Organizations generate, source, and consume vast amounts of unstructured data every day, including emails, reports, research documents, technical documentation, marketing materials, learning content and customer interactions. However, this wealth of information often remains hidden and siloed, making it challenging to utilize without proper organization. Unlike structured data, which fits neatly into databases, unstructured data often lacks a predefined format, making it difficult to extract insights or apply advanced analytics effectively.

Integrating unstructured data into a knowledge graph is the right approach to overcome organizations’ challenges in structuring unstructured data. This approach allows businesses to move beyond traditional storage and keyword search methods to unlock knowledge intelligence. Knowledge graphs contextualize unstructured data by linking and structuring it, leveraging the business-relevant concepts and relationships. This enhances enterprise search capabilities, automates knowledge discovery, and powers AI-driven applications.

This blog explores why structuring unstructured data is essential; the challenges organizations face, and the right approach to integrate unstructured content into a graph-powered knowledge system. Additionally, this blog highlights real-world implementations demonstrating how we have applied his approach to help organizations unlock knowledge intelligence, streamline workflows, and drive meaningful business outcomes.

Why Structure Unstructured Data in a Graph

Unstructured data offers immense value to organizations if it can be effectively harnessed and contextualized using a knowledge graph. Structuring content in this way unlocks potential and drives business value. Below are three key reasons to structure unstructured data:

1. Knowledge Intelligence Requires Context

Unstructured data often holds valuable information, but is disconnected across different formats, sources, and teams. A knowledge graph enables organizations to connect these pieces by linking concepts, relationships, and metadata into a structured framework. For example, a financial institution can link regulatory reports, policy documents, and transaction logs to uncover compliance risks. With traditional document repositories, achieving knowledge intelligence may be impossible, or at least very resource intensive.

Additionally, organizations must ensure that domain-specific knowledge informs AI systems to improve relevance and accuracy. Injecting organizational knowledge into AI models, enhances AI-driven decision-making by grounding models in enterprise-specific data.

2. Enhancing Findability and Discovery

Unstructured data lacks standard metadata, making traditional search and retrieval inefficient. Knowledge graphs power semantic search by linking related concepts, improving content recommendations, and eliminating reliance on simple keyword matching. For example, in the financial industry, investment analysts often struggle to locate relevant market reports, regulatory updates, and historical trade data buried in siloed repositories. A knowledge graph-powered system can link related entities, such as companies, transactions, and market events, allowing analysts to surface contextually relevant information with a single query, rather than sifting through disparate databases and document archives.

3. Powering Explainable AI and Generative Applications

Generative AI and Large Language Models (LLMs) require structured, contextualized data to produce meaningful and accurate responses. A graph-enhanced AI pipeline allows enterprises to:

A. Retrieve verified knowledge rather than relying on AI-generated assumptions likely resulting in hallucinations.

B. Trace AI-generated insights back to trusted enterprise data for validation.

C. Improve explain ability and accuracy in AI-driven decision-making.

Challenges of Handling Unstructured Data in a Graph

While structured data neatly fits into predefined models, facilitating easy storage and retrieval of unstructured data presents a stark contrast. Unstructured data, encompassing diverse formats such as text documents, images, and videos lack the inherent organization and standardization to facilitate machine understanding and readability. This lack of structure poses significant challenges for data management and analysis, hindering the ability to extract valuable insights. The following key challenges highlight the complexities of handling unstructured data:

1. Unstructured Data is Disorganized and Diverse

Unstructured data is frequently available in multiple formats, including PDF documents, slide presentations, email communications, or video recordings. However, these diverse formats lack a standardized structure, making extracting and organizing data challenging. Format inconsistency can hinder effective data analysis and retrieval, as each type presents unique obstacles for seamless integration and usability.

2. Extracting Meaningful Entities and Relationships

Turning free text into structured graph nodes and edges requires advanced Natural Language Processing (NLP) to identify key entities, detect relationships, and disambiguate concepts. Graph connections may be inaccurate, incomplete, or irrelevant without proper entity linking.

3. Managing Scalability and Performance

Storing large-scale unstructured data in a graph requires efficient modeling, indexing, and processing strategies to ensure fast query performance and scalability.

