- [Instructor] When you start a knowledge graph project,
one of the first task is to analyze if there are already
standards of terms and vocabularies
in your industry or function.
For example, in the financial industry,
healthcare, and in corporate functions
such as human resources,
there are standardized vocabularies published.
In some industries, the use of a controlled vocabulary
is even becoming mandatory.
For example, in the context of electronic health data.
In particular, the use of multiple languages
poses challenges to companies.
Here, knowledge graphs can help to ensure
the correct translation of technical terms.
You should check whether the vocabularies relevant to you
are already existing in a standardized
machine-readable form.
The next question you should ask yourself is
how you can better link your data assets.
On one hand, this could be linking internal data assets,
such as meta data management or master data management,
but there's also potential to link internal data
with external data to gain new insights.
The goal is to increase data quality.
Knowledge graphs can help to identify and properly
manage personal data in the enterprise,
in order to fulfill data privacy requirements.
The next checkpoint is to improve the knowledge base
about your customers.
Knowledge graphs can help to gain new insights
about customer preferences, and a knowledge base can also
improve automated advice and product recommendations.
For example, in online stores.
Can as semantic knowledge base help you to
manage the configuration of complex products?
For example, automotive manufacturers
are using knowledge graphs to manage equipment options.
Does your product or service have to comply
with certain standards or norms?
This is where there is potential for knowledge graphs.
How do you handle your data in the enterprise?
Do you need to process large volumes of text documents
and to extract knowledge from them?
Then knowledge graphs are an effective support.
You can use knowledge graphs to improve the results
of machine learning.
For example, if the error rate in text recognition
is too high, then you should consider
first building a knowledge base with a knowledge graph
and then making the machine learning
algorithms available to it.
This is called machine teaching.