msg.ProfileMap

The means by which companies currently try to match the skills of their employees with the requirements of customers and the market are characterized by high manual processes and heterogeneous data sources. 

One way to solve this problem is an approach that uses methods from artificial intelligence to collect the knowledge of a company and to convert it into a machine-based knowledge representation. An AI-instance as a  learning system uses this knowledge to perform a cognitive analysis of text collections and to identify connections (knowledge discovery) using technologies such as Natural Language Processing (NLP) and neural networks. 

One solution based on this approach is ProfileMap from msg. msg.ProfileMap draws on a process called Named Entity Recognition (NER). NER analyzes texts that are based on natural language and whose information content is therefore available in semi-structured or unstructured form. 

The special feature of this cognitive text analysis is that it collects all information related to an issue and creates a relationship between the information provided. The result is a knowledge graph. It maps the complex relationships between entities, topics and fields of competence and makes them accessible for analysis. msg.ProfileMap is capable of determining such contextual relationships between data and to visualize them. 

This structure can now be used for various use cases. This includes in particular a AI-based search and matching of persons to project requests. Using a machine learning model, msg.ProfileMap matches the request text with the profiles of potential candidates. For this purpose, msg.ProfileMap uses not only the explicitly specified information, but also the information that can be derived from the knowledge graph. 

Using these AI technologies, staffing processes can be accelerated sustainably, transparency about the future viability of the company and the workforce can be shown and development paths for all employees can be identified.