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Helmut Nagy

COO

Expert in: Data Integration and Data Fabric, Search and Recommender Engines

I have been working in the field of knowledge management for about 20 years. Since joining SWC in 2010, I have been involved as a senior consultant in many projects for the introduction of knowledge graphs in industry and public administration.

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Andreas Blumauer

CEO

Expert in: Data Integration and Data Fabric, Search and Recommender Engines

I am passionate about knowledge graphs, semantic technologies and machine learning. Since the company was founded 15 years ago, we have helped more than 150 organizations to make use of graph and Semantic AI-based methods and techniques.

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Data Integration and Data Fabric

The semantic data fabric is a new solution to data silos that combines the best-of-breed technologies, data catalogs and knowledge graphs, based on Semantic AI. With a semantic data fabric, companies can combine text and documents (unstructured) with data residing in relational databases and data warehouses (structured) to create a much more comprehensive and accurate view of their customers, employees, products, and other vital areas of business.

Search and Recommender Engines

Arrive at enriched and in-depth search results that provide relevant facts and contextualized answers to your specific questions, rather than a broad search result with many (ir)relevant documents and messages – but no valuable input. Find out how knowledge graphs can be used to implement semantic search and recommendations that are relevant to your users.

Taxonomies, Ontologies and Knowledge Graphs

Many organizations are already using knowledge graph technology to help themselves stay ahead of the game. And knowledge graphs and graph databases have been in use for all types of industries, ranging from banking, the auto industry, oil and gas to pharmaceutical and health, retail, publishing, the media and more. Although these companies use Knowledge Graphs for different use cases, the end game is the same: adding value to data so it can be used (and ultimately re-used) in a meaningful and more intelligent way.

Text mining and Tagging

Manually tagging an entire database, file by file, is very time-consuming and involves a lot of people. But worse than that, one of the results of manual tagging are inconsistent search results. Graph-based text mining can improve this process making it not only faster, but more consistent and precise. This is achieved by automatically assigning metadata to documents based on controlled vocabularies.