Objective: Design, development and evaluation of techniques for distance measures and ranking for querying and reasoning on heterogeneous linked data.
Scientific datasets are inherently imprecise, and do not fit neatly into the black and white world of existing Semantic Web technology. Current and nascent techniques for linked data querying, representation and reasoning must be extended to take semantic distance measures into account.
We will develop reasoning and decision support services that cater for query results and data on a graded or continuous scale. These services can be tailored to meet the demands of different domains and users. This requires fault-tolerant, safe and scalable reasoning over uncertain and incomplete knowledge, embracing data, metadata and knowledge.