DSTC's Knowledge research examines the semantic interoperability of existing knowledge bases including ontology integration. The program explores aggregating and deriving knowledge from static and real-time information in rich and human-centric forms such as natural language and multimedia. It explores secure knowledge sharing through the use of views which filter, aggregate or depersonalise private information and explores inferencing and reasoning using information from multiple organisations that may be inconsistent or aggregated.

The goal of the Program is to offer knowledge that is known by one entity to other entities who may reason about it individually or in aggregation with other knowledge.

Traditionally, information modelling has focussed on schemas based around the storage of orthogonal primitive information elements (basic facts) and relied on querying and other processing to derive all other information. In reality, much information is available in alternative primitive forms - the differences may be subtle nuances or fundamental paradigm shifts - or in aggregated complex forms.

The Knowledge program is mainly concerned with what can be shared, rather than kept private. Specific issues are: merging of alternative views of similar knowledge; support for temporal and historical analysis; access-control aligned with trust models and contracts; aggregation and anonymisation of data for sharing purposes; and detection of attempts or reverse-engineer of private information from aggregated summaries.