Concept Sequencing
Rich Skill Descriptor Standards Implementation Recommendations
This document describes the Rich Skill Descriptor (RSD) class and how RSDs should be published for
compatibility with services that process machine readable representations of skills and competencies. Each
RSD is primarily identified by a canonical URL and consists of a skillStatement and supporting metadata to
identify alignment to other skills or job categories.
How Skillifying Can Make our Institutions More Employer-Friendly - Helix Education
powered by Sounder Kelly Ryan Bailey, Global Skills Evangelist at Emsi, a labor market analytics firm, joined the Enrollment Growth University podcast to talk about forging new partnerships with local employers by creating a shared skills language between us. “Skillifying” Education Employers typically need to move a lot faster than traditional higher education can go. …
Semantic Interoperability: Are you training your AI by mixing data sources that look the same but aren’t? - KDnuggets
Semantic interoperability is a challenge in AI systems, especially since data has become increasingly more complex. The other issue is that semantic interoperability may be compromised when people use the same system differently.
The power of Machine Learning to drive Talent Acquisition - Profinda
By Rob Hill, CRO at ProFinda Can a Machine Learning powered Knowledge Graph transform Talent Acquisition? The true transformation of talent acquisition will only occur when we start to focus the power of Machine Intelligence on understanding and mapping out the knowledge contained within an organisation. This can be achieved by building a knowledge graph of … The power of Machine Learning to drive Talent Acquisition Read More »
Formalizing Tag-Based Metadata With the Brick Ontology
Current efforts establishing semantic metadata standards for the built environment span academia, industry and standards bodies. For these standards to be effective, they must be clearly defined and easily extensible, encourage consistency in their usage, and integrate cleanly with existing industrial standards, such as BACnet. There is a natural tension between informal tag-based systems that rely upon idiom and convention for meaning, and formal ontologies amenable to automated tooling. We present a qualitative analysis of Project Haystack, a popular tagging system for building metadata, and identify a family of inherent interpretability and consistency issues in the tagging model that stem from its lack of a formal definition. To address these issues, we present the design and implementation of the Brick+ ontology, a drop-in replacement for Brick with clear formal semantics that enables the inference of a valid Brick model from an informal Haystack model, and demonstrate this inference across five Haystack models.