Event time & location
Sept 30 - Oct 2, 2022
Main building of the FHNW University of Applied Sciences And Arts Northwestern Switzerland
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.
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:
Prerequisites: Familiarity with database technology, Machine Learning.
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 »