IDEAA · Techniques, methods and tools for Issue-Driven European Arena Analytics: supporting citizens to easily explore the trove of publicly available data to build a viewpoint on a specific issue.

Techniques, methods and tools for Issue-Driven European Arena Analytics: supporting citizens to easily explore the trove of publicly available data to build a viewpoint on a specific issue.

The final aim of the IDEAA MSCA is to support non-expert users to easily explore the trove of publicly available graph-based data and, in particular, semantic, i.e. RDF, data. RDF is a standard model where data are represented as triples subject-predicate-object. For example, the notion “Bill Gates has occupation entrepreneur” in RDF is represented by: a subject denoting “Bill Gates”, a predicate denoting “has occupation”, and an object denoting “entrepreneur”. Similarly, the fact that “Bill Gates co-founded Microsoft” is represented by: again a subject denoting “Bill Gates”, a predicate denoting “co-founded”, and an object denoting “Microsoft”. One can further increase the data by adding, e.g., that “Microsoft is of type technology company”. Here “Microsoft” becomes the subject of this triple, the predicate is “of type”, while the object is “technology company”. In this small example, the RDF dataset is composed of three triples. Many datasets are available in RDF, several are on the European Data Portal. They are also increasingly being published as part of Linked Open Data whose aim is to relate information from different data sources through semantic links. For example, a source D1 exposes information about People while a source D2 exposes information about Companies. D1 contains data about "Bill Gates" and D2 contains data about "Microsoft". We can link them by adding a property "co-founded" from "Bill Gates" in D1 to "Microsoft" in D2. This leads to great opportunities to analyze and monitor the many different aspects of our society and tremendous challenges in making sense of the data. A lot of public information is available. However, RDF graphs tend to grow very much in size, heterogeneity and complexity. They are hard to read and acquiring a comprehensive picture of their content is very difficult as it is easy to get lost in the huge amount of data. This lack of knowledge in an abundance of content hinders our full participation to modern society where basically all interactions are either directly digital, or are captured/recorded in digital fashion. In IDEAA we worked to overcome such challenge by pursuing three main objectives: (O1) the extraction of succinct and meaningful knowledge from RDF graphs as a means of representing the content of the graphs, (O2) suggesting, in an efficient way, interesting and unexpected aspects of the data to users and (O3) comparing knowledge from different sources.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз