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AI Solutions for the Digital Workplace

Creating an intuitive workplace for a digital world

Personnel Changes, Siloed Data, and Digitalization of Systems

These are simply a few things to consider in the ever-changing digital workplace – but what exactly is a digital workplace?

Whether seated at a desk at the office or comfortably on a couch at home, laptops, tablets, and smartphones have created a virtual space where we can perform our work-related responsibilities from almost anywhere. The traditional concept of a “workplace” is becoming increasingly outdated in our digitally connected world.

The digital workplace needs to be accessible and agile, reliable and trustworthy, intuitive and efficient. That being said, digitalization of an organization – especially a large one that has massive amounts of legacy data and content – is extremely difficult. As the professional landscape changes, companies are seeking out advanced technologies to help them navigate the challenges they contintuously experience.

Semantic AI is the answer, where through knowledge graphs, semantic capabilities can give organizations the framework to optimize workplace structures and processes, thus improving the employee experience and quality of service to customers. 

Semantics can transform inconsistent data into interoperable knowledge graphs that form the foundation of intelligent applications – all while maintaining the existing infrastructure of the organization so that they do not have to worry about making drastic tool changes.

Built on knowledge graphs, PoolParty has a number of examples of AI solutions for the digital workplace. Knowledge Hubs, Smart Recommender Systems, and PoolParty for SharePoint can all tackle obstacles such as personnel changes, siloed data, and digitalization of systems, to name a few.

The common challenges when crafting a digital workplace

When things are not being linked together, whether through synonyms on a linguistic level or through repositories for data and content management, they simply just sit.  There is no enrichment, there is nothing to learn from, and the potential of these objects are wasted because they’re still in a rather basic stage.

This happens when …

    • Critical information is spread and sectioned off into too many places or Data silos
    • No universal or central vocabulary is being used across a database or Multilinguality
    • Different data points and business objects are not being connected to generate valuable insights or Data with no knowledge
    • Manual and paper-based processes are transformed from analog to digital or Digitization
    • Changes in human resources impact how and what knowledge is shared and captured or Knowledge retention

To mitigate these problems, the following AI solutions for the digital workplace can be used:

Knowledge graphs ensure that enterprises no longer have to deal with silos and disparate databases. 

Knowledge hubs provide a powerful environment that contextualizes information in one place – making it easier for users to find, maintain, and use assets in the future.


Recommender systems make sophisticated matches between objects using the context within a knowledge graph. Users can benefit from “suggested reading” items and personalized search experiences.

Knowledge graphs as a basis for AI solutions

A Knowledge Graph is a model of a knowledge domain created by subject-matter experts with the help of intelligent machine learning algorithms. It provides a structure and common interface for all of your data and enables the creation of smart multilateral relations throughout your databases. Representing an additional virtual data layer, the Knowledge Graph lies on top of your existing databases or data sets to link all your data together at scale – be it structured or unstructured.

For perspective, the graphic below shows a simplified version of a knowledge graph which connects various objects together so that you may see the logical relations between those objects.

An example of a graph database in an easy-to-understand knowledge graph of the Mona Lisa.

 Graphic from Yashu Seth

On the business side, an enterprise knowledge graph contains business objects and topics that are linked, classified, semantically enriched, and connected to existing data and documents.

Knowledge graphs are especially helpful when considering data silos across a large organization. Typically, marketing stores their data in one place, product management stores theirs in another, and so on and so forth. Within these different storage systems, there are also vastly different ways to build and manage the data; marketing may use mostly text documents and slide decks, and product management may rely on spreadsheets. The obvious problem here is that none of this data is being shared even though different departments could benefit from it.

Perhaps the marketing team would like to run an analysis on products that have garnered the most customer attention and support, or maybe they want to understand how the product itself works in order to create content for product campaigns. How can the marketer do this when all the information sits in the product management silo that is sectioned off from them? As a result of data silos and a lack of sharing, employees on average spend more than 25% of their time searching for the information they need to complete their tasks – effectively wasting productivity and money.

Enterprise knowledge graphs ensure that your data stays put, yet can be efficiently linked together. By providing a semantic layer, the enterprise knowledge graph does not need to store instance data, but rather helps map information needs and often complex queries (as used by recommender systems, for example) to the required information appropriately. An enterprise knowledge graph that has been correctly implemented will guarantee that all users (regardless of which department) will always have the most up-to-date and relevant information because it will act as the single source of truth for all your data.

Other Resources


White Paper:

Knowledge Graphs Done Right – Tips for Implementing Semantic AI in Business

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Case Study:

Knowledge Graphs in the Banking and Insurance Sectors

Learn more 

What is a Knowledge Hub?

Knowledge graphs not only help to curb data silos, they also serve as the foundation to knowledge hubs. We can think of a Knowledge Hub as a “one-stop-shop” of data, content, and know-how for an enterprise, where data converges together in a connected workplace and provides the best user experience possible – making it easier for users to find, maintain, and use assets in the future.

