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Building new LMS integrations can be tricky. Here are five tips to keep in mind to help you create successful integrations.

An introduction on how to build a knowledge graph using employee and occupation skillsets. This presentation was given at the Nordic People Analytics Confere...

Networks have emerged as a popular method for studying mental disorders. Psychopathology networks consist of aspects (e.g., symptoms) of mental disorders (nodes) and the connections between those aspects (edges). Unfortunately, the visual presentation of networks can occasionally be misleading. For instance, researchers may be tempted to conclude that nodes that appear close together are highly related, and that nodes that are far apart are less related. Yet this is not always the case. In networks plotted with force-directed algorithms, the most popular approach, the spatial arrangement of nodes is not easily interpretable. However, other plotting approaches can render node positioning interpretable. We provide a brief tutorial on several methods including multidimensional scaling, principal components plotting, and eigenmodel networks. We compare the strengths and weaknesses of each method, noting how to properly interpret each type of plotting approach.

Analyzing arXiv articles with neo4j

In recent years Knowledge Graphs have been used to solve one of the biggest problems not only in machine learning but also in computer…

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.

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 »

PDF | This paper proposes an auto-tagging methodology using tags defined in the ontology. The auto-tagging methodology consists of two main processes:... | Find, read and cite all the research you need on ResearchGate

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.

Tag recommender schemes suggest related tags for an untagged resource and better tag suggestions to tagged resources. Tagging is very important if the user identifies the tag that is more precise to ...

DeakinCo. delivers purpose-built learning to help organisations prepare their workforce for the future.

Learn how NASA uses Neo4j to develop a knowledge graph as part of its knowledge architecture to analyze lessons learned and save astronauts' lives.

PDF | We present our CareerVis system, an interactive visualization tool to aid career education for high school and freshman college students. In... | Find, read and cite all the research you need on ResearchGate

David Meza, senior data scientist at NASA, and his team are building a talent mapping database to better identify talent for jobs.

His team is building a talent mapping database using Neo4j technology to build a knowledge graph to show the relationships between people, skills, and projects. VentureBeat: How are you using those categories to build a data model? Those elements consist of knowledge, skills, abilities, tasks, workforce characteristics, licensing, and education. And it’s similar for programs: we can connect back to what knowledge, skills, and tasks a person needs for each project. Within a graph database or knowledge graph, you can easily add information as you get it without messing up your schema or your data model.

At Coursera, we use data to power our product and better serve our learners. One example is our Skills Graph —a series of algorithms…

Access never-before-possible insights, and scale your team to deliver talent transformation with AI.

A few years after Google announced that their knowledge graph allowed searching for things, not strings, knowledge graphs...

Check out how David Meza from NASA combined knowledge graph and graph algorithms to find hidden skills within the organization.

David Meza, senior data scientist at NASA, and his team are building a talent mapping database to better identify talent for jobs.

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 »