How knowledge graphs can help businesses put their data to good use

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To understand the role of knowledge graphs and how they can be used to benefit a business, we asked Andreas Blumauer if we could repost one of his useful LinkedIn explainer posts. He kindly agreed! Andreas is the founder and CEO of Austrian-based Semantic Web Company, whose core product PoolParty Semantic Suite is the leading semantic AI platform for enterprise knowledge graphs, serving many of the Fortune 500.

Here he explains how knowledge graphs can be used in multiple ways to improve knowledge management capabilities, and add value to businesses in how they store and retrieve information for both staff and customers.

andreas blumauerKnowledge management is better with knowledge graphs

Data is not information, and information is not yet knowledge. For decades there has been a heated debate about the fact that a functioning knowledge management system is not something that can be installed in an intranet like any software system, and that knowledge cannot be stored in documents or databases.

With the rise of knowledge graphs (KGs), many knowledge management practitioners have asked themselves the question whether KGs are just another database, or whether this is ultimately the missing link between the knowledge level and the information and data levels in the DIKW pyramid as depicted here.

Knowledge-graphs-DIKW-pyramid

Can knowledge graphs help to turn data and information into knowledge?

Knowledge graphs stimulate cross-departmental and interdisciplinary communication. They help to orchestrate information flows or to link activities and expertise or ultimately even knowledge workers in larger organisations that are initially isolated from each other, eg, through mechanisms of ‘Semantic Matchmaking‘. Knowledge graphs should therefore be able to fulfil an abundance of long-cherished wishes of the knowledge manager community.

Can knowledge graphs help to turn data and information into knowledge?

Let’s look at this systematically. Which typical challenge in knowledge management can be met with the help of KGs – and how?

  • Keeping people motivated to share data and information – Provide controlled vocabularies so that people can trust that their sharing activities will be successful.
  • Keeping shared information up to date and accurate – The continuous content analysis as part of the ongoing work on the knowledge graphs serves as a mechanism to keep both the metadata and the shared information up to date.
  • Interpreting data and information effectively – KGs help that information derived by one person or group will be mapped or standardised in order to be meaningful to someone else in the organisation.
  • Ensuring relevancy, making it easy for people to find what they are looking for – The algorithms for information retrieval focus mainly on relevancy scoring models. Knowledge graphs enable semantic content classification and context-driven searches, making a more accurate calculation of relevancy possible.

With the help of knowledge graphs, more active users benefit from more precise and relevant recommendations from the system.

  • Rewarding active users – Instead of simply rewarding more active users with stars or thumbs-ups, they are rewarded directly with the help of knowledge graphs: more active users benefit from more precise and relevant recommendations from the system, since knowledge and interest profiles can be continuously updated and expanded with the help of semantic technologies.
  • Facilitating collaboration among team members and different teams – Semantic matchmaking on the basis of graphs helps to network people according to their knowledge profiles.
  • Providing more user-friendly IT-Systems – KGs are changing the way business users and developers can view data. It is no longer the database engineers’ regime that determines how applications are developed, but rather how we as end users think about and interpret data. Therefore, KGs are not only a good basis for visualising knowledge, but also an effective way to organise data and make it available as an interface to developers and users along the actual business logic.
  • Facilitating individual learning paths – Based on personal skills, competencies, interests and learning styles, there are many ways through a curriculum to achieve individual learning goals or next career steps. With a knowledge graph, learning systems are equipped with recommendation systems that can help people identify individual learning paths that fit into complex learning goal systems where individual and organisational interests are linked.
  • Not-invented-here-syndrome: Overcoming the phenomenon of generating resistance within an organisation against externally developed knowledge requires a stronger focus on the principle of ‘inclusion’. Ongoing work on knowledge graphs can be organised in such a way that they are perceived as highly collaborative activities, and thus KGs will be widely accepted as central knowledge hubs.

It is clear that knowledge graphs will not replace a comprehensive knowledge management program but they should be embedded as an integral part of such a program. Ultimately, every department and every person involved in a KM program should be included in the process of designing, building and shaping an enterprise knowledge graph, which then not only links data but also brings people and their knowledge together.

Will knowledge graphs ultimately make us even wiser?

Coming back to the DIKW pyramid: knowledge graphs have great potential to finally link the more technically oriented layers of data and information with the human-centric knowledge management topic of knowledge. I am afraid that wisdom has to emerge elsewhere, and the missing link to knowledge has not yet been found.

This post first appeared on Andreas Blumauer’s LinkedIn and is reproduced here with his kind permission. Some contextual links added by Firehead.

For a free download of Andreas’s book ‘Knowledge Graph Cook Book’, click here or on the image…

knowledge-graph-cookbook

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