for types of information and include search best practices.

Use a consistent view when displaying the same content on multiple pages.

Determine which attributes and relationships in the data need to be highlighted in search results versus those that should be only displayed in spots requiring an additional click, like an accordion dropdown or an entirely new page. When designing the interface, standardize how users will interact with the interface and different content types. The consistent interactions build trust with users and ensures that interaction with search is intuitive.

Innovate with Knowledge Graph Search Features

Up until now, step three has been all about designing the search solution using search design best practices. Now that we have that baseline, we want to include knowledge graph specific use cases like the below.

Identify the Search Subject

Use named-entity recognition (NER) or a knowledge graph entity lookup to identify what a user is looking for and present the user with all relevant compiled information about that entity. For example, imagine the search information includes people, documents, and projects. If a user searches for the id of a project, design a project page that the user is redirected to that includes all of the project metadata, links to the documents associated with the project, and all team members that worked on the project. Creating these encyclopedic-like pages for an organization’s content can greatly improve the user’s ability to find the information they are looking for.

Extend the Search Results

Along the same line as the above, surface additional information, properties and relationships, about the search query and search results from the knowledge graph. If a specific term or entity is recognized in the search query, use that to populate a knowledge panel on the right hand side with all relevant information about that term or entity. A knowledge panel provides users with a snapshot of information based on their search query. When displaying the knowledge panel and search results, pull the most up-to-date contextual information about a search result from the knowledge graph. For example, contextual information could include project statuses, most recent documents, or most similar content within the knowledge graph by metadata.

A knowledge panel collects and highlights project details in one place for a user search.

Natural Language Search Across Data

One of the most powerful resources for a knowledge graph search is natural language processing (NLP). NLP enables search to recognize entities in the graph as well as user intent. In one of our knowledge graph projects, EK developed an NLP-based search that recognized what entities a user was asking for and used that context to automatically collect and prioritize big data in a tabular format for analysts to review. This gave business analysts quick access to the data insights they needed from multiple large datasets. The ability to recognize the intent behind a user’s query enables the search interface to adapt and provide specialized answers to the most important questions.

Step Four: Ingest the Data

Steps one, two, and three focus on analyzing, prepping, and designing the knowledge graph search solution. Now that we have our initial plan, we can pull the data together through extract, transform, and load (ETL) pipelines and populate our knowledge graph.

Index the Data Source Information

Using the data collected in step one, build out the integrations with each of the required data sources. When possible, use application programming interfaces (APIs) or other feeds to extract content from the source systems. If this is not possible, database connections or temporary data exports may be required in order to proof out the integrations. Next, determine how content will be indexed from the sources by answering the following questions.

• What amount of content should be extracted each time the pipeline runs?
• How often should the content be indexed?
• Does the content from this data source need to be combined with any other data source?

There are numerous types of indexing techniques in order to ensure that the knowledge graph and search data are kept accurate and up-to-date without overloading the search indexing or data pipelines.

Transform Information using the Ontology Mapping

When designing the ETL pipelines, reference the ontology and ontology data source mapping to ensure that all information is transformed into the expected format. In most cases, this involves using transformation techniques like object mapping (i.e. Entity Framework) or XSLT to transform information from the source format into a graph data format (i.e. RDF) or into a document format for search (i.e. JSON). This is the first time that all information from a data source is being transformed so expect some data quality issues. Required fields may not always be present in the data, the values may not match the expected type, and data standardization issues may need to be addressed. Work with your stakeholders and data source owners to determine where and how issues should be addressed.

Transform and enrich your knowledge with a consistent vocabulary to populate a knowledge graph and display that information to users.

Enrich the Information with Context

One key piece to developing relationships between information from various sources is to leverage NER or an existing taxonomy. As content is pulled into the knowledge graph and search, the metadata fields provided with each type of information may not be enough. Combining information together from multiple sources builds a better picture of each information type, but some of the best sources of similarity reasoning and clustering will come from associating content with entities through auto-tagging a taxonomy or NER and topic modeling of terms. When designing the ETL pipelines to bring in data, consider how content may be enriched by adding in auto-tagging and NER techniques to the pipelines.

Step Five: Implement and Iterate

At this point, the indexed information is within the knowledge graph and search platform. In this step, build out a prioritized feature set based on the search designs from step three. Depending on the selected development stack, it may be beneficial to build an API layer on top of the knowledge graph and search platform and leverage these APIs to pull data into the user interface. In order to get the interface in front of stakeholders quickly, you may need to leverage some of the sample data you created in step 2.

Developing a user-centric product requires feedback early and often. Validate the designs with both stakeholders and users through demos and user testing. Demos allow stakeholders to give instant feedback on the solution as soon as it is available. For user testing, provide users with tasks to perform and observe how users perform the task. It is important to note where users click, where their eyes are drawn to first, and how design choices impact the flow of the website navigation.

Prioritize and iterate on the user interface based on user feedback and testing.

