The post Notice of Special Interest (NOSI): Analysis of Existing Linked Datasets to Understand the Relationship between Housing Program Participation and Risk for Chronic Diseases and Other Conditions (R01-Clinical Trial Not Allowed) [First Available Due Date: Oct 07] was curated by information for practice.

The post Notice of Special Interest (NOSI): Analysis of Existing Linked Datasets to Understand the Relationship between Housing Program Participation and Risk for Chronic Diseases and Other Conditions (R01-Clinical Trial Not Allowed) was curated by information for practice.

We argue that "Question Answering with Knowledge Base" and "Question Answering over Linked Data" are currently two instances of the same problem, despite one explicitly declares to deal with Linked Data. We point out the lack of existing methods to evaluate question answering on datasets which exploit external links to the rest of the cloud or share common schema. To this end, we propose the creation of new evaluation settings to leverage the advantages of the Semantic Web to achieve AI-complete question answering.

Graphical models are commonly used to represent conditional dependence relationships between variables. There are multiple methods available for exploring them from high-dimensional data, but almost all of them rely on the assumption that the observations are independent and identically distributed. At the same time, observations connected by a network are becoming increasingly common, and tend to violate these assumptions. Here we develop a Gaussian graphical model for observations connected by a network with potentially different mean vectors, varying smoothly over the network. We propose an efficient estimation algorithm and demonstrate its effectiveness on both simulated and real data, obtaining meaningful and interpretable results on a statistics coauthorship network. We also prove that our method estimates both the inverse covariance matrix and the corresponding graph structure correctly under the assumption of network “cohesion”, which refers to the empirically observed phenomenon of network neighbors sharing similar traits.

The O*NET Content Model includes worker- and job-oriented hierarchical taxonomies that can effectively serve as frameworks for organizing workforce competencies, credentials, and other work-relevant information. Visit the new Competency Frameworks section to discover a variety of easy-to-use competency frameworks, including Technology Skills, Abilities, Cross-Functional Skills, Basic Skills, and Knowledge.

The frameworks are available in Excel format and also as JSON-LD: machine-readable Linked Data  described using the CTDL-ASN  schema (Credential Transparency Description Language Profile of Achievement Standards Network Description Language) developed by the Credential Engine  project.

For a summary of O*NET machine-discoverable information see: Link to Us--Machine-Readable Data.

Synonym content is added to the Abilities, Cross-Functional Skills, Basic Skills, and Knowledge competency frameworks. The simplified language is drawn from easy-to-read content included within My Next Move and My Next Move for Veterans. The frameworks, built from worker- and job-oriented hierarchical taxonomies, can help in organizing workforce competencies, credentials, and other work-relevant information, and also serve as a starting point for creating skills thesauruses. Visit the Competency Frameworks section to discover these easy-to-use tools.

The frameworks are available in Excel format and also as JSON-LD: machine-readable Linked Data described using the CTDL-ASN schema (Credential Transparency Description Language Profile of Achievement Standards Network Description Language) developed by the Credential Engine project.

For a summary of O*NET machine-discoverable information see: Link to Us--Machine-Readable Data.