It’s a haunted time of year, so let’s make this scary word a little more approachable. In this blog, I want to help alleviate concerns about ontologies by defining what the word means for an organization so that you can discuss them with your colleagues without inducing fear.

How EK defines Ontology

Enterprise Knowledge (EK) defines an ontology as “a defined model that organizes structured and unstructured information through entities, their properties, and the way they relate to one another.”

Let’s tackle each part of that definition separately.

A Defined Model

The simplest way to think about an ontology is as a data model. I commonly use “data models” as an alternative to ontologies – this similarity is best realized by looking at its Wikipedia definition:

A data model is an abstract model that organizes elements of data and
standardizes how they relate to one another and to the properties of real-world entities. 

Ontologies and data models both provide detailed and visual representations of information, helping us understand what we have and what we can do with it. Both can be designed in Excel or through model-specific languages such as UML (Unified Modeling Language) or OWL (Web Ontology Language). However, ontologies slightly differ from data models in that they focus on describing an entire data domain, such as sustainability or finance. Also, ontologies define the meaning of a domain by providing structure and definitions, whereas data models are normally only the structure. EK experts have defined several steps and best practices to help you build a successful model.

Organizes Structured and Unstructured Information

Every organization has a wealth of potential data waiting to be leveraged. Deliverables (documents), shared documentation sites (like Confluence and SharePoint), and structured data sources (HR or project data, databases, etc.) are all relevant inputs to help an organization answer questions. In our model, we want to bring this data together and describe it well enough that all our users can understand and leverage it. Ontologies excel in data aggregation situations, as their purpose is to represent a concept of information, regardless of the shape the data takes (structured or unstructured).

Through Entities, Their Properties, and How They Relate

When we describe the data that we have, we organize it into groups. For example, at EK, we could create groups for employees, clients, projects, and deliverables. Each one of the groups is a type of entity, and each individual in the group is an entity. That is, all of my colleagues would be entities, and so are the projects we have worked on. Every entity we examine has some information associated with it, like a name, description, or date: these are the properties. And, at EK, we know that employees work on projects for clients. This association is an example of how our entities relate.

Ontology Use Cases

We put in all of this effort defining an organization’s information and, as a result, we have a pretty diagram of connected bubbles. 

Now what? While it’s possible to create a data model for an organization for the pure purpose of understanding the domain, there are usually knowledge management use cases driving the model. 

Unified Views of Knowledge

Developing an ontology, or data model, is an important part of understanding what an organization has, how it can be brought together, and how it can be leveraged to support a successfully unified user experience. Often described as 360 Views or Knowledge Portals, a unified view of an organization’s knowledge enables internal and external users to find and discover information from multiple sources in one place.

From an internal user perspective, knowing who is an expert in a particular topic or who has worked with which clients on which projects – and being able to find all of that information in one place – enables employees to make decisions and take action faster. This information is usually scattered across multiple systems, but an ontology provides evidence of how an organization understands data across systems, allowing architects and designers to approach a solution with an organization-wide mindset.

From an external perspective, being able to find all product documentation, support cases, and FAQs in one place alleviates customer frustration when looking for answers. In this case, the ontology enables an organization to identify and prioritize knowledge that should be made readily available to users.

Powering Recommendation Logic

One of my colleagues recently published “5 Steps For Building Your Enterprise Semantic Recommendation Engine,” and, lo and behold, step two is “Create Supporting Data Models”. To support a comprehensive recommendation engine, an organization should create ontologies to define the web of complex relationships within data. These relationships between people, clients, projects, and so on help to realize the recommendation results, as a developer can leverage those relationships to create paths from some input to the desired output.

For example, EK worked on a course recommendation system for a healthcare provider. The recommendation engine leveraged the relationships between key learning competencies and courses to help personalize course recommendations for individuals based on their learning goals. Additionally, the ontology helped highlight areas where the organization could add information and improve data quality to provide more recommendation pathways to consider.