Make sure to continue to explore the unique features of knowledge graphs. Incorporating new relevant sources with relationships to existing data can help cover edge cases of your search queries that are not yet answered. Highlighting inferences made through traversing the knowledge graph at query time, can bring users previously undiscovered steps.

 Always remember search is a journey.

Finally, iterate on the solution and respond to feedback. Steps one through five are meant to be repeated over and over as

• New data sources and information is considered for search;
• Users ask different questions that requires updating the ontology;
• Designs adapt to feedback and testing to provide a more intuitive user experience;
• Pipelines are extended to extract more information from the data sources; and
• Features and design changes are required to the end solution.

Additionally, don’t forget to start small–EK recommends building out an end-to-end system, completing steps 1-5 in order for a subset of content and prioritized use cases. Use this first iteration to test capabilities and identify any integration risks or concerns.

Conclusion

These steps ensure that your organization builds the right search solution, creating a knowledge graph that answers your users’ questions and surfaces the results in an intuitive interface. Building search on top of a knowledge graph enables your organization to provide tailored, advanced search features as well as create a foundation of organizational knowledge that can be leveraged for other use cases such as chatbots, recommendation engines, and data analysis.

Interested in expanding your organization’s search to leverage the capabilities of a knowledge graph? Contact us and let’s work together to build a search solution that fits your organization’s needs.

The post Five Steps to Implement Search with a Knowledge Graph appeared first on Enterprise Knowledge.

While these tasks have always been difficult for new hires, the shift to remote work has made them even burdensome and problematic for those starting a new job during the pandemic. Being remote makes it all the more challenging to find experts, search for and locate documents, and collaborate with others, especially when organizations lack the KM technology or processes to support these tasks. Without colleagues in the office to guide them, new hires can feel overwhelmed and lost in remote work environments with no way to solve their issues.  This can lead to longer time to proficiency, lower employee retention and satisfaction, and ultimately money lost for the organization. 

There are, however, ways to alleviate issues that new hires and their mentors face. Effective Knowledge Management (KM) can help to address the onboarding challenges, especially in remote work scenarios. At Enterprise Knowledge (EK), we define KM as the people, culture, processes, and enabling technologies necessary to capture, manage, share, and find information. In the following sections, I will explain how KM can guide new hires to proficiency and ensure that organizations are empowering their employees to do their best work. 

Finding Experts 

Subject matter experts (SMEs) are often the foundation of many organizations. They provide deep expertise in subjects and can provide people of all tenures with the information they need to know to complete both basic and advanced tasks. While finding an expert in a subject or contacting a seasoned co-worker may seem like a simple task to most experienced employees, this can be a particularly daunting and stressful task for someone who is new to their job and isn’t familiar with the experts dispersed across their organization. New hires can especially struggle in organizations that do not have a searchable repository of experts, people, or documented information that references who SMEs are, and shifting to remote work has made it even more difficult to find and get in touch with people. 

One possible way to connect new hires to experts is by designing and implementing an expert finder. The creation of an employee search system with proper governance and management would allow new employees to actively seek out experts to best learn from them. An expert finder can enhance search results with people/expert profiles that display information about employees, their expertise, and basic contact information, thus allowing new hires to be able to seek out experts without having to know them beforehand, which is almost impossible if they’re just starting at an organization. With an expert finder, new employees would waste less time trying to find the people they needed to learn from and more time actually learning from them. This is particularly important in a remote and virtual setting, where it can be difficult to find and ask the right person the right questions as no one is there in person to assist a new hire. 

EK has assisted many companies with creating a portal where employees can find each other with accuracy and the information they need to make decisions on who to contact. For example, at a federally funded engineering research center, project managers were struggling to find employees who process specific skills sets or experience for their projects. EK incorporated a knowledge graph into a semantic search platform to engage employees to find individuals who had specific experiences and expertise, thus reducing time to find the right employee from weeks to a few minutes. This kind of search function would be greatly beneficial to new hires looking to find the experts that they could learn from to become proficient in their roles.  

Locating Documented Knowledge

While access to experts is important, they are also important knowledge workers who have their own tasks to accomplish. They can’t spend all their time teaching new hires the ropes, nor should new hires rely on ad-hoc, personal meetings to learn everything. It is equally important for organizations to have documented reference materials, policies, procedures, and learning materials that can help new hires get up to speed and that they can reference down the line. The lack of this documented information and knowledge, or the inability to properly and quickly find it, can easily halt any employee’s progress on projects and their day to day tasks, but new hires are particularly vulnerable to this type of slow down. Without context and experience, they won’t have the skills necessary to work around a lack of documentation. With remote work, this problem is amplified as a new hire has to wait for their mentor to respond before they can proceed if they cannot find documented policies or procedures that can show them how to accomplish a task. 

