MAKEathon 2022

metaphacts is an active contributor to this year's MAKEathon coming up on Sept 30 - Oct 2, 2022 at the FHNW Campus in Olten, Switzerland.


metaphacts contributions


Pre-MAKEathon tutorial

Title: Explicit knowledge modeling for AI-driven decision making

Date & time: September 23, 2022, 5:00 pm

Format: Online session

Abstract: Artificial Intelligence – be it data-driven or explicit AI – has the potential to revolutionize the way we work. A form of explicit AI, knowledge graphs deliver connected insights across functional or use case boundaries and drive knowledge democratization in the enterprise. Because they support the explicit modeling of knowledge traditionally hidden in complex processes, long documents, domain-specific applications, or domain experts' minds, they are a great foundation for trustworthy and explainable business decisions and processes.


This tutorial will prepare participants that want to approach the metaphacts challenge. Participants will gain solid familiarity with the following topics, datasets, tools, and technology:

  • Knowledge Graphs as well as standard formats, tools, and technologies behind the state-of-the-art, including ontologies and vocabularies as knowledge models and the underlying open standards RDFS, OWL, and SHACL.
  • metaphactory, a tool that enables participants to search, explore, and discover existing knowledge graphs based on a low-code application development concept. The tool will be used as an entry point to the training data for this exercise and can also be used as a proof-of-concept to display the outcome.
  • The semopenalex Scientific Publications dataset, presently in the form of a knowledge graph, offered as the basis for Knowledge Graph augmentation; together with some possible use-cases that can enable AI-driven decision making. These are provided as an open list to choose from or as inspiration for further use cases of choice.

Prerequisites: Familiarity with database technology, Machine Learning.

Find out more here »



Title: Building a smart job/candidate recommender

Abstract: A common challenge in every company is finding and retaining talent, which, amongst other things, requires a good understanding of skills needed and skills available. For this challenge, we propose explicit knowledge modeling using ontologies and vocabularies (taxonomies) in a knowledge graph, to create a common definition of skills; this knowledge model can be easily extended as skills evolve or new skills emerge. Building on top of public vocabularies allows for a quick start and should serve as a basis to provide a "smart" recommender utilizing not only the explicitly modeled knowledge, but also machine learning for a refined AI which can identify optimal candidates for a project or role, based on given needs within the project. Possible approaches could include, but are not limited to, simple query based recommendations, expanding the model and taxonomies to consider additional aspects besides just a direct mapping of skills, improvement of the skill taxonomy through better linking or hierarchy between skills (e.g., using some algorithmic approach) or introducing thesauri to improve matches and related terms, or, finally, machine learning to augment the explicit knowledge with learned knowledge.
Find out more here »