Use Natural Language to Enhance Knowledge Management

How advances in Natural Language Processing can be used to improve and augment knowledge management solutions to provide better customer service and more efficient workflows

Using NLP with KM
Using NLP with KM

What is Knowledge Management?

Knowledge Management (KM) is the combined, efficient handling of information and resources within a commercial organization, including structuring and sharing the knowledge and experiences of employees.

Here is the definition according to Wikipedia:

Knowledge management is the process of creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieve organizational objectives by making the best use of knowledge.

An effective knowledge management solution offers improved efficiency, innovation, sharing of lessons learned and more generally a competitive advantage, lowering development cycle and improving customer support and the overall end-user experience, allowing them to self-serve in order to find answers, which has been shown to be significantly more preferable that containing support teams for assistance.

Transferring knowledge within your organization, retaining it when people leave, as well as sharing this intelligence with your end users are practical examples of efficient knowledge management, where information is gathered, organized and is easily accessible.

With so much rich information available in the form of blogs, wikis, social messaging, and other document sharing applications, it is easy to lose track of where all your valuable information is located. A good knowledge management strategy is one that harnesses all of your data, making it readily accessible to the right people through a rich set of permissions in a way that is easily consumable by humans or machines.

Using NLP with KM

What is NLP?

Natural Language Processing (NLP) is a form of artificial intelligence dealing with interactions between computers and humans.

The definition according to Wikipedia is:

Natural language processing is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of

natural language data.

The primary goal of NLP is to read and understand human language in a more meaningful, and therefore valuable way.

Machine Learning techniques and algorithms are used to derive meaning from human language, often breaking down sentences into semantic parts, where the type and form of words is derived.

This means things like verb and nouns are not only identified, but also how they are applied in the context of the surrounding sentence structure is extracted, providing much more meaning to what started out as a string of words as far as the computer was concerned.

Using NLP with KM

How is NLP Used?

Today’s speech processing applications rely on NLP to derive semantic results or meaning from spoken phrases leading to better (more intelligent?) automated assistants and call center services.

Word processing applications use NLP to verify the grammar of texts, and prompt when ambiguity or possible errors are detected, potentially offering alternatives.

Language translation applications like Google Translate and others use NLP to derive the meaning from sentences to translate them more accurately into other languages.

Consider the word “duck” for example, depending on how it is used in a sentence, it could mean the bird (the noun), or the action of ducking (the verb).

NLP assists in making this sort of distinction and therefore makes human text more understandable from a machine’s perspective, so that information can be more easily referenced and used (or even produced) by computers and software.

How can NLP be applied to Knowledge Management?

One of the most frustrating things about knowledge management is finding information quickly when it’s needed.

Previously, whenever data was stored and later accessed without NLP, finding something was a question of querying a database to search for a word or phrase in its unstructured repository, returning results as they are found.

Not only is this a time-consuming process, but the results that are returned are also unstructured and unfiltered, so the information you are looking for might be somewhere in those results, but your next challenge would be working through those results to find it. This is not an effective use of anyone’s time or energy.
Using NLP with KM

With NLP, data can be processed automatically, behind the scenes, and indexed in a structured way that can easily be propagated using NLP search algorithms, so the semantic structure of the information is already indexed and easily searched.

This is how search engines like Bing and Google are able to very rapidly parse all of the millions of pages across the Internet to show results that are relevant to your search query, with the most likely results shown first.

In this way, NLP does a lot of the hard work of looking through pages of information to show you the most likely candidates you are looking for, without you needing to pick through lots of results.

NLP and Artificial Intelligence (AI) in general continue to grow in popularity and implementation across all industries, especially with the current trend towards cloud and serverless computing. This is largely due to the potential to optimize the way many businesses operate in a fundamental way.

Viewed globally, AI is predicted to contribute 14.5 percent of GDP due to economic growth in North America by 2030 according to the Artificial Intelligence software market growth forecast by Statistica.

Companies able to adopt NLP and AI into their workflows and embrace this change are likely to accelerate ahead of those that do not keep pace.

With NLP being one of the primary links between humans and machines, more and more companies will be leveraging the potential of Natural Language in coming years to grow and maintain an edge over their competitors.

Our Approach

At GoGoWorx, we strongly believe in the potential of NLP and apply this throughout our business infrastructure.

For example, when someone performs a search on your hosted GoGoWorx Knowledge Base service, we perform a complex NLP search to very quickly sort through potentially thousands of pages of your stored information to present the highest ranked results to the user.

In other words, leveraging the power of Natural Language Processing can give you and your end users essentially instant access to your complete knowledge base in an intelligent way. Also, the more content you add to your knowledge base, the smarter the system becomes.

Taking this a step further, since most of your accumulated organization and product knowledge resides within your knowledge base (or rather, it should), your own client services teams and internal development teams can also rely on this rich resource as a valuable tool to quickly find answers to questions from both within your organization, or from your customers.

With GoGoWorx, this NLP capability is enabled automatically, without you needing to enable anything for this to work seamlessly.

Using NLP with KM

Who are GoGoWorx?

GoGoWorx is focused on improving customer interactions through exceptional knowledge management tools.

Our knowledge base is a fully hosted and managed service that’s designed for scalability and ease of use

Using NLP with KM

Did you know?

According to several online sources, including, Natural Language Processing is growing at an annual compound growth rate (CAGR) of at least 21% and was estimated to have a market value of USD 10.93 billion in 2019 (, increasing to USD 80.68 billion over the next 6 years?

Also, Knowledge Management is growing at a similar rate, with a CAGR of over 22% according to a recent report from Zion Market Research. Notably, this report points out that more than price or churn, customer satisfaction is based on the quality of service provided by companies, going on to point out that satisfied customers contribute 14 times more revenue than dissatisfied customers.

Is your Knowledge Management solution on track to maintain this pace? If not, we can help

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