Ontos EIGER platform comprises a suite of tools for integrating and linking many different data sources. Through this way we bring meaning to data and continuously enhance the organisational knowledgebase.


MINER: Extracting knowledge from text

Domain modeling

Instead of the classical entity relationship model (ERM), Ontos models are flexible by using the W3C standard of RDF and OWL. The graph based approach of real world entities like “people”, “location”, “product” or “organisation” including their attributes allows a fast development of the domain knowledge. Based on those models (ontology) we can on the fly define and re-define the model and map it to the various heterogeneous data sources. No programming is required and therefore increases the efficiency and lowers the costs during design, development and ongoing support.



MINER is one of the flagship module that allows to work with natural languages. We use neural networks and deep learning to train MINER in order to extract from natural language text objects like “people”, “location” or “products”. In the near future we will be able to analyse the semantic relation between the objects like “people work at company”, understand facts such as “company was bought for x $ on date y” and extract the sentiment and mood. The extracted information is transformed and linked within the platform and stored in the enterprise knowledge graph.


QUAD is the backbone to store the enterprise knowledge within graphs. We combine semantic and graph technologies supporting triples/quads while maintaining the standard of W3C such as RDF and SPARQL 1.1.

Based on QUAD you can run complex queries to support enhanced search and analytics helping companies make better decisions.

QUAD is a lightweight deployment allowing to run the graph store on mobile devices including smart phones, tablet and raspberry.


Extract essential information from structured data silos. We can map to the domain knowledge (ontology) to structured data sources such as Relational DB,CSV, XML/JSON or RDF and access those data sources directly from our workbench.

Machine and Deep Learning

We are continuously improving our machine and deep learning module to understand patterns inside the data. Currently tuned to understand patterns in natural language text but in the near future we will apply the approach to identify patterns in all aggregated data inside the knowledge graph in order to make better predictions supporting user to make better decisions.


Intelligent entity linking and disambiguation. Extracting knowledge from various heterogeneous data sources is not enough if you are not able to link them together in order to gain more in depth insight into your data. LINKEd allows a (semi) automatic linking of the entities using for example the owl:sameAs link. Through this method your knowledge
graph of linked information is growing and
proving more actionable knowledge for better


We provide an intuitive possibility to define your own dashboards based on a selection of predefined widgets. This approach guarantees the highest flexibility of customer solution without the need to have programming skills.

Big Data, Scalability and Security

Ontos Eiger relies on proven technologies such as Apache Spark, HBase, Cassandra and Kafka. Built on top of those technologies we developed our own modules to handle the large data flows, streams and text pipeline processing. Through settings we can assign users a variety of access permissions. Specify if a graph is private or public.