Knowledge Graph Extension for Python

A team of DWS students has developed a Knowledge Graph extension for Python.

Knowledge graphs have been proposed as a universal means to organize information, both for free, public knowledge bases such as Wikidata or DBpedia, as well as for integrating information within organizations. As such, they can add value in many data analytics tasks. Therefore, bridges between the data analytics and the knowledge graph worlds have been proposed in the past, e.g., in the form of the Weka extension FeGeLOD and the RapidMiner LOD Extension.

Today, we are happy to introduce a new Python extension for processing knowledge graphs. With this extension, data analysts can easily link their data at hand to both public and private knowledge graphs, and add the wealth of public knowledge graphs such as Wikidata to their data analytics tasks with just a few simple lines of code. The extension can be combined with popular frameworks such as scikit-learn, and offers a variety of features ranging from data linkage and fusion to knowledge graph specific feature extraction and filtering.

The extension was developed in a student team project by Tabea-Clara Bucher, Xuehui Jiang, Ole Meyer, and Stephan Waitz, and supervised by Sven Hertling and Heiko Paulheim.