Strongly recommended course: Multimedia and Semantic Technologies (MST)
This topic is part of the FactCheck research project.
Currently, FactCheck collects information from the Web via IdaFix and dedicated crawlers, and provides the resulting insights via a Web API that serves, for the most part, JSON. Allowing complex, semantically rich queries over our collected data may be beneficial in delivering our results in a semantic-web-friendly way. Your task will be to enrich our existing FactCheck prototype(s) with semantic web technologies. As part of your work, you will…
- revisit our currently document-based data model, and redesign it to a triple/graph-based model more closely aligned to semantic web standards like OWL or RDF; this may additionally involve...finding suitable vocabularies (e.g., RDF-Cube)
- writing a script to automate the conversion from our existing document-based model to your new triple-based one
- enriching our existing data set with data collected from LOD collections (e.g., DBPedia)
- investigate a suitable storage solution (e.g., a triple store such as Apache Jena or RDF4J) for storing the redesigned data
- enable semantic search by configuring a suitable SPARQL endpoint and interface (e.g., Virtuoso, YASGUI), and optionally also hosting a customized version of DBPedia Lookup
Technologies: Python; rdflib; SPARQL; Docker; Java
Tags: Aichinger; FactCheck; Semantic Web; Databases