Complementary Approaches to Unlocking Knowledge Intelligence from Unstructured Data

A strategic and comprehensive approach is essential to unlock knowledge intelligence from unstructured data. This involves designing a scalable and adaptable knowledge graph schema, deconstructing and enriching unstructured data with metadata, leveraging AI-powered entity and relationship extraction, and ensuring accuracy with human-in-the-loop validation and governance.

1. Knowledge Graph Schema Design for Scalability

A well-structured schema efficiently models entities, relationships, and metadata. As outlined in our best practices for enterprise knowledge graph design, a strategic approach to schema development ensures scalability, adaptability, and alignment with business needs. Enriching the graph with structured data sources (databases, taxonomies, and ontologies) improves accuracy. It enhances AI-driven knowledge retrieval, ensuring that knowledge graphs are robust and optimized for enterprise applications.

2. Content Deconstruction and Metadata Enrichment

Instead of treating documents as static text, break them into structured knowledge assets, such as sections, paragraphs, and sentences, then link them to relevant concepts, entities, and metadata in a graph. Our Content Deconstruction approach helps organizations break large documents into smaller, interlinked knowledge assets, improving search accuracy and discoverability.

3. AI-Powered Entity and Relationship Extraction

Advanced NLP and machine learning techniques can extract insights from unstructured text data. These techniques can identify key entities, categorize documents, recognize semantic relationships, perform sentiment analysis, summarize text, translate languages, answer questions, and generate text. They offer a powerful toolkit for extracting insights and automating tasks related to natural language processing and understanding.

A well-structured knowledge graph enhances AI’s ability to retrieve, analyze, and generate insights from content. As highlighted in How to Prepare Content for AI, ensuring content is well-structured, tagged, and semantically enriched is crucial for making AI outputs accurate and context-aware.

4. Human-in-the-loop for Validation and Governance

AI models are powerful but have limitations and can produce errors, especially when leveraging domain-specific taxonomies and classifications. AI-generated results should be reviewed and refined by domain experts to ensure alignment with standards, regulations, and subject matter nuances. Combining AI efficiency with human expertise maximizes data accuracy and reliability while minimizing compliance risks and costly errors.

From Unstructured Data to Knowledge Intelligence: Real-World Implementations and Case Studies

Our innovative approach addresses the challenges organizations face in managing and leveraging their vast knowledge assets. By implementing AI-driven recommendation engines, knowledge portals, and content delivery systems, we empower businesses to unlock the full potential of their unstructured data, streamline processes, and enhance decision-making. The following case studies illustrate how organizations have transformed their data ecosystems using our enterprise AI and knowledge management solutions which incorporate the four components discussed in the previous section.

• AI-Driven Learning Content and Product Recommendation Engine
  A global enterprise learning and product organization struggled with the searchability and accessibility of its vast unstructured marketing and learning content, causing inefficiencies in product discovery and user engagement. Customers frequently left the platform to search externally, leading to lost opportunities and revenue. To solve this, we developed an AI-powered recommendation engine that seamlessly integrated structured product data with unstructured content through a knowledge graph and advanced AI algorithms. This solution enabled personalized, context-aware recommendations, improving search relevance, automating content connections, and enhancing metadata application. As a result, the company achieved increased customer retention and better product discovery, leading to six figures in closed revenue.
• Knowledge Portal for a Global Investment Firm
  A global investment firm faced challenges leveraging its vast knowledge assets due to fragmented information spread across multiple systems. Analysts struggled with duplication of work, slow decision-making, and unreliable investment insights due to inconsistent or missing context. To address this, we developed Discover, a centralized knowledge portal powered by a knowledge graph that integrates research reports, investment data, and financial models into a 360-degree view of existing resources. The system aggregates information from multiple sources, applies AI-driven auto-tagging for enhanced search, and ensures secure access control to maintain compliance with strict data governance policies. As a result, the firm achieved faster decision-making, reduced duplicate efforts, and improved investment reliability, empowering analysts with real-time, contextualized insights for more informed financial decisions.
• Knowledge AI Content Recommender and Chatbot
  A leading development bank faced challenges in making its vast knowledge capital easily discoverable and delivering contextual, relevant content to employees at the right time. Information was scattered across multiple systems, making it difficult for employees to find critical knowledge and expertise when performing research and due diligence. To solve this, we developed an AI-powered content recommender and chatbot, leveraging a knowledge graph, auto-tagging, and machine learning to categorize, structure, and intelligently deliver knowledge. The knowledge platform was designed to ingest data from eight sources, apply auto-tagging using a multilingual taxonomy with over 4,000 terms, and proactively recommend content across eight enterprise systems. This approach significantly improved enterprise search, automated knowledge delivery, and minimized time spent searching for information. Bank leadership recognized the initiative as “the most forward-thinking project in recent history.”
• Course Recommendation System Based on a Knowledge Graph
  A healthcare workforce solutions provider faced challenges in delivering personalized learning experiences and effective course recommendations across its learning platform. The organization sought to connect users with tailored courses that would help them master key competencies, but its existing recommendation system struggled to deliver relevant, user-specific content and was difficult to maintain. To address this, we developed a cloud-hosted semantic course recommendation service, leveraging a healthcare-oriented knowledge graph and Named Entity Recognition (NER) models to extract key terms and build relationships between content components. The AI-powered recommendation engine was seamlessly integrated with the learning platform, automating content recommendations and optimizing learning paths. As a result, the new system outperformed accuracy benchmarks, replaced manual processes, and provided high-quality, transparent course recommendations, ensuring users understood why specific courses were suggested.

