Linked Open Data-based knowledge panel built during a seminar at Karlsruhe Institute of Technology
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Linked Open Data Seminar 2016 - Knowledge Panel


This is a project in the context of the Linked Open Data (LOD) Seminar at AIFB at the Karlsruhe Institute of Technology. Goal was basically to integrate multiple LOD sources (in a first step only DBPedia and Yago) to build a knowledge panel or fact box (as known from Google or Wikipedia) on that basis. A major challenge was how to determine which properties of an entity, e.g. dbp:Karlsruhe are relevant and meaningful to be displayed to the user and which are not. Accordingly, a ranking of properties for specific entities or classes (rdf:type) of entities had to be elaborated, which is capable of ranking properties among multiple, distinct sources. While [1] already presented a good solution (although only working for one dataset, namely DBPedia) based on supervised machine learning, our approach is based of rather naive statistical metrics like TF-IDF. Our evaluation is based on rank biased overlap (RBO), as described in [2].

[1] Dessi, A., & Atzori, M. (2016). A machine-learning approach to ranking RDF properties. Future Generation Computer Systems, 54, 366–377.

[2] Webber, W., Moffat, A., & Zobel, J. (2010). A similarity measure for indefinite rankings. ACM Transactions on Information Systems, 28(4), 1–38.


The project consist of four software components.

  • Preprocessing scripts: Responsible for extracting statistics from LOD graphs and calculating TF and IDF on that base
  • Backend: Responsible for computing entity-specific, multi-source property ranking at runtime as well as constructing a combined JSON-LD serialized RDF graph from DBPedia and Yago on that base. Exposed as a RESTful webservice.
  • Frontend: Single Page App as user interface, which queries the backend based in a user input and prints a knowledge panel based on the response's RDF graph.
  • Evaluation: Scripts facilitating "manual" computation of RBO metrics for specific entities.

UML component diagram

UML sequence diagram