eccenca develops knowledge graph based solutions for an agile data exploitation and company-wide data transparency.
eccenca Corporate Memory provides a multi-disciplinary integrative platform for managing rules, constraints, capabilities, configurations and data in a single application. Overcoming the limitations of traditional, application centric (meta) data management models, its semantic knowledge graph is both highly extensible, integrative as well as interpretable both by machines and business users.
We recently added a plugin interface to our platform which allows externals to extend the functionality of eccenca Corporate Memory in terms of Python-based plugins.
Feel free to contact us for more information. We eagerly look forward to working with you and will support you in anything you need. Once you get selected, we will assist you in your onboarding process and will grant you access to the eccenca Corporate Memory.
The results of the GSoC project could be a plugin which adds workflow and transformation functionality which use DBpedia infrastructure.
Examples could be:
- DBpedia spotlight lookup
- Extraction of specific sub-datasets
- Integration with the DBpedia Databus
- Build a DBpedia Machine Learning model (embeddings, deep learning) pipeline.
Feel free to submit propose your own idea, we are always looking for new ways of integrating knowledge graphs.
The plugin itself will be open source, and can be developed in the DBpedia GitHub space as well as published to PyPi. Corporate Memory users (admins) can then install the plugin to Corporate Memory and have instantly enabled their deployment to use your self-made DBpedia functionality.
The GSoC student will be integrated into the work of the DBpedia community as well as eccenca.
- Have a look at the knowledge transfer video or the slide deck.
- Have a look at the template repository for cmem plugin template: https://github.com/eccenca/cmem-plugin-template
- Project size: 300h
- Language: Python
- Difficulty: easy - medium