Conclusion

Unstructured data holds immense potential, but without structure and context, it remains difficult to navigate. Unlike structured data, which is already organized and easily searchable, unstructured data requires advanced techniques like knowledge graphs and AI to extract valuable insights. However, both data types are complementary and essential for maximizing knowledge intelligence. By integrating structured and unstructured data, organizations can connect fragmented content, enhance search and discovery, and fuel AI-powered insights. 

At Enterprise Knowledge, we know success requires a well-planned strategy, including preparing content for AI,  AI-driven entity and relationship extraction, scalable graph modeling or enterprise ontologies, and expert validation. We help organizations unlock knowledge intelligence by structuring unstructured content in a graph-powered ecosystem. If you want to transform unstructured data into actionable insights, contact us today to learn how we can help your business maximize its knowledge assets.

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The Solution

EK architected and developed a Sales Enablement Proof of Concept (POC) built in a CCMS with a front end in React. This POC broke the content in slide decks down into topical components that could then be efficiently assembled into customized microsites by representatives in the field. These highly configurable microsites allowed sellers to efficiently customize their sales collateral for the needs of buyers.

For example, content about a research study might have components for Overview, Methods, Design, and Results. When a representative wanted to discuss just the general details of the study, they had the option to only include the study Overview component in their microsite, so as not to bog down their conversation with unnecessary details. The content in the Dynamic Content POC was also much more conducive to being used on the go, by being available in a URL instead of a .ppt file that required downloading, and by being searchable both using keywords or via the site navigation. These features made it much easier for sales representatives to fluidly navigate from topic to topic during their conversations with medical professionals, customizing their presentation on the fly to respond to the lead qualifying information. The content model also accounted for regulatory and compliance rules by creating relationships between content segments that must be shown together, or in a certain order. This way, representatives faced less of a burden to recall and adhere to compliance themselves, and could count on the content itself to be compliantly ordered. 

Finally, the POC made multimedia assets like images, graphs, and charts more findable by leveraging a specific content component for media assets. This way each asset could be tagged and described with metadata, rendering it searchable by keywords. This created a much simpler interface for representatives seeking to find a specific data point or image within many media assets. Ultimately multimedia handling requirements at the enterprise level would require a DAM, but EK was able to deliver satisfactory functionality appropriate to the POC within the CCMS.

The EK Difference

EK’s Advanced Content Team was able to provide the healthcare company with a dynamic, innovative Sales Enablement POC that solved for multiple challenges medical sales representatives were facing. EK combined industry leading content engineering expertise to redefine the shape and structure of content with user experience design best practices to ensure the solution was intuitive to navigate so that Sellers could easily and efficiently find customized Go To Market content and sales collateral. EK’s technical consultants were also able to develop an extensible POC over a short amount of time and with little risk, so that the healthcare company could quickly realize business value from their investment. Rapid delivery of working software also provided an efficient tool for testing and providing feedback on features and functionality.

The Results

As a result of the Sales Enablement POC, the healthcare company has been able to populate and build out a larger Dynamic Content repository that extends the content model and includes even more configurable sales collateral. The company will be able to mature their solution over time using the extensible content model and Sales Enablement POC that EK built. Medical sales representatives will ultimately be armed with an ever-expanding selection of easily personalizable content, resulting in improved sales conversion and win rates.

