- [Instructor] Digital transformation is an important topic
in every organization.
Agile ways of working are common in most projects today
and flat hierarchies are intended
to help companies focus more on their customers.
While Agile methods are already common in projects today,
we operate the filing of important documents and data
as we did 30 years ago.
Here, knowledge graphs can help to automatically prepare
the documents and the company with additional information
so that effective knowledge management is possible.
We want to make the most of the experience gained
from projects and from longtime employees
that leave the company.
Information from large volumes of text documents
can be processed using semantic technology.
In this way, the unstructured data
can be turned into a knowledge base that can be used
by both humans and machines.
Cross-organizational collaboration
is becoming increasingly important.
Products are now produced in global value chains
and regulatory requirements are becoming more stringent.
Today, however, data silos exist in many companies,
making it difficult to share data with business partners.
This is where semantic technology can help
to bring together data from different silos
and provide relevant information to business partners.
We want to use artificial intelligence,
especially machine learning, to gain new insights,
but the results should be comprehensible to humans.
In many areas of application, machines will make decisions
such as in self-driving cars.
It is necessary to check whether the algorithms
are calculating correctly.
However, in many machine learning projects,
the computer is a black box that generates outputs
purely on input data and statistical methods.
Here, knowledge graphs can be used to provide the machine
with a knowledge base
and to make the results comprehensible.
This is also referred as machine teaching.