Conclusion

The post Breaking it Down: What is an Ontology? appeared first on Enterprise Knowledge.

• Limited understanding of the business application and use cases to define a clear vision and strategy.
• Not knowing where to start, in terms of selecting the most relevant and cost-effective business use case(s) as well as supportive business or functional teams to support rapid validations.
• There are multiple initiatives across the organization that are not streamlined or optimized for the enterprise.
• Enterprise data and information is disparate, redundant, and not readily available for use.
• Lack of the required skill sets and training.

Our experience at Enterprise Knowledge demonstrates that most organizations are already either developing or leveraging some form of Artificial Intelligence (AI) capabilities to enhance their knowledge, data, and information management. Commonly, these capabilities fall under existing functions or titles within the organization, such as data science or engineering, business analytics, information management, or data operations. However, given the technological advancements and the increasing values of organizational knowledge and data in our work and the marketplace today, organizational leaders that treat their information and data as an asset and invest strategically to augment and optimize the same have already started reaping the benefits and having their staff focus on more value add tasks and contributing to complex analytical work to build the business. The most pragmatic approaches for developing a tailored strategy and roadmap toward AI begin by looking at existing capabilities and foundational strengths in your data and information management practices, such as metadata, taxonomies, ontologies, and knowledge graphs, as these will serve as foundational pillars for AI. Below, I share in detail a series of steps and successful approaches that will serve as key considerations for turning your information and data into foundational assets for the future of technology.

What is AI?

At EK, we see AI in the context of leveraging machines to imitate human behaviors and deliver organizational knowledge and information in real and actionable ways that closely align with the way we look for and process knowledge, data, and information.

What is a Knowledge Graph?

An Enterprise Knowledge Graph provides a representation of an organization’s knowledge, domain, and artifacts that is understood by both humans and machines. To this end, Knowledge Graphs serve as a foundational pillar for AI, and AI provides organizations with optimized solutions and approaches to achieve overarching business objectives, either through automation or through enhanced cognitive capabilities.

Getting Started…

Step 1: Identify Your Use Cases for Knowledge Graphs and AI?

As an enterprise considers undergoing critical transformations, it becomes evident that most of their efforts are usually competing for the same resources, priorities, and funds. Identifying a solid business case for knowledge graphs and AI efforts becomes the foundational starting point to gain support and buy-in. Effective business applications and use cases are those that are driven by strategic goals, have defined business value either for a particular function or cross-functional team, and make processes or services more efficient and intelligent for the enterprise. Prioritization and selection of use cases should be driven by the foundational value proposition of the use-case for future implementations, technical and infrastructure complexity, stakeholder interest, and availability to support implementation. The most relevant use cases for implementing knowledge graphs and AI include:

• Intuitive search using natural language;
• Discovering related content and information, structured or unstructured;
• Reliable content and data governance;
• Compliance and operational risk prediction; etc.

For more information regarding the business case for AI and knowledge graphs, you can download our whitepaper that outlines the real-world business problems that we are able to tackle more efficiently by using knowledge graph data models.

Once your most relevant business question(s) or use cases have been prioritized and selected, you are now ready to move into the selection and organization of relevant data or content sources that are pertinent to provide an answer or solution to the business case.

Step 2: Inventory and Organize Relevant Data

The majority of the content that organizations work with is unstructured in the form of emails, articles, text files, presentations, etc. Taxonomy, metadata, and data catalogs allow for effective classification and categorization of both structured and unstructured information for the purposes of findability and discoverability. Specifically, developing a business taxonomy provides structure to unstructured information and ensures that an organization can effectively capture, manage, and derive meaning from large amounts of content and information.

There are a few approaches for inventorying and organizing enterprise content and data. If you are faced with the challenging task of inventorying millions of content items, consider using tools to automate the process. A great starting place we recommend here would be to conduct user or Subject Matter Expert (SME) focused design sessions, coupled with bottom-up analysis of selected content, to determine which facets of content are important to your use case. Taxonomies and metadata that are the most intuitive and close to business process and culture tend to facilitate faster and more useful terms to structure your content. Organizing your content and data in such a way gives your organization the stepping stone towards having information in machine readable format, laying the foundation for semantic models, such as ontologies, to understand and use the organizations vocabulary, and start mapping relationships to add context and meaning to disparate data.

Step 3: Map Relationships Across Your Data

Ontologies leverage taxonomies and metadata to provide the knowledge for how relationships and connections are to be made between information and data components (entities) across multiple data sources. Ontology data models further enable us to map relationships in a single location at varying levels of detail and layers. This, in turn, sets the groundwork for more intelligent and efficient AI capabilities, such as text mining and identifying context-based recommendations. These relationship models further allow for:

• Increasing reuse of “hidden” and unknown information;
• Managing content more effectively;
• Optimizing search; and
• Creating relationships between disparate and distributed information items.

Tapping the power of ontologies to define the types of relationships and connections for your data provides the template to map your knowledge into your data and the blueprint needed to create a knowledge graph.

Step 4: Conduct a Proof of Concept – Add Knowledge to your Data Using a Graph Database

Because of their structure, knowledge graphs allow us to capture related data the way the human brain processes information through the lens of people, places, processes, and things. Knowledge graphs, backed by a graph database and a linked data store, provide the platform required for storing, reasoning, inferring, and using data with structure and context. This plays a fundamental role in providing the architecture and data models that enable machine learning (ML) and other AI capabilities such as making inferences to generate new insights and to drive more efficient and intelligent data and information management solutions.

Start small. Conduct a proof of concept or a rapid prototype in a test environment based on the use cases selected/prioritized and the dataset or content source selected. This will give you the flexibility needed to iteratively validate the ontology model against real data/content, fine tune for tagging of internal & external sources to enhance your knowledge graph, deliver a working proof of concept, and continue to demonstrate the benefits while showing progress quickly. Testing a knowledge graph model and a graph database within such a confined scope will enable your organization to gain perspective on value and complexity before investing big.

This approach will position you to adjust and incrementally add more use cases to reach a larger audience across functions. As you continue to enhance and expand your knowledge across your content and data, you are layering the flexibility to add on more advanced features and intuitive solutions such as semantic search including natural language processing (NLP), chatbots, and voice assistants getting your enterprise closer to a Google and Amazon-like experience.

Ready for AI? Automate, Optimize, and Scale.

Core AI features, such as ML, NLP, predictive analytics, inference, etc., lend themselves to robust information and data management capabilities. There is a mutual relationship between having quality content/data and AI. The cleaner and more optimized that our data, is the easier it is for AI to leverage that data and, in turn, help the organization get the most value out of it. Within the context of information and data management, AI provides the organization with the most efficient and intelligent business applications and values that include:

• Semantic search that provides flexible and faster access to your data through the ability to use natural language to query massive amounts of both unstructured and structured content. Leveraging auto-tagging, categorization, and clustering capabilities further enables continuous enhancement and governance of taxonomies/ontologies and knowledge graphs.
• Discover hidden facts and relationships based on patterns and inferences that allow for large scale analysis and identification of related topics and things.
• Optimize data management and governance through machine-trained workflows, data quality checks, security, and tracking.

Organizations that approach large initiatives toward AI with small (one or two) use cases, and iteratively prototype to make adjustments, tend to deliver value incrementally and continue to garner support throughout. The components that go into achieving this organizational maturity also require sustainable efficiency and show continuous value to scale. As your organization is looking to invest in a new and robust set of tools, the most fundamental evaluation question now becomes ensuring the tool will be able to make extensive use of AI.

If you are exploring pragmatic ways to benefit from knowledge graphs and AI within your organization, we can help you bring proven experience and tested approaches to realize and embrace their values.

The post How to Build a Knowledge Graph in Four Steps: The Roadmap From Metadata to AI appeared first on Enterprise Knowledge.

An organization that successfully links their structured and unstructured information through ontologies can see meaningful improvements in the findability and discoverability of their information. An ontology will create connection between all information, meaning that information becomes a web that may be traversed by your end users to better find and use the information they seek. This leads to greater productivity, collaboration, and overall satisfaction.

The easiest way to understand how ontologies can help link structured and unstructured information is through examples. This blog shares two different examples showing how an ontology can associate these two different types of content.

• Merging customer information with customer metrics
• Mining product information

Merging Customer Information with Customer Metrics

The Problem

A large financial services firm that worked primarily with corporate clients needed to integrate customer metrics into their customer intelligence portal. The portal was a central location for news and information about their customers to improve sales and account management. The content included formal customer documents (contracts, invoices and license agreements), news, and call notes. A separate data warehouse team had a database of key customer metrics. The firm wanted a way to show key metrics about a customer while people were reading news, documents, or call notes about that customer.

The Ontology Solution

The firm used their customer database to seed an ontology that included a customer entity type. Each customer entity was assigned attributes like industry, status, and customer number using information from the customer database. This list of customers and their attributes was loaded into an ontology management and entity extraction tool, like PoolParty. The entity extraction tool was run against the content repository to identify references to customers in the content. Once the entities were identified and tagged, the structured and unstructured information could be linked.

The portal content was organized by customer, industry, and topic. The customer and industry information comes from the ontology. When users look a document that mentions one of their customers, they also see metrics about the customer and their industry.

Mining Product Information

The Problem

A manufacturing company was looking to find patterns about product defects in order to improve the reliability of the products they manufacture. They had information in a variety of formats:

• A database of information about product defects and returns;
• Defect reports with problem descriptions; and
• User comments from their website.

The manufacturer needed a way to mine all of this information to identify patterns that would allow them to improve the way they manufactured their products.

The Ontology Solution

An ontology was the best way to link this content together for analysis. The manufacturer created an ontology that included the following entities:

• Products
• Parts
• Defects

Products store the name of the product, its SKU, and the unique identifier that can be used to link it back to the product database that contains the structured information. Parts include the name of the part, any similar names, and manufacturing information. The defects are a list of common problems that will grow as more information is captured.

The products were loaded into the ontology management tool with the SKU and product identifier so that they could be linked back to the database. We entered part information and common defects in the parts and class entities. Entity extraction was run against the unstructured content (defect reports, surveys, and social media). This allowed us to identify new defects and parts and associate them with the products aligned with the content.

The manufacturer was able to use SPARQL (an ontology query language similar to SQL) to see relationships between defects, parts, and products that were not easily visible before. Using SPARQL queries, the manufacturer was able to see that 2-3 parts that were used across the product line accounted for most of the defect descriptions. This information would not have been available without associating problem descriptions with defect and return information from their product database.

Conclusion

As you can see from these two examples, an ontology is a great way to link structured and unstructured information. Ontology products like PoolParty automate much of the process and make it an affordable and scalable solution. The next time you revisit your organization’s Knowledge Management capabilities do not limit yourself to documents and web pages. Use ontologies to integrate databases and reports so that you have a true Knowledge and Information Management (KIM) solution.

EK can help make the integration of your structured and unstructured information seamless. For more information contact us at info@enterprise-knowledge.com.

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Ontologies and semantic technologies are becoming popular again. They were a hot topic in the early 2000s, but the tools needed to implement these concepts were not yet sufficiently mature. Ontology and semantic technologies have now matured to the point where they are widely available and reasonably priced. This has potentially vast benefits for organizations that are seeking to improve the use and reuse of their structured and unstructured information and want to maximize findability and discoverability. If you are like many of our clients, you are asking “What is an ontology and why do I want one?

Enterprise Knowledge defines an ontology as “a defined model that organizes structured and unstructured information through entities, their properties, and the way they relate to one another.” Many of you are familiar with terms like taxonomies and metadata. Think of an ontology as another way to classify content (like a taxonomy) that allows you to relate content based on the information in it as opposed to a term describing it. For example, you could create an ontology about your employees and consultants (see the image below.)

The example above shows an ontology of a company, its employees, consultants, and the projects they are working on. In this example, Kat Thomas is a consultant who is working with Bob Jones on a Sales Process Redesign project. Kat works for Consult, Inc and Bob reports to Alice Reddy. We can infer a lot of information through this ontology. Since the Sales Process Redesign is about sales we can infer that Kat Thomas and Bob Jones have expertise in sales. Consult, Inc must provide expertise in this area as well. We also know that Alice Reddy is likely responsible for some aspect of sales at Widgets, Inc because her direct report is working on the Sales Process Redesign project.

There are many reasons why this is valuable for your organization. Ontologies can allow your organization to:

• Manage content more effectively;
• Maximize findability and discoverability of information;
• Increase the reuse of “hidden” and unknown information; and
• Elevate SEO on external search engines.

Manage Content More Effectively

Content management is a time consuming process. It is one thing to manage metadata on a couple thousand pieces of content. What if you are managing hundreds of thousands of pieces of content? Ontologies are focused on relationships between entities. To extend the example above, I can identify content authored by Kat Thomas or Bob Jones and associate it with Sales information because Sales Process Redesign project is about sales. I no longer need to manually tag this content as I can rely on the entities in the content and the information I have about them.

Improved Findability

Ontologies give you new ways to find and discover content. Ontologies can power faceted search or allow people to browse through related content based on the people, places, and things that are mentioned in the text. I can see all of the deliverables created by Consultant, Inc and see all of the deliverables they have provided to my company. I can also see who works for them and who they have worked with at my company. I am navigating based on things that I understand to find relevant content and information.

Ontologies also allow for more accuracy in the way content is classified as opposed to classic metadata. For example, a piece of content on our intranet quotes Kat Thomas who at the time was an outside consultant. If we used the metadata approach her content might be tagged as consultant information. A year later Kat takes a job as head of sales. Using an ontology her recommendations would show up as recommendations from the head of sales. If I was just relying on metadata, I would have to go back and update my content to reflect her new position.

Greater Content Reuse

Publishers use ontologies to group content in new ways. The best example of this is the New York Times Topic Pages. All of the content related to famous people or topics are grouped on a single page. These articles appeared in the paper, but are now reused as a single place to learn all about a specific topic.

Content reuse is not limited to content on your site. Because ontologies are standards based, other sites can use your content to augment their content. As a result, your content appears in more places and is more likely to be seen by others.

Improved SEO

Ontologies are machine readable. This means that search engines are able to understand the content. As a result, content based on ontologies rates higher in Google and other search engines. Wordlift is a great example of an ontology plug-in for WordPress that promises to improve SEO.

I hope you have a better understanding of what an ontology is and why you might want one. If you are interested in implementing an ontology for your organization, take a look at my two part blog series on Ontology Design Best Practices. Part I describes best practices for any ontology design project. Part II provides specific recommendations for the design of the ontology.

The post What is an Ontology and Why Do I Want One? appeared first on Enterprise Knowledge.

As a reminder, our most successful ontology projects deliver business users new and meaningful ways to see relationships between content and information. The complexity of the ontology is hidden from the business users who are able to discover related content without the need for formal or rigid navigation between content. The content owners/administrators are able to manage the relationships between content at a much more granular level with minimal additional effort. Often, these changes give our customers new ways to consolidate and present content and information both internal and external to their organization. Most importantly the value of these changes is visible to the project stakeholders and sponsors.

The design recommendations in this post assume that the project recommendations in Part I have already been implemented. These recommendations are focused on maximizing business value and usability.

Use Non-Technical Terms

Most ontology designers use terms like graphs, classes, nodes, and edges. Though these are accurate descriptions of what we are designing, they make ontologies seem unapproachable and difficult. Make a point of replacing these highly technical terms with terms that are more recognizable to your stakeholders.

It is important that you communicate regularly with your stakeholders. Using non-technical terms will make the project feel more approachable and more business focused. As a result, it will be easier to get feedback from your stakeholders on their wants and needs.

Identify Your Domain 

The first step in creating an ontology is to identify the domain to which it belongs. By this, I mean the category or topical area the ontology describes. This cannot be done in a vacuum. Review your content and then come up with 3-4 terms that describe your domain. Use these terms to search for public ontologies with similar domains. To find similar domains that are publicly available, look in places like the following: 

• http://wiki.DBPedia.org
• http://protegewiki.stanford.edu/wiki/Protege_Ontology_Library
• http://semanticweb.org/wiki/Ontology.html
• https://www.w3.org/standards/semanticweb/ontology
• http://ontologydesignpatterns.org
• http://schema.org/  

Evaluate the content and entities in these public domains to see which one is most closely aligned with the content and entities in your domain. Use the public domain as the starting point for the rest of your work. Not only will this give you a headstart on the work, it will also ensure that you are following a standard that others use so that you can more easily integrate your domain with others.

Prioritize the Entities to Model 

Each ontology has a list of classes (think entities or types of things) that need to be modeled. For example, an ontology about people in an organization would likely include the following entities:

It would be very easy to create a huge list of entities for any domain that you work with. Many people begin by creating an exhaustive list to make sure they capture everything. This is a common mistake for people creating their first ontology. Start with a smaller list of entities that are easily recognizable and model those first. Prioritize the entities to develop through the use cases and goals you defined at the beginning of the project. This approach will save time and allow you to show value sooner. It will also result in a design that meets the business needs without introducing undue complexity and clutter.

Minimize Characteristics and Look for Patterns

The next step is to model the characteristics of these entities. If you are following a standard, some of this work may already be done for you. Some standards allow for a great deal of flexibility to define these characteristics. In these cases, you will need to make decisions about how much information you want to manage. Minimize the number of characteristics you capture though the use cases and goals for the project. Less is always better as you start out. Also, look for repeatable patterns that can be applied to as many entities as possible. Consistency among entities simplifies implementation and simplifies the way content can be queried using the SQL-like query language (SparQL).

Prioritize Relationships

Identifying the ways in which entities are related is one of the most powerful features of semantic ontologies. It is, unfortunately, also one of the easiest ways to create an ontology that is overly complex. Review the goals for your ontology. Prioritize the types of relationships to those that directly support the goals of your taxonomy. Try to limit these relationship types to less than 5 if possible. As with all of these things, it is better to start small and grow than to try and be comprehensive with your first ontology.

Validate your Design

Ontologies can be confusing to people who have never worked with them before. This does not mean that you should develop the ontology in a vacuum or without validation. Make sure you have a visual representation of your ontology and share it with the project stakeholders at every stopping point in the project. Show how the ontology relates to their content and information and how it will help them meet the objectives defined at the beginning of the project.

This feedback loop accomplishes three things:

• Your stakeholders see progress and understand why they are developing an ontology.
• The stakeholders may spot things that are missing, and
• You can validate that the design is easy to understand.

It is important that this is done before the ontology and its related features are implemented so that you do not go down the wrong path.

Ontologies offer a powerful way to manage and present content. Technology has advanced to the point where the ontologies and the semantic web are now a reality. An effective ontology can:

• Provide new ways to navigate and find content,
• Expose relationships between content that were once not visible, and
• Provide a seamless view of content across organizations.

I encourage all of you to consider how an ontology can improve your content or knowledge management sites. We are happy to help if you need ontology consultants to assist in the design of your ontology. Contact us at info@enterprise-knowledge.com.

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