 

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This event is brought to you by The Content Wrangler and is sponsored by Heretto, a powerful component content management system (CCMS) platform to deploy help and API documentation in a single portal designed to delight your customers.

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The Solution

EK architects, developers, and strategists worked with the office over a period of three years to iteratively develop and release a structured content management system that would revolutionize the way the office managed content. In the first year of the engagement, EK’s expert content engineers performed a detailed content analysis to identify the multi-channel publishing opportunity of content. The output of the content analysis was a content model which defined how content would be broken down into its component parts in the new system, documenting the relationships between content types, supporting metadata, and the overall structure of the content (and templates) which developers would later implement in the content management system. 

A typical approach for achieving structured content in a CMS employs the use of predefined fields which are filled out at the time of content creation – similar to the experience of filling out a web form. Due to the need for near-instantaneous content publishing, that experience was undesirable. Content authors needed an authoring experience which felt unstructured, while it actually structured content behind the scenes. The solution for this was a custom built document editor that allowed users to add content in a seemingly unstructured fashion. Upon save, a translation microservice would save the editor’s content into JSON structured content when saved to the database.

Government executives who were the ultimate audiences for this content had very strong preferences for how they wanted to consume the content. Some wanted to continue to receive email, some wanted the email to go to an assistant who would print a PDF for them to read while in transit, and some had a strong preference for a mobile app or SMS. By structuring content, we enable the flexibility to design multiple end-user experiences to consume the content.  

While the need for immediate distribution to government executives was met through these distribution channels, analysts had a need to search for previously published content in order to analyze trends and make policy recommendations. This type of deeper analysis is better supported by a web interface and so a web application with search functionality became yet another publication endpoint. 

One thing is a constant of content models:  as an organization evolves, so must the model. Indeed, over the course of our three year engagement, the name and structure of the reports being authored by the government office changed several times. It became imperative that the custom CMS EK developed would support the administrative management of additional content structures and templates. In response to this need, EK created a template manager which allowed non-technical content designers to define their own templates. They could instead choose which structured content components from the content model would be in the default content creation experience as a content author rapidly created a new piece of content directly through the administrative interface.

The EK Difference

EK employed our Advanced Content expertise to design and develop a custom CMS which met the complex requirements of the government agency. Our expert consultants identified the need for rapid, multi-channel publishing and immediately translated that to the technical requirement of a structured content model and a CMS which would support not only structured content, but also the creation of content in a way which felt unstructured to content authors. 

EK conducted a targeted content analysis, facilitating focus groups with the stakeholders to identify themes and duplicate content across publications. This enabled us to help the office define the major and minor content types within publications, highlighting the structured content components that were critical to a publication and had potential for reuse in multiple publications. Additionally, we were able to define the structured content model, including the content types, the structural components which were reusable across content types and end-user experiences, and the metadata which supported reuse of structured content components. 

EK not only provided content strategists and content engineers for the engagement, but also provided a solution architect and software engineering team with dedicated CMS development experience. This team was able to customize a Drupal CMS and enrich it with a set of Python microservices in order to maximize the value of the open source tool, but also meet the complex, custom requirements of the government agency.

The Results

EK architected the content model to accomplish a few different business goals: 

• Efficient Content Authoring: Through collaborative content creation, the application of templates, and tools for the automated application of metadata, staff writers spend 20% less time composing content. 
• Optimized End-User Interfaces for Consuming Content:  The structured content model and CMS enabled authors to rapidly compose one report and publish relevant structured content components to multiple distribution channels including email, PDF, and web. Email and PDF met the preferences and demands of government executives and web met the needs of analysts. Additionally, recipients of the office’s publications benefit from consistently structured, authoritative information that, through a structured content model.
• Improved Knowledge Management: Analysts gained ability to search for and analyze previously published content where previously that knowledge was lost to the email black hole.
• Decreased Time to Inbox: One of the highest priority distribution channels for the CMS is publishing to email. The optimizations to the content management system and processes reduced the average time of distribution to email inboxes from 76 minutes down to one minute.

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John Collins, Senior Content Architect at Atlassian, and Yanko Ivanov, Enterprise Knowledge Senior Solution Architect, will co-present. Collins and Ivanov will explore how Atlassian is helping end-users and administrators prepare for changes by delivering timely, relevant, personalized multi-channel release notes enabled by componentized content and taxonomy.

