Data management: Currently, the market is even more focused on skills and knowledge focused on management and strategic planning, but with increasing participation of technology and data.
What Are Knowledge Graphs?
Very briefly, the Knowledge Graph is a collection of information. But it doesn’t stop there because the most exciting thing is how this information connects and interacts.
This initiative was launched with the Google search system in 2012. As it is a platform that moves millions of data daily, the objective was to format the information so that people can access quality content without false content.
In the case of Google, information is gathered from varied, public and factual sources. This way, every time we search for something in the search engine, for example, the name of an artist, a song, or a procedure, among other possibilities, we obtain the maximum amount of information regarding the term searched in the minimum amount of time.
Suppose you type “Artificial Intelligence” using the search engine. In that case, a series of content will appear as suggestions, including advertisements, images, articles and even a list of questions (with the same term from other users).
But the results must not violate the specific policies of:
- integrity of information;
- highly representative content;
- dangerous content that incites hatred and violence;
- bullying;
- harassment;
- between others.
Any content that violates these conditions will be removed or corrected through automated processing of searches.
Such automation is possible thanks to search algorithms that omit information that violates the criteria, as well as through public reporting systems.
How Does It Work?
Previously, we mentioned that Google creates its Knowledge Graph by obtaining data from varied, public, verifiable sources and that the data is automated with algorithms.
This dynamic takes place using Artificial Intelligence, Data Sciences and Linguistics mechanisms as its central means, such as, for example, Natural Language Processing (NLP), which also has as a technique the composition of words and the analysis of vocabulary and context.
Knowledge Graphs have been used to assist in approving medicines for seriously ill patients since the hypothesis is developed within a context, and the reading of different data can be carried out by machines and humans, which speeds up solutions.
Thus, we have:
- application of semantics (interpretation of sentences) in context;
- relationship between data;
- framework for integration, unification, analysis and sharing of data.
Why Is It Necessary For Data Management
Without a doubt, the paramount importance is to enable innovation strategies in the field of knowledge.
As you can see, the digitalization of knowledge contributes to solving complex issues, such as health problems and environmental issues, among others, more quickly, easily and efficiently.
How Are Companies Applying The Concept In Business?
Large companies apply Knowledge Graphs to collect, use and understand data. They understand that, through Knowledge Graphs, it is possible to bring people together and facilitate collaborative work, which results in exponential business growth.
See some examples of business segments and how some companies apply Knowledge Graphs.
Chemicals
The German chemical company BASF applies Knowledge Graphs to digitalize knowledge, aiming at product development (R&D).
This large-scale data feed combines PLN with millions of technical and scientific documents. This way, it obtains more robust information for specific applications in R&D projects.
Financial System
The largest banks in the world use Knowledge Graphs to solve data problems involving information control, management and connection between employees and to achieve greater business flexibility through consumer behaviour analysis, offering proposals and services that better serve customers’ needs.
Technology
In addition to Google mentioned above, companies such as Facebook, LinkedIn and Amazon are betting on Knowledge Graphs to expand and facilitate increasingly intelligent research so that understanding data is not limited to just a restricted number of specialized professionals, which would harm the opportunity for new business.
Conclusion
Implementing the Knowledge Graph still presents organizational and technical challenges. We can mention the high cost to implement and the need for specialists, such as data engineers and knowledge engineers, with skills and experience in domain modelling (Domain Model) and data model.
With the examples mentioned, we were able to appreciate how much the use of Knowledge Graphs advances, leading to the need to overcome the limitations inherent in its applicability, requiring the participation of specialized professionals.
Also Read: Why Invest In Data Management? See 3 Reasons
