Discovering Connections

Discovering Unknown Connections - the DBpedia Relationship Finder

The Relationship Finder is a tool for exploring connections between objects in a Semantic Web knowledge base. It offers a new way to get insights about elements in an ontology, in particular for large amounts of instance data. For this reason, we applied the idea to the DBpedia data set, which contains an enormous amount of knowledge extracted from Wikipedia. We describe the workings of the Rel...

Discovering contextual connections between biological processes using high-throughput data

Discovering Meaningful Connections between Resources in the Web of Data

We will show that semantically annotated paths lead to discovering meaningful, non-trivial relations and connections between multiple resources in large online datasets such as the Web of Data. Graph algorithms have always been key in pathfinding applications (e.g., navigation systems). They make optimal use of available computation resources to find paths in structured data. Applying these alg...

Discovering Mid-level Visual Connections in Space and Time

Finding recurring visual patterns in data underlies much of modern computer vision. The emerging subfield of visual category discovery/visual data mining proposes to cluster visual patterns that capture more complex appearance than low-level blobs, corners, or oriented bars, without requiring any semantic labels. In particular, mid-level visual elements have recently been proposed as a new type...

Discovering Domains Mediating Protein Interactions

Background: Protein-protein interactions do not provide any direct information re‌garding the domains within the proteins that mediate the interactions. The majority of proteins are multi domain proteins and the interaction between them is often defined by the pairs of their domains. Most of the former studies focus only on interacting do‌main pairs. However they do not consider the in...