Companies and social media (such as forums) produce a large amount of texts. These texts often contain complex knowledge of what is important for companies and for the communication with customers to ensure the success of projects. The diversity of the texts makes the analysis of the texts and the extraction of knowledge very difficult and require well scalable, smart algorithms. Specific challenges in the analysis of customer texts are colloquial word-forms, foreign-language formulations, grammatical mistakes as well as synonyms and homonyms.

In the Data Analytics application center, algorithms are developed able to convert unstructured texts into knowledge graphs. A knowledge graph describes the entities of a domain and how they are related. The creation of a Knowledge Graph, however, is very time-consuming depending on the domain. For this reason, the implemented procedures are highly optimized and support the knowledge extraction process with different views and meta-data. The developed solutions are integrated into practical applications and benchmarked on different data collections.