While the lack of this information can create roadblocks for a new hire and make them feel useless, Knowledge Management can help streamline this process. The creation of a Knowledge Base or searchable repository with internal facing pages is one aspect of a solution to this problem. Designing pages on the Knowledge Base that are specifically geared towards new hires will give them a place to start looking for the information they need to know. Working in tandem with other solutions such as a taxonomy for findability, properly configured search, and a content strategy to ensure content is up-to-date and easily digestible, a well-designed Knowledge Base will vastly improve the working lives of employees of all tenures. This would reduce the time new employees waste looking around various systems they may be unfamiliar with or waiting for responses from busy co-workers. This centralized hub of information would also provide new hires with the opportunity to explore their organization’s information and knowledge more deeply without having to gather important information piece by piece on their own, thus increasing their time to proficiency rate. 

For a global engineering firm who experienced a significant uptick in hiring due to COVID, we recently designed a central space on their SharePoint Online site where new hires could find the resources and experts to be successful during their first three months on the job. This site increased time to proficiency rates as well as overall satisfaction in their job experience. This not only helped new hires, but also supported more tenured team members because they were able to be productive more quickly than if they had to search multiple sites for the information they needed to do their job. This kind of ROI is exactly how KM can help organizations train their employees faster and more efficiently. 

Collaborating and Sharing Knowledge

Collaboration and knowledge sharing is key to the success of experts and new hires alike, but often it is difficult to collaborate within an organization, and everyone working from home only makes the problem worse. There are different systems and processes that enhance and encourage participation among groups of employees, including SMEs and new hires. 

The proper implementation of social and collaboration systems, guided by KM, can provide employees with a space (virtual or in person) to collaborate with one another and build their own communities. Learning from colleagues alongside other new hires, collaborating, building knowledge, and learning as a group are key factors to organizational success and community building. Giving new hires and employees of varying tenures the opportunity to interact and build collective knowledge together is very important. Ways to do this include implementing a Lunch & Learn series to provide a deeper look at topics followed by a discussion, the creation of collaboration channels in a tool such as Slack, or creating an internal company forum where people can post ideas, questions, and brainstorm strategies. Collective knowledge always enhances individual knowledge and makes it easier to turn important information from tacit and inside people’s heads to explicit and written down where it can be accessed by all. 

EK recently worked with the National Park Service to design and develop a Common Learning Portal (CLP) where employees can easily search and locate educational and instructional resources. The CLP was enhanced with social networking features that allowed learners of similar interests or backgrounds and careers to be able to connect with each other. This solution allowed for employees and volunteers of all tenures spread out across the United States to collaborate, share, find each other, build community, and learn all in one space. 

Personal Experience

While the COVID-19 pandemic is unfamiliar territory for all, new hires face unique challenges when onboarding remotely. Many of the simplest aspects of asking questions and finding information have now become difficult and time consuming. I can personally attest to how challenging onboarding remotely can be. I was hired and onboarded to EK fully remotely during the pandemic. There were certainly many challenges and roadblocks to my progress as a new employee, but EK had the KM foundations that served as my stepping stones to proficiency. 

For example, our internal Knowledge Base, populated with blogs, articles, best practices, and tutorials written by my expert colleagues, allowed me to get a jump start on understanding EK and my role within the company. Rather than wait for one of my mentors to field a high-level question, I could turn to the knowledge base and find my answers there. Additionally, our internal directory gave me a great sense of the fields and subjects in which my co-workers have expertise. This gave me easy and quick access to the right person to help me with certain problems, rather than relying on unformed social connections. If I was confused about a taxonomy project, our expert finder told me exactly which co-workers are experts in taxonomy who I could turn to for help. 

This internal directory was only the first step in helping me build strong relationships with everyone at EK. At EK, we leverage the collaboration and communication tool Slack. Our Slack channels are filled with people asking questions and receiving immediate answers, links to content, and input from many people, thus creating open and inclusive lines of communication across the company. Also, our bi-weekly Knowledge Sharing sessions gave me the opportunity to put faces to my colleagues that I had only heard of at that point and learn something new from whoever was presenting. These examples, among many others, gave me the opportunity to join the community and contribute to the collective knowledge developing at EK, even with the barrier of remote work standing in my way. 

My onboarding experience with EK showed me how the proper implementation of Knowledge Management and a direct investment in the curation and dissemination of information and knowledge can alleviate the tensions brought on by onboarding for both new hires and their mentors. I could not have asked for a better remote work onboarding experience, and I can only imagine how many more roadblocks I would have encountered without all the KM infrastructure at EK. Not only did I become proficient much faster than I thought possible, but I felt like I was part of the company and community more deeply than I expected, all thanks to KM and the great people working at EK. 

Conclusion

These are difficult times for every person and organization, but with the right dedication to Knowledge Management, new hires will become proficient much more quickly, providing value to their companies and giving them confidence to be the best employees they can be, whether they are working remotely or in-person. Employees who feel that they are learning, improving, and providing value will be more satisfied, and thus employee retention will increase. Contact us at EK and check out our success stories to find out more about how we support organizations in their KM efforts.

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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.

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