View source for Native-code Rule Execution for efficient inference on large Web Graphs

{{Projectproposal
|Contact person=Jacopo Urbani
|Master areas=Computer Science and Communication, High Performance Distributed Computing, Internet and Web Technology, Knowledge Technology and Intelligent Internet Applications, Parallel and Distributed Computer Systems
|Fulfilled=No
}}
On the Web, search is moving from a traditional keyword search to
a more entity-centric search. To support this new type of search, Web engines
construct "Knowledge Graphs" -- graphs where nodes represent entities and edges
identify semantic relations between them. See, for instance, Google's Knowledge
Graph project, Microsoft's Satori, or Yahoo's Knowledge Graph. 

An important research topic consists of performing inference on these graphs to
enhance searches with more answers. One type of such inference is performed by
if-then rules that can be extracted from ontologies. Applying if-then rules is
challenging on Web graphs with billion edges and hundred of millions of
nodes. In order to improve the performance, this project proposes to define new
techniques to translate the declarative if-then rules into highly optimized C++
native code. This optimization should look into the actual structure of the
rules, and produce code that is tailored for each specific rule. This project
is embedded within the department's VLog project, which is currently one of the
leading inference engines for large Knowledge Graphs.