Two of Enterprise Knowledge’s Software Engineers James Midkiff and Kate Erfle, will present a live demo of a software solution built by EK software engineers and showcase how it improves the accuracy and efficiency of content recommendations across multiple user interfaces inside a global development organization’s systems.

Britt Elmer, Director of the Content Strategy Program at TSYS, and Yanko Ivanov, Enterprise Knowledge Senior Solution Architect, will discuss the business case and drivers for the advanced content authoring and delivery framework solution at TSYS. Learn from the two content experts about how they leverage a unified taxonomy and auto-tagging, a componentized content model for content reuse, knowledge graphs, and additional content engineering best practices to deliver connected, discoverable, and personalized content to  internal and external clients.

Register for the conference here: Online event registration | Omnichannel Conference

June 13-16, 2022

Sessions will run 10am to 7pm Central European Time

Use discount code DISC3022

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Motivation

Motivation is a complex factor to understand, but psychologists, neuroscientists, and learning theorists have amassed quite a body of research. We know that motivation can be positively influenced by intrinsic motivation, experiences of success, and overall positive system user experience.

Learning ecosystems that leverage curiosity and interest to drive intrinsic motivation create much better learning outcomes than learning ecosystems which depend upon compulsory training or fear. There are obviously a lot of process and cultural elements that influence curiosity and interest-driven learning, but knowledge management technology has a role to play as well. In knowledge management, we talk a lot about the findability and discoverability of information:

• Findability describes the ability of a system user (in this case a learner) to find the information for which they came to the system. If I want to learn the basics of graphic design, I might execute a search for “graphic design basics.” Findability refers to my ability to find a beginner eLearning course so I can get started.
• Discoverability describes the ability of the learner to discover new information in the system which is useful – but for which they weren’t even searching. In the example above, I search for “graphic design basics” and find an eLearning course, but I also find an entire training plan with multiple levels of graphic design proficiency and supporting learning assets for each. I didn’t know those additional resources were there, but I’m thankful to discover them as they provide me not only with the course, but with a roadmap to continue advancing my skills.

A well-designed knowledge management portal supports both findability and discoverability of learning assets. Enabling the discoverability of additional learning assets and learning paths inspires curiosity and helps create an intrinsic motivation to learn.

Research shows that learners who experience success are also motivated to keep learning. Knowledge management technology can build success experiences into your organization’s learning ecosystem by automatically conferring certificates when learners complete metadata-enabled learning paths. Knowledge management technology can also create personalization of feedback by leveraging some of the same tools we use to deliver a multitude of content personalization experiences – componentized content and a robust metadata strategy.

Motivation is also strongly linked to the overall user experience a learner has with the learning technology. Learning is a process which requires sustained attention and effort, and if a learner is frustrated with outdated information, a lack of cues to guide attention, or visual clutter which creates cognitive overload, motivation is greatly reduced.

Attention

It is difficult for learners to sustain attention, and many learning activities take place in an online environment where there is fierce competition for that attention. Many traditional training approaches rely on unrealistic expectations of our ability to pay attention. Full-day, instructor-led workshops or even hour-long webinars are examples where learner attention can drop off drastically.

Knowledge management technology can provide solutions to this problem. Componentized content can enable the chunking of educational content in such a way that the same core components of content are reusable across multiple learning contexts. SCORM packages promised this benefit, but SCORM was only designed for reuse within eLearning courses. With ever-increasing demands on learner attention, we know that diverse learning opportunities – including informal learning and social learning – are absolutely critical. Componentized content in a CCMS can actually enable the reuse of content in any context – not just in courses.

A Headless CMS delivery architecture can provide further benefits and allow for the personalized delivery of these reusable learning asset components across multiple learner experiences. If you’ve created a reusable learning asset that explains how to create a budget report, a Headless CMS would enable you to publish that information to:

• A checklist that provides context for a project manager to create and update the report; and
• An explanatory reference sheet for a department director that explains how to apply the information in the report for department-level strategic planning.

When we leverage the latest knowledge management technology to create reusable, componentized learning assets, which can be reused across multiple learning experiences, we allow ourselves to create shorter, varied, and personalized learning experiences, which will help our learners sustain their attention and improve learning outcomes.

