# Semantic Layer The semantic interoperability challenge in verifiable education and skills credentials arises because, unlike ID credentials, the information they contain is not universally understood. In the case of ID credentials, claims like name, address, and ID number are straightforward and easily interpreted by any institution. However, in education and employment, the meanings of qualifications, skills, and degrees can vary significantly across different organizations and contexts. This fragmentation makes it difficult to ensure that the information in these credentials is accurately and consistently interpreted by all stakeholders. Velocity Network protocols make credentials universally meaningful while keeping credential payload light for end users. Issuers are using linked open data to ensure semantic interoperability of credentials. Velocity Network highlights semantic interoperability as a critical component to successful global credential exchange. Velocity Network protocol supports the capability for linked open data on all credentials, for example: (a) each credential includes an alignment property that permits an array of links to linked-data registries explaining what qualifications, skills and knowledge the credential attest to; (b) each credential type registered on Velocity network must have a JSON-LD context file, which enables these credentials to have semantic information about them in case they are presented off-network (for example on a social network page) (c) Issuers can rely on registries to add externally registered semantic information to each credential. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://velocity-network.gitbook.io/velocity-network/implementing-proof-of-qualifications-at-scale/semantic-layer.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.