2024 10 25 Semantic Flow Overview

• interlocutor: chatgpt4.o

sflow description

dