Enterprise Knowledge is concluding the first round of our new webinar series, The Semantic Exchange. In this webinar series, we follow a Q&A style to provide participants an opportunity to engage with our semantic design experts on a variety of topics about which they have written. This webinar is designed for a variety of audiences, ranging from those working in the semantic space as taxonomists or ontologists, to folks who are just starting to learn about structured data and content, and how they may fit into broader initiatives around artificial intelligence or knowledge graphs.

This 30-minute session invites you to engage with James Egan’s case study, Humanitarian Foundation – SemanticRAG POC. Come ready to hear and ask about:

• How various types of organizations can leverage standards-based semantic graph technologies;
• How can leveraging semantics addresses data integration challenges; and
• What value semantics can provide to an organization’s overall data ecosystem.

This webinar will take place on Wednesday July 23rd, from 2:00 – 2:30PM EDT. Can’t make it? The session will also be recorded and published to registered attendees. View the recording here!

The post The Semantic Exchange: Humanitarian Foundation – SemanticRAG POC appeared first on Enterprise Knowledge.

Benefits of Taxonomies and Ontologies

Taxonomies (hierarchical or faceted structured controlled vocabularies of concepts) primarily enhance search and retrieval of content, but they have related benefits. Taxonomy uses and benefits include: 

• Tagging: to index content consistently so that retrieval is comprehensive and accurate
• Normalization: to bring together different names, localizations, and languages for concepts
• Standard search: to enable users to find content about something (whereby the user’s search string matches taxonomy concepts)
• Topic browse: to enable users to explore subjects arranged in a hierarchy and then get content on the selected subject
• Faceted (filtering/refining) search: to enable users to find content that matches a combination of basic criteria
• Discovery: to enable users find additional, related content tagged with the same concepts; to explore broader, narrower, and (sometimes) related taxonomy topics
• Content curation: to create feeds or alerts based on pre-set search terms
• Metadata management: to support identification, comparison, analysis, etc., in addition to content retrieval

Ontologies (semantic models comprising the types/classes, semantic relationships, and attributes of entities) were originally for describing a domain while also supporting inference for learning more about the domain. However, when entities from a taxonomy are combined with an ontology, benefits and capabilities include:

• Modeling complex interrelationships (e.g. in product approval or supply chain processes) while also connecting to content
• Executing complex multi-part search queries
• Exploring explicit relationships between concepts, not just broader, narrower, or related
• Searching across datasets, not just searching for content
• Searching on more specific criteria that vary based on category (class)
• Visualizing concepts and semantic relationships
• Reasoning based on inferences
• Creating knowledge graphs (incorporating instance data), upon which additional knowledge applications can be built

“Content” refers to files, documents, images, intranet pages, spreadsheets, etc. “Data” refers to such things as the information within database records and the cells within tables or spreadsheets. Sometimes people are looking for content, sometimes they are looking for data, and sometimes they are looking for both. Taxonomies focus on connecting users to content, and ontologies focus on data, so a combination of taxonomies and ontologies can connect users to both content and data, in addition to connecting the content and data together. 

Taxonomies and Ontologies Combined

Taxonomies and ontologies have different origins (library/information science vs. computer/data science), and thus usually different experts, but these two knowledge organization systems have converged greatly in the past decade. There are two primary reasons for this convergence:

• The adoption of shared Semantic Web (World Wide Web Consortium) standards, whereby both taxonomies and ontologies are built on the same data model, RDF (Resource Description Framework), and other models and standards based on RDF. Thus they can be built in the same tools and connect to each other seamlessly.
• The increased business needs to manage and extract knowledge from growing volumes of content and data together in sophisticated ways  as well as the growing demand for data and information, not just for documents and pages

As mentioned above, there are different definitions for ontologies, and a leading difference concerns whether individual entities are included within the scope of “ontology.” An ontology is either:

1. 1) A model of a knowledge domain, comprising classes, semantic relationships, and attributes (along with prescribed rules or constraints on each of these components, etc.), or

• 2) A model of a knowledge domain, comprising classes, semantic relationships, and attributes, plus all the individual members of the classes, which are described in controlled vocabularies, including taxonomies

The following pair of diagrams listing different controlled vocabulary and knowledge organization systems illustrate the views of these two different definitions of ontologies. 

• 1) Ontology as a model of a knowledge domain that serves as a semantic layer connected to various controlled vocabularies:
  
  

1. 2) Ontology as the most semantically rich type of knowledge organization system, which includes all the features/components of taxonomies, thesauri, and named entity controlled vocabularies plus more semantics:

Depending on how you define ontology, above, a taxonomy can then either

1. be enhanced to include an ontology as an additional semantic layer (definition #1), or
2. be used as an important component of an ontology (definition #2

Ontologies alone may have taxonomic features of deep hierarchies of classes and subclasses, but without a taxonomy or thesaurus built on the SKOS (Simple Knowledge Organization System) data model, the full range of functionality of alternative labels, labels in other languages, multiple definitions and types of notes, etc. are not supported. Taxonomies provide a linguistic aspect that ontologies alone lack. 

Ontologies alone would support modeling, exploring, and visualizing entities and their relationships, which may be based on their properties. Ontologies may also support inference reasoning. However, functions involving semantic search, which brings together synonyms and disambiguating homonyms, etc. require taxonomies, thesauri, or other controlled vocabularies. 

Creating an Ontology Based on Taxonomies

Regardless of which of the two definitions of ontology you prefer, if you already have a taxonomy, which is often the case, you can extend it to become or or add an ontology and then reap the additional benefits of the combined knowledge organization system. If you have multiple taxonomies and other controlled vocabularies, an ontology can link them together. 

Whether you are building a taxonomy, ontology, knowledge graph, or a broader digital transformation for knowledge management, there should be a combination of top down and bottom up approaches to the process. The top-down methods focus on obtaining input from stakeholders, whereas the bottom-up methods focus on analysis of content and data. 

The basic approach to building an ontology, especially a business or enterprise ontology, is to identify groups of things (or “business objects”), which become classes in an ontology, identify relationships between pairs of classes, and identify important characteristics (or attributes) of members of a class. The top-down approach to this task involves interviewing stakeholders and conducting brainstorming sessions and focus group sessions to identify these classes, relationships, and attributes. The bottom-up approach to ontology creation often involves looking at spreadsheets and tables of critical data pertaining to different business objects. 

A quicker bottom-up approach to creating ontologies is to look at the taxonomies and controlled vocabularies you already have. Each taxonomy hierarchy, controlled vocabulary, term set, facet, or what is designated as a “concept scheme” in the SKOS model can be considered to be a class in an ontology. Additional classes or subclasses might get added, and some term lists might not be needed in an ontology, but often concept schemes can serve as the basis of classes, one-to-one.

Facets in a faceted taxonomy enable browsing or limiting searches for content items by certain aspects. However, content needs to be limited to that of a similar kind that shares the same facets, such as all product pages, all reports, all employee profiles, or all media files. If we can convert the facets to ontology classes, create new semantic relationships between them, and tag all content, a search application is no longer limited to a certain kind of content or asset. Rather, conditional queries in the same application/user interface can be targeted at any kind of content. 

Example: Converting Facets to Classes to Build an Ontology

Consider an example for an organization’s internal knowledge base. There may exist multiple repositories of content and data, each with its own faceted taxonomy and its own user interface.  

• Reports could be searched using a Reports faceted taxonomy, which has the facets Report Type, Subject, Author Name, and Division.
• Employees as experts could be searched using a People faceted taxonomy, which has the facets Name, Job Title, Location, Division, Skills, and Subject Expertise.
• Media files could be searched using a Digital Asset Management faceted taxonomy, which has the facets Subject, Location, Event, Person Depicted, and Creator

We could create classes to reflect the aggregation of all of these facets.

• Division
• Employee name (which also includes report authors and media asset creators)
• Event
• File type (with subclasses for Document type and Asset type)
• Job role (including titles)
• Location
• Skill
• Subject (including expertise areas

Then we could consider the relationships or links between the classes, and create verb-based semantic relationships. Any class that is a target/object of a relationship can be a target of a search query. The following are just some examples, but not a complete list with all reciprocal relationships.

Employee knows Subject
Employee created File Type
Employee possesses Skill
Employee basedIn Location
Employee belongsTo Division

File Type hasTopic Subject
File Type createdBy Employee
File Type belongsTo Division

Subject knownBy Employee
Subject topicOf Division
Subject topicOf File Type
Subject topicOf Event

Event basedIn Location
Event belongsTo Division
Event hasTopic Subject

Finally, you should consider what additional data is of importance for the entities in each class, such as contact information for Employees and dates of publication for files and for the occurrence of Events. These would normally not exist in a taxonomy, but should be added to the ontology to support the exploration of more kinds of data.

Conclusions

Combining a taxonomy with an ontology provides many benefits and capabilities which a taxonomy alone or an ontology alone (as merely a semantic model) cannot provide. 

Building an ontology based on one or more existing taxonomies is an efficient and very suitable method of bottom-up development. The existing taxonomies and controlled vocabularies provide a basis for knowledge modeling. Furthermore, by leveraging an existing taxonomy that has already been tagged to content, certain benefits of the ontology will already be in place. 

Managing the taxonomy plus the ontology as a semantic layer also has benefits. A taxonomy plus ontology is more flexible and adaptable than a single large ontology, since the taxonomy changes more frequently than does the ontology. Also, more taxonomies and controlled vocabularies can easily be added in the future. There are also several software options for combined taxonomy-ontology creation and management. These applications are based on RDF, including SKOS for taxonomies and RDF-S and OWL for ontologies. This facilitates the technical aspects of extending a taxonomy to become an ontology. 

Although extending taxonomies to become ontologies is easier than creating ontologies from scratch, it still requires ontology design expertise. For assistance in extending your taxonomies into an ontology, contact us to get started.

The post Extending Taxonomies to Ontologies appeared first on Enterprise Knowledge.

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.

The Solution

EK facilitated a series of workshops, interviews, and focus groups with subject matter experts, content creators, and technical partners to define the current and target state of the retailer’s Content Management maturity using EK’s proprietary 50-factor Content Management Benchmark. EK then partnered with the learning team to define a fully customized, iterative, task-based content management strategy, implementation roadmap, and KM Platform design to help the learning team improve their Content Management maturity over a multi-year period using a phased approach. The KM Platform design featured recommendations to leverage new and existing technologies, including a metadata management hub, taxonomy management system, knowledge graph, and search engine. These technical recommendations have resulted in the creation of a digital library that is currently helping the retailer to more effectively and efficiently manage the sheer scale of content in their learning ecosystem, increase the organization’s speed in creating learning content, and decrease the time it takes associates to find and discover lessons.

The EK Difference

EK leveraged its unique, 50-factor benchmark to develop a comprehensive analysis of the retailer’s Content Management maturity and define a future state for the retailer to work towards. EK also utilized its thorough understanding of the client’s culture and processes to produce a Content Management Strategy and Roadmap for implementation using an iterative, Agile approach, and leveraged in-house technical expertise to recommend a unique set of technological solutions aimed at alleviating the inefficiencies the client was experiencing. 

The EK team was uniquely positioned to deliver expertise in learning solutions because of our extensive experience delivering KM training sessions, workshops, and materials to a variety of clients as well as our in-house team of instructional designers and learning technology experts. We have conducted dozens of similar efforts with organizations like this one, and the EK team was equipped to deliver both hands-on training and in-depth technical support. This enabled us to holistically understand this organization’s needs and develop a strategy to help the learning team find the learning content and training materials they needed to support the organization’s employees. The EK team also demonstrated effective knowledge transfer techniques that the learning team could then utilize within their own training efforts. 

EK is also skilled in bridging the gaps between strategy, design, and implementation, as this effort fused personal interaction with stakeholders to develop a content management strategy with targeted, technical recommendations to plan and implement a KM Platform design. Rather than evaluating just the current state of the organization and developing a strategy to address current challenges, the EK team worked with stakeholders to determine the organization’s long-term goals and recommended various technologies that would help the organization update and maintain its learning content in the future.   

The Results

The retailer and EK’s long-standing partnership allowed them to successfully design, develop, and deploy three major releases for the digital library into the retailer’s production environment, resulting in increased time savings and reduced costs related to developing learning content, as well as a workforce with the necessary skills and expertise to do their jobs effectively and adapt to a rapidly changing environment. The retailer was able to gain an improved visibility into each associate’s capabilities and an enhanced ability to identify gaps in their learning content, resulting in more targeted learning experiences to upskill employees and guide their professional development. Additionally, the renewed consistency, reuse, and findability of learning materials allowed the retailer to mitigate any repercussions associated with on-site store safety, diversity and inclusion, and employee and customer health and wellbeing.

The post Content Management Strategy for an International Retailer appeared first on Enterprise Knowledge.

The Solution

EK performed initial user and system research and analysis to identify issues and recommend solutions to improve overall UX. As part of this research, EK facilitated cross-departmental focus groups and workshops with stakeholders and SMEs, in addition to conducting a current and target state analysis. During these activities, EK identified strengths and challenges in multiple aspects of the content lifecycle, from authoring and publishing to end-user content engagement. The challenges identified in the analysis provided an opportunity to identify and prioritize relevant use cases, which helped shape the Agile product roadmap and EK’s tool recommendations. Additionally, the analysis enabled EK to identify user goals and evaluation criteria that could be measured to test solution effectiveness. The EK team also collaborated with taxonomy SMEs to review and improve the existing content metadata, providing the foundations for more granular content tagging. EK then developed an Agile product roadmap that incorporated our UX and system recommendations with iterative milestones and collaborated with the client company on implementing the multi-workstream roadmap to ensure the client company met its goals and improved solution effectiveness.

EK’s user and system research found a cycle of challenges that reduced user confidence in the client’s documentation hub.

The EK Difference

The EK team leveraged our experience with taxonomy design, ontology design, UX best practices, and enterprise search to design an Agile roadmap to achieve expanded search capabilities, governance workflows, and more personalized content experiences. Our certified taxonomists and ontologists collaborated with client company SMEs to capture and translate existing metadata and authoring processes into an expanded taxonomy that the CCMS could leverage for granular content tagging. EK’s expert taxonomy and ontology designers discerned metadata pain points to design and deliver data models that would support the client company’s current and future advanced user-driven use cases. EK also leveraged our knowledge graph experience to implement and query the data models created by the ontology designers to support the prioritized use cases surfaced from our initial research and analysis. The EK team leveraged our advanced content management, search, and knowledge management solution architecture experience to design a new system architecture that enables dynamic content assembly, improves search experience, provides personalized content to system users, and decreases manual authoring time spent creating the content.

The Results

EK delivered a state-of-the-art solution architecture that enables increased granular tagging of componentized content for improved content and metadata management, content reuse across multiple end-user content experiences, and a streamlined content authoring process. The focus groups that EK conducted enabled the EK team to include and inform multiple departments across the organization, facilitating future cross-department collaboration. After applying the content changes, EK worked with the client to reevaluate user feedback on the site and found that

• Users were more satisfied with the content experience,
• Users were much more accurate with their answers, and
• Users were able to find answers in almost half the time.

The bench-marked structured Agile roadmap will enable the team to socialize the architecture and governance changes within the organization, communicating and promoting momentum and buy-in for the architecture and governance implementation.

The post User-Centric Content Engineering to Improve Customer Experience appeared first on Enterprise Knowledge.

The Solution

EK collaborated with the financial provider to better leverage a componentized content management system (CCMS), enabling the organization to consistently model, create, and reuse content previously curated and authored by siloed business units. The “componentized” feature was critical to allow the organization to produce reusable product documentation segments. EK integrated an auto-tagging system to more accurately and efficiently apply consistent vocabularies to each of these segments so that the organization could assemble and reassemble content components, aligning them with customer profiles to create personalized customer experiences. Once product documentation content was more meaningfully structured and tagged with consistent vocabularies, EK developed a series of APIs to deliver the content for display in various omnichannel front-end delivery platforms and experiences.

Additionally, EK worked with organizational subject matter experts to design and implement a taxonomy that enables authors to associate content segments with financial topics, solutions, and user-facing views. These associations help drive the personalization of content for each user and organization. On top of the CCMS, EK architected and implemented a knowledge graph to store the content segments and their metadata for quick reference when building content and front-end views. Segments are stored separately and pulled together from the graph to create a seamless search and documentation experience for users.

The EK Difference

The EK team leveraged our experience with taxonomy design, content management, ontology design, knowledge graphs, and enterprise search to strategize and implement the CCMS solution for the organization. Our taxonomists and ontologists collaborated with the organization’s subject matter experts to capture and translate existing metadata and authoring processes into taxonomies and content models that could be leveraged by the CCMS and knowledge graph. Our technical expertise guided the organization to build a flexible, knowledge graph foundation that could be leveraged through a bespoke API layer to power their vision of content curation, delivery, and search through their user-facing platforms.

The Results

The combination of a CCMS platform and a knowledge graph allows for dynamic assembly of content components and data elements to produce a flexible, adaptive, and customized client experience for the financial solutions provider. The new solution connects clients to the content and data they need to perform their work in a vastly more efficient manner that saves time for both the financial solutions provider as well as their clients. The CCMS platform is providing the organization with the ability to create individual content segments, tag them with a consistent taxonomy, and produce personalized views for end-users. With personalized content and connected data, users are provided only the documentation relevant to the version and tier of service they have purchased. Additionally, we can ensure that product information is described with a consistent vocabulary across all documentation. This removes the previous need to comb through irrelevant and inconsistent information to find the relevant documentation. Leveraging a knowledge graph allows us to connect content with relevant data elements to produce richer and more detailed documentation.

Improving Product Documentation With a CCMS and a Knowledge GraphDownload

The post Improving Product Documentation With a CCMS and a Knowledge Graph appeared first on Enterprise Knowledge.

The panel will focus on Knowledge Management, current techniques and approaches, how organizations are applying KM to improve organizational efficiencies, and the future of the discipline.

Registration to the event is open, and you can register at https://www.eventbrite.com/e/rocky-mountain-sla-8th-annual-mini-conference-tickets-169027747543.

The post EK at the Rocky Mountain SLA Virtual Conference appeared first on Enterprise Knowledge.

This is the fourth consecutive year that EK has achieved a place on the Inc. 5000. Of the tens of thousands of companies that have applied to the Inc. 5000 over the years, only a fraction have made the list more than once, let alone four times in a row. In addition to being listed as one of the country’s fastest growing companies, EK was also included again this year on Inc’s list of the best workplaces in the country.

“Our fourth consecutive year on this list is a great achievement for EK, and one that belongs to each member of our growing team” said Zach Wahl, CEO of EK. “What is most important to me is that we’ve successfully sustained our culture of kindness and collaboration as we’ve grown.” 

Joe Hilger, EK COO added, “Our growth means greater depth and breadth of capabilities to serve our clients, continuing to work at the intersection of Knowledge Management, Advanced Technologies, and Enterprise Artificial Intelligence.”

“The 2021 Inc. 5000 list feels like one of the most important rosters of companies ever compiled,” says Scott Omelianuk, editor-in-chief of Inc. “Building one of the fastest-growing companies in America in any year is a remarkable achievement. Building one in the crisis we’ve lived through is just plain amazing. This kind of accomplishment comes with hard work, smart pivots, great leadership, and the help of a whole lot of people.”

About Enterprise Knowledge

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

Our core services include strategy, design, and development of Knowledge and Information Management systems, with proven approaches for Taxonomy and Ontology Design, Project Strategy and Road Mapping, Brand and Content Strategy, Change Management and Communication, and Agile Transformation and Facilitation. At the heart of these services, we always focus on working alongside our clients to understand their needs, ensuring we can provide practical and achievable solutions on an iterative, ongoing basis.

More about Inc. and the Inc. 5000 Methodology

Companies on the 2021 Inc. 5000 are ranked according to percentage revenue growth from 2017 to 2020. To qualify, companies must have been founded and generating revenue by March 31, 2017. They must be U.S.-based, privately held, for-profit, and independent—not subsidiaries or divisions of other companies—as of December 31, 2020. (Since then, some on the list may have gone public or been acquired.) The minimum revenue required for 2017 is $100,000; the minimum for 2020 is $2 million. As always, Inc. reserves the right to decline applicants for subjective reasons. Growth rates used to determine company rankings were calculated to three decimal places. There was one tie on this year’s Inc. 5000.  Companies on the Inc. 500 are featured in Inc.’s September issue. They represent the top tier of the Inc. 5000, which can be found at http://www.inc.com/inc5000.

About Inc. Media

The world’s most trusted business-media brand, Inc. offers entrepreneurs the knowledge, tools, connections, and community to build great companies. Its award-winning multiplatform content reaches more than 50 million people each month across a variety of channels including web sites, newsletters, social media, podcasts, and print. Its prestigious Inc. 5000 list, produced every year since 1982, analyzes company data to recognize the fastest-growing privately held businesses in the United States. The global recognition that comes with inclusion in the 5000 gives the founders of the best businesses an opportunity to engage with an exclusive community of their peers, and the credibility that helps them drive sales and recruit talent. The associated Inc. 5000 Vision Conference is part of a highly acclaimed portfolio of bespoke events produced by Inc. For more information, visit www.inc.com.

The post EK Included on Inc. 5000 for Fourth Consecutive Year appeared first on Enterprise Knowledge.

Below I briefly highlight some of the new features and how they impact the knowledge management business.

1. Implicit Content Relationships (a.k.a. Shadow Concept Extraction)

One of the main goals that PoolParty strives for is to help organizations relate their content objects, thus building the organizational knowledge. In past versions, PoolParty achieved this by relating content where concepts were mentioned explicitly within the content items. In other words, through its complex algorithms and Natural Language Processing capabilities, PoolParty extracted the main concepts that it found in the content.

With 6.0, however, PoolParty takes this capability to the next level. In addition to being able to learn the organization’s knowledge domain and suggest the most relevant tags for content items in an auto-classification style, PoolParty now automatically identifies topics and concepts that are closely related to a piece of content, even if the topic or concept is not explicitly mentioned in the content. In other words, the system can now recommend semantically related content based on indirect, implicit relationships that it identifies based on its own content analysis.

Below is a screenshot of the view a Knowledge Manager would see during the fine-tuning process to identify concepts and shadow concepts. This is not the front end user interface, but it does provide a quick and easy way to test content excerpts against the analysis engine:

In the screenshot above, the items in grey denote the identified shadow concepts.

In more technical terms, the shadow concept functionality allows you to see how a document relates to a concept in your knowledge graph even if the document does not mention that concept explicitly. Additionally, PoolParty allows you to see why that relationship was suggested by providing the found terms and their relationship to the shadow concept.

Why It Matters

The clearest benefit of this technology is its ability to help your users discover relationships between knowledge objects they didn’t know existed. In the background and based on your organization’s content, PoolParty now builds its own network of related terms in addition to your organization’s explicit ontology or thesaurus. This allows PoolParty to recommend related items based on its comprehensive organizational knowledge even if the user did not specifically know of the relationship (or that the recommended concept even existed). Think of the Amazon recommender system, but for your organization and based on your own content.

2. Centralized Interface for All Your PoolParty Data Integrations (a.k.a. Semantic Middleware Configurator)

PoolParty was already well-connected. Its latest version provides additional integration options with Linked Data Sources, search and graph search engines, graph databases (i.e. triple stores), and visualization engines. To aid in managing all these connections and integrations, PoolParty introduces the Semantic Middleware Configurator functionality which provides a quick view and easy management options for all integration channels to systems integrated with PoolParty. Knowledge managers and engineers will find this new functionality extremely helpful. The ability to quickly see all connections, their status, and settings at a glance is a significant improvement on clarity and efficiency.

PoolParty’s new Semantic Middleware Configurator interface showing all connections and integrations in one location.

Why It Matters

With the growing number of data sources and tools that allow organizations to interconnect, visualize, and analyze their knowledge, the ability to manage all these connections in one place becomes critical. With this in mind, the new Semantic Middleware Integrator feature is a solid contender to become the enterprise semantic data integration center.

3. Visualization

With larger thesauri, and especially ontologies, it quickly becomes difficult to manage relationships and knowledge models and even more so to communicate them to stakeholders. The 6.0 release of PoolParty tackles this challenge in two ways: improved native visualization; and integration with visualization engines like webVOWL.

The enhancements to PoolParty’s native visualization capability help knowledge managers view relationships between types of concepts (i.e. classes), as well as the custom properties, or attributes associated to a knowledge object.

PoolParty native visualization of concepts and relationships.

With the introduction of integration capabilities to visualization tools like webVOWL, knowledge managers can now produce a visual model of the full ontology to help them in their ontology design efforts as well as communicate the model with other stakeholders.

PoolParty ontology visualized through webVOWL integration.

Why It Matters

One of the issues inhibiting the proliferation of business ontologies is their perceived complexity. With scale, the challenge of designing, collaborating, and communicating the knowledge model, i.e. the ontology, grows. However, the ability to visually represent concepts, their attributes, and how they are connected significantly simplifies these tasks. As a result, we are looking at shorter design and onboarding time, thus reduced costs.

4. Additional Linked Data Sources

We all try to avoid reinventing the wheel where possible. Or in KM terms, why recreate knowledge if it already exists? With that in mind, even in past versions, PoolParty has provided automatic integration with public sources of information like definitions and alternative names of terms, topics, etc. PoolParty 6.0 includes additional sources of shared data that organizations can utilize to quickly and easily enrich their internal content as well as bring in externally managed and updated content – without additional content management load.

With PoolParty 6.0, these sources of Linked Open Data now include additions like:

• PermID – A Thomson Reuters Permanent Identifiers source that provides company information, mainly in the finance industry;
• Getty Vocabularies – A linked open data source of “structured terminology for art and other material culture, archival materials, visual surrogates, and bibliographic materials”;
• DBPedia in Dutch and Russian – PoolParty continues to add support for new language versions of one of the most popular and active linked data sources.

Why It Matters

Linked Open Data sources are gaining popularity and can be utilized to enhance your content with minimal additional effort. Leveraging public shared data allows you you expand your thesauri by adding new terms, definitions, and synonyms, thus increasing the quality and richness of your knowledge. This in turn helps make your enterprise search results and relationships more accurate and relevant.

Closing Thoughts

As a supervised learning system, PoolParty 6.0 takes the next step to integrating siloed structured and unstructured content, identifying relationships, and augmenting organizational knowledge. The result is more relevant and findable content for your users.

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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.

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