Summary

A modern workforce faces many demands for their time and attention and it’s easy for learning to get put on the back burner – even for those of us who love learning. When designing a learning ecosystem, it’s important to remember the learning theory that helps us best support learners and set them up for successful learning outcomes. Supporting learner motivation and attention are key. Knowledge management technology has the potential to improve the motivation and attention of learners – and thereby increase learning outcomes. If you’d like to apply knowledge management best practices to the design and development of your learning ecosystem, EK can help.

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• Componentized Content Management Systems (CCMS) and 
• Knowledge Graphs (Graph).

A CCMS allows organizations to create and manage content so that people receive only the information they need. A Graph allows organizations to better target what information should be delivered. In the rest of this blog post, I am going to explain how these two tools work to provide the best possible content personalization experience. To keep things simple, I am going to refer to the receiver of this personalized information as a customer, although these concepts could easily apply to personalization for partners and employees as well.

Tool 1:  Bite-Sized Content Components

A CCMS supports the content side of the personalization equation. It allows organizations to author and manage content in components or sections rather than long documents. Componentized content is structured content that represents a portion of a larger document (typically a chapter or section) that can be combined to build documents dynamically. A great example of this is your car’s owners manual. This manual has instructions for changing a tire, filling the wiper fluid, and jump starting the car. Dividing the content of the owners manual into components allows the manufacturer to send only the jump starting instructions when that is what the customer needs.

Truly effective personalization requires this division of content so that the customer gets only the information they need and not extraneous information. Imagine you are a product manufacturer and you sell multiple products, many of which use the same or similar parts. As you interact with customers, it is important that the information that you provide to them is limited to products that they own or related products that would be of interest. A CCMS allows organizations to deliver individual components to directly answer questions or to dynamically assemble larger manuals that include only information relevant to the products the customer owns. This level of personalization reminds the customer that you understand who they are and that you value their time. A common phrase in Knowledge Management is “Deliver people the right information at the right time!”. A CCMS helps ensure that the right information can be delivered in a way that is specific and relevant to the user.

Tool 2:  Knowledge Graphs

Once the information is componentized the personalization platform needs a way to decide what information is the right information to share with the customer. This is where Knowledge Graphs provide a level of control over personalization that is new and much more powerful than prior technologies. Knowledge graphs deliver on personalization through two key features: aggregation and inference.

Graphs are very good at aggregating information from multiple sources. This is the use case that Google shows with their famous knowledge panels (the information about people, places, and things that show on the right side of the search results). A graph can pull information about customers from multiple sources such as the CRM system, product support tickets, and sales information. This aggregated view means that the graph can be used to determine which content chunks should be assembled together to best align with the purchases and concerns of the customer. The CCMS produces the chunked content and the graph assembles the “right” chunks of content and delivers them to the customer based on what the graph knows about the customer.

A graph can aggregate more than just information about a customer. Graphs are also used to aggregate information about the products and services of an organization. This information can come from the product information management system, the product catalog, and ticketing systems. With this information, the graph can look at the products a customer owns and the latest information about those products so that highly targeted and almost predictive information can be delivered to customers. For example, a customer may own a product that has had a recent recall. The CCMS stores information about how to get the recall and how to install the solution. The graph is able to proactively see that the customer owns this product from the customer portion of the graph and then identify that the product has a recall from the product portion of the graph. The organization can then send a highly personalized and targeted message that explains what the recall is and how to install the solution. This type of proactive personalization can turn a potentially negative situation into a more positive engagement. A simplified example of how the graph provides this information can be seen in the image below.

Additional personalization can be offered through a feature in graphs called inference. Inference occurs when two entities in the graph are seen as related because they have relationships in common. For example, two products that a company offers might use a part that lowers noise. A third product might use a different part, but one that also offers noise canceling features. Even though these products are not directly related we can infer that they are similar because the parts that they use have a similar characteristic (see the image below).

Inference allows organizations to personalize recommendations in a way that could not easily be done with older technologies. This opens a new path for personalization that allows for even more proactive content interactions with both customers and prospects.

Summary

Componentized Content Management Systems and Knowledge Graphs are foundational elements that are key to providing content personalization. Organizations that personalize customer, employee, and partner experiences with these tools can create compelling digital experiences that surprise and delight. Our content management and graph specialists can help your organization build a true cutting edge personalization platform. If you are interested in learning more about our services in this area, you can reach us at info@enterprise-knowledge.com.

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