At the heart of a strong knowledge hub is a solid knowledge graph. Connecting data across your knowledge domains, the knowledge hub draws from the underlying knowledge graph for quick and easy access to content.

Consider the example of a consultancy firm that takes on various client companies across the globe. With a knowledge hub, dedicated account managers can view all their clients’ information and assets. The knowledge graph that sits in the background connects all their relevant information in one place, so account managers can seamlessly manage their clients’ issues with traceable client history, regardless of the client size or number of subsidiaries they may contain. Account managers can also integrate external information such as local workforce regulations, compliance laws, and tax information to the client profiles, in order to easily advise HR best practices. 

Consultants can anticipate challenges by having a detailed, dynamic overview of their clients’ systems, and recommend the best services in real-time.

Backed by semantic technologies, a PoolParty Knowledge Hub provides a comprehensive solution for the further development of your existing knowledge infrastructure. The good news is you don’t need to start from scratch to build a knowledge hub. PoolParty provides a “semantic layer” that integrates seamlessly with your existing knowledge infrastructure. 

The Beauty of the Semantic Layer

Altogether, our semantic capabilities allow you to transform inconsistent metadata often found in siloed systems into interoperable knowledge graphs that build the foundation for intelligent applications. These digital workplace solutions use your data more effectively so that you can benefit from a more productive workforce.

Company-wide consistency

No matter where your knowledge resides or will be used, profit from a standardized, universal language of your content.

Factual Fidelity

The ability to access a single version of truth is a requirement for deployment scenarios in regulated and critical industries.

Orchestrated Governance

A semantic layer sits between the data and the applications, providing the digital infrastructure with the right levels of management, authentication and authorization.

Less Time to Insights

We combine the knowledge of your experts with the power of machines to produce data that is easier to access, use, and understand.

Use Cases for a PoolParty Knowledge Hub

A PoolParty Knowledge Hub can be implemented virtually anywhere to help an enterprise improve the findability of objects, remove data silos, reduce costs in maintenance, and achieve faster time to delivery. The following use cases highlight exactly how a knowledge hub can help in various scenarios depending on the user.

Content Specialist

The documents and materials you regularly work with are often hard to find and slow to turn into profitable results. A knowledge hub allows you to keep track of your content in one dashboard, enabling smarter content creation and maintenance.


Your forecasting is crucial to make informed decisions about current processes and next steps for innovation. By having information at your fingertips with a hub, you can analyze your data more easily and feel confident in next best steps.


A hub removes silos and lets you create a universal access point for your enterprise’s employees. You can scale your AI applications with more precision and continuously evolve your infrastructure securely.


All this culminates in a better user experience for your customers. You will be able to deliver quality services and products to your customers with more ease, keep them regularly informed, and shape personalized customer journeys.

What’s the benefit of a knowledge hub?
Where a library simply stores information, a knowledge hub provides a powerful environment that contextualizes information in one place. Allowing the user to efficiently store, retrieve, and edit all in one digital workplace.

Other Resources



The Semantic Knowledge Hub – where enterprises benefit from connected workspaces and better user experiences

Download now 

Success Story:

A Knowledge Hub at HealthDirect Australia – the basis for innovative health services

Read more 

What is a Smart Recommender System?

Recommender systems are powerful tools that can assist users in accessing information, media, products, and other assets. In an enterprise, recommender systems are only helpful if they match the specific requirements of the employees who are using them.

Building on a knowledge-based approach with the help of knowledge graphs, smart recommender systems provide employees with the content they seek plus additional suggestions that are not explicitly related to their search query; though the employee is not specifically looking for this content, it can provide additional input that is relevant to the query because it is deemed relevant through these implicit relations.

While other systems go through untraceable processes, knowledge-based recommendation systems follow a clearly defined step-by-step process. (1) The user submits the query, which consists of a sentence, a paragraph, a section, or an entire document. (2) The text passes through the text annotation component and (3) receives its semantic footprint. (4) The subsequent query expansion is a traceable intervention in recommendation depth and recommendation sharpness. (5) The matches found thus contain both the implicit knowledge of the domain model and the query-specific adjustments. (6) The finally obtained recommendation is clearly traceable and controlled.

Smart recommenders are not only useful to find content – which proves to be a major headache of organizations – they are also helpful in human resources and project planning.

The HR Recommender is a demo of a prototypical HR solution made in PoolParty, which draws upon employee profiles, project specifications, and job postings.

The user can assume the role of an employee whose semantic footprint contains employee data such as role, department, hard skills, soft skills, and language skills. The user can toggle skills to see who they could get in contact with about a particular skill, which existing projects best match their profiles, and which open positions could suit them for further development. From an HR and management perspective, they can have an overview of the skills held by colleagues to either identify gaps in the workforce and define career pathways for specific employees who can build on existing skills.

With smart recommender systems, employees are better informed with the information that is supplied to them, so they can complete tasks more efficiently and feel more secure in decision making – ultimately freeing up employee working time.

The Major Benefits of the PoolParty Recommender

Built for you!

