Don’t. Identifiers should be opaque. If you’re given an owl:sameAs assertion from a party you trust, use that. If you need to mint surrogates because what you’re given aren’t Globally Unique, Persistent and Resolvable Identifiers (GUPRIs) ¹ , either house your inheritance as local parts/suffixes in your global namespace, assert datatype properties to record the historical correspondence, or both.

Is a metadata record “almost” expressed in the same language you used for your filter criteria?

Why would one consider indexing validators? Reuse. The value of reuse seems obvious for structural and semantic specification , i.e. schemas and controlled vocabularies – there is opportunity to perceive two datasets as aligned.

Indexing identifiers is key to disambiguating entities. Wikipedia has disambiguation pages. For example, there are various concepts in mathematics and computing, various computing products, and various companies that identify with the term “Precision”. I made disambiguation pages for same-chemical-formula inorganic crystal structures for the Materials Project. Indexing identifiers is also key to unifying entities.

How do you validate a reified trace of digital-object provenance? Is it even possible? This is syntactic validation. Values that should be strings are strings, dates are dates, lists are lists, you know the drill… Is it plausible? This is semantic validation.

Rory Macneil nails it: ¹ Rory Macneil and Nick Garabedian, FAIR Data Podcast, August 31, 2022.

Given a representation of (meta)data that dcterms:conformsTo some data profile, you may wish to translate it to another data profile. If a resource is accesible from an HTTP server, then as a client you may negotiate the content representation in a standard way.

To validate is to compute, so indexing metadata for past validation events and caching any detailed payloads can save time and effort. Why index? To search. Why search? To find relevant (“likely valid”), ranked (“more likely valid”) results.

Given a fip:Metadata-schema and a validator for it, such as a sh:Validator or a JSON Schema, how do you determine that the validator is…valid? That it speaks the desired fip:Knowledge-representation-language, that it knows all the terms in a desired fip:Structured-vocabulary and checks their usage against a desired fip:Semantic-model? In other words, that it adheres to a doap:Specification? I do not know.

What conveys that data has been validated or is yet to be validated? How do you identify the nature and process of validation for a given digital object? Who is involved? What auxiiary resources are involved? Is the process: Do-it-yourself, with (implicit or explicit) references to validation assets? Do-it-with-you, with references to validation services? Do-it-for-you, with references to validation results and/or signoffs?

This week on Machine-Centric Science, I interviewed Martynas Jusevičius, currently at AtomGraph and based in Copenhagen, Denmark.