At the end of 2021 our colleague, Senan Kiryakos left the JVMG project to pursue other professional goals. Senan had been responsible for a lot of the work on processing the data we received from the communities and integrating them into the JVMG Knowledge Graph. He wrote two extensive posts for this blog on some aspects of this work: Turning Fan-Created Data into Linked Data I: Ontology Creation and Turning Fan-Created Data into Linked Data II: Data Transformation.Continue Reading
The project has reached an important milestone. The collected data from six sources is available in RDF and can be viewed on the mediagraph.link domain. Currently, only the transformed original data is available, as we are still working to complete the next step in the data integration process.
We would like to use this opportunity to thank all the enthusiast communities who make data available under a free license on the web and specifically the communities who kindly supported the project by attending our initial workshop, and exchanged ideas on data in the Japanese visual media domain with us. We are especially grateful to the communities who have agreed to offer us a specific open licence (detailed information is available here) for the parts of their data that have been integrated into our database.Continue reading “Milestone: Public access to the knowledge graph”
The JVMG project collects data from multiple sources and converts it into the RDF format. One of the core characteristics of this format is that all entities and attributes are represented as URIs, while the value of said attributes are either URIs (thus linking two entities using a property) or literal values.
The SPARQL language can then be used to formulate search queries on RDF stored in a database, but this requires the user to be both familiar with the query language as well as the structure of the RDF data.
As all entities and properties are identified by URIs, one way to explore RDF data is having a web server that serves the domain that the data URIs are residing in and shows all information that can be associated with a given URI.
This functionality is one of the main ideas of linked data: a linked data frontend can serve “raw” RDF data to programs that try to resolve an URI while human users using a browser to resolve the same URI get a human-readable HTML view of all the data that is associated with this URI.
Such a frontend also allows for simple exploration and navigation of a dataset, as all URIs in the human-readable view can be made into clickable links.
As our project is data-driven, it can use the resources of the Stuttgart Media University Institute for Applied Artificial Intelligence. Those resources have recently been upgraded. Meet the deep learning servers, each with four nvidia GPUs:
The project members will work in the new lab room of the Stuttgart Media University Institute for Applied Artificial Intelligence. The lab room is currently being furnished, and I have prepared a suitable decorative piece 🙂
We have found two excellent young researchers who are interested in working on the project. As soon as the contracts are finalized, we will introduce the new team members here on the project website.
We have been informed by the German Research Foundation (DFG) that our grant application has passed peer review and the program board has deceided to approve our application. The research plan will be fully funded with no cuts.
The project can start in 2019.