Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Partnerships and Cooperations
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Section: New Results

Semantic graph exploration through interesting aggregates

RDF graphs can be large and complex; finding out interesting information within them is challenging. One easy method for users to discover such graphs is to be shown interesting aggregates (under the form of two-dimensional graphs, i.e., bar charts), where interestingness is evaluated through statistics criteria. While well understood for relational data, such exploration raises multiple challenges for RDF: facts, dimensions and measures have to be identified (as opposed to known beforehand); as there are more candidate aggregates, assessing their interestingness can be very costly; finally, ontologies bring novel specific challenges through the presence of implicit data, but also novel opportunities, enabling ontology-driven exploration from an aggregate initially proposed by the system.

The system Dagger we had previously proposed (2017) pioneered this approach, however its is quite inefficient, in particular due to the need to evaluate numerous, expensive aggregation queries.

In 2019, we have built upon Dagger to develop more efficient and more expressive versions thereof. Thus:

Figure 3. Interesting multi-dimensional aggregate automatically identified by Dagger .