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…

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

At Beamery, we are pushing the boundaries of how modern recruitment is being done.

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

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

Background Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relationship between subject and object. A triple can also contain provenance information, which consists of references to the sources of the triple (e.g. scientific publications or database entries). Knowledge graphs have been used to classify drug-disease pairs for drug efficacy screening, but existing computational methods have often ignored predicate and provenance information. Using this information, we aimed to develop a supervised machine learning classifier and determine the added value of predicate and provenance information for drug efficacy screening. To ensure the biological plausibility of our method we performed our research on the protein level, where drugs are represented by their drug target proteins, and diseases by their disease proteins. Results Using random forests with repeated 10-fold cross-validation, our method achieved an area under the ROC curve (AUC) of 78.1% and 74.3% for two reference sets. We benchmarked against a state-of-the-art knowledge-graph technique that does not use predicate and provenance information, obtaining AUCs of 65.6% and 64.6%, respectively. Classifiers that only used predicate information performed superior to classifiers that only used provenance information, but using both performed best. Conclusion We conclude that both predicate and provenance information provide added value for drug efficacy screening.

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.

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.

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

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 »

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

Learn the ins and outs of RDF vs. labeled property graphs so you can choose the right graph technology for your use case and get the most from your data.

In this section, you will learn how to represent graph data using a variety of modeling decisions. How you construct your data model can impact your queries and performance.

Online skill tree maker to design detailed, stunning skill trees. Prebuilt templates, customization options, and powerful collaboration capabilites.

Doing cool things with data!

Reflections on this Year's Prostep IVIP Symposium This year’s Prostep IVIP Symposium was laden with topics around the usage of data ranging from AI over the digital thread and the digital twin. I am convinced there are two major technologies that will be the driving force for such data centric devel

For the last year I have been slowly moving to the realization that to be competitive, almost all organizations are going to need to build…

Gaia-X represents the next generation of data infrastructure: an open, transparent and secure digital ecosystem, where data and services can be made available, collated and shared in an environment of trust.