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Thomas Burg

SVP North America, Associate Director Sales

Expert in: Search and Recommender Engines, Text mining and Tagging

I quit History when I encountered the first web browser in 1994. Since then I’m aiming at Linking Data to Knowledge by:

  • Structuring the Unstructured
  • Enabling Answers To Questions

Let’s start a conversation and find the right technology that can solve your business problem.

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Daniel Gallar

Daniel Gallar

Customer Success Manager

Expert in: Taxonomies, Ontologies and Knowledge Graphs, Text mining and Tagging

I have a strong academic background in Linguistics and extensive customer relationship management experience in the software industry. I am very keen on understanding what keeps customers “awake at night” and trying to offer them bespoke and scalable semantic solutions that address their specific problems.

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Alexi Lopez Lorca

Alexi Lopez Lorca

Knowledge Engineer

Expert in: Taxonomies, Ontologies and Knowledge Graphs, Text mining and Tagging

Drawing upon 10 years of experience in academia, the semantic web, business analysis and project management, I do taxonomy and ontology modelling for our customers at the SWC and I provide training and project coordination to ensure customer success.

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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.