Out-of-the-box solutions quickly reach their limits and it can happen that your recommendation system does not generate the added value you expect. The PoolParty recommender system is as flexible as your company’s needs are. We have been integrating knowledge systems for companies for over 20 years, so our systems are prepared for your specific circumstances by design.

The Advanced Assistant

Semantic recommender systems are driven by domain, context, and intent, which means that they are highly customizable to an organization’s specific use case. While other systems cannot function as the “knowing assistant” to the user, the PoolParty Recommender can make knowledge available that makes a difference and adds value.

Your Plug-in-Style Recommender System

While other systems require a company’s knowledge management to be built from scratch, the PoolParty recommendation system leaves the company’s internal knowledge management intact as basis for implementation and training. A usually existing taxonomy, knowledge graph, or corpus can serve as the foundation on which the PoolParty recommender system is built. The recommender can be operated by existing personnel.

How can smart recommender systems help?
It’s not that the data and information you need to do your job isn’t there,
it’s just that it’s never just one click away – it’s many hops, and that’s tiring. That’s where the PoolParty Recommender comes in. A semantic recommender is one of the prized AI solutions for the digital workplace because it can get employees the information they seek based on the sophisticated pairings it’s making to the search query in the background. 

AI Solutions for the Digital Workplace: Bringing Recommender Systems and Knowledge Hubs Together

When a knowledge hub is coupled with semantic recommendations, the outcomes for the workplace are even higher. The following screenshot is an example of the knowledge hub used at Semantic Web Company:

This image is a screenshot for a knowledge hub which is an AI solution for the digital workplace

Taken in the context of a Knowledge Hub for an enterprise, an employee searching for a value proposition summary for a prospect could also benefit from being recommended an article that lists all the times that the company was mentioned in Gartner publications, a price list, use case material with the relevant contact who worked in building a previous PoC, etc.

The knowledge hub – powered by the recommender system – increases the findability of what the user is searching for and helps them take advantage of new information that is just as useful to the original search intent. 

Other Resources


White Paper:

Recommender Systems for Business Professionals

Read more 


Building Recommender Systems that Work

Watch now 

PoolParty for SharePoint

PoolParty for SharePoint is a digital workplace solution that lets you take advantage of semantic search, automated tagging, and a centralized taxonomy manager – offering a more powerful search experience to the native SharePoint Online application.

Where enterprises often experience weeks of manual work to update documents with (rather simple) tags, PoolParty can provide more precise and enriched tags with no hassle. Our solution allows you to capture and categorize information worth keeping, tag content so it’s easy to find, and validate it with expert approval to maintain quality.

Enable knowledge exchange with AI you can trust

Remain competitive by adopting a cutting edge, explainable AI

Mitigate risks by working with data that is a 1:1 match for your business

Make knowledge accessible across your organization by connecting disparate libraries 

Feel confident about content through maintaining a single source of truth

Empower your people to work independently and consistently

Spend less time looking for what you need

Retrieve documents in a fraction of the time with a new and improved search field

Get more relevant and accurate results based on semantically-enriched metadata

Benefit from additional content discovery with smart recommendations

Minimize the need for extensive browsing by narrowing down results with search facets

Explore the context of tags with our Info Box to see how documents and topics relate to each other

Our solution is implemented with the latest Microsoft technologies such as SharePoint Framework (SPFx) and Microsoft Graph API. There is a direct connection between the Azure Platform and the PoolParty server.

Only a few hours after setup, the software is able to seamlessly label SharePoint documents, recommend documents with related content and semantic search, and enrich documents with relevant information from external sources.

Useful Resources

Product Sheet:

Content Discovery, Content Reuse, Collaboration – The path towards an enriched SharePoint experience

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Intergration Page:

Leverage smarter tags and centralized knowledge management for better finability of assets

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Creating an ideal employee experience with the digital workplace


While most orangizations produce large amounts of data on a daily basis, few are able to use this information to gain valuable insights and enhance employee experience, especially when data points need to be connected.

Linking information from different sources using knowledge graphs provides the basis for a unified system that connects information and enables informed decision-making for knowledge workers in all industries. Knowledge graphs form the basis of numerous applications that can sit on top, thus improving the digital workplace.

Knowledge hubs are a space where data, content, and know-how of an enterprise connect – allowing employees to easily find, maintain, and use assets for task completion.

With the help of smart recommender systems, employees recieve a helping hand in accessing content. These recommender systems are built on a knowledge-based approach and provide users with intuitive recommendations not explicity connected to their seach query. In the end, the employee is better informed with the information that is available within the enterprise.

The digital workplace is not a one size fits all case. Much like the users that work in the digital workplace, it cannot be static – it has to be as dynamic as the needs of the user. AI Solutions such as Knowledge Graphs, Knowledge Hubs, and Smart Recommender Systems help organizations create a customized user experience in the digital workplace that is accessible, reliable, and agile.

These steps toward creating an intuitive workplace for a digital world help organizations reduce or minimize impact of personnel changes, breakdown siloed data to create accesibility of content, and promote the digitalization of systems to provide an ideal employee experience.

See examples of AI solutions for the digital workplace in our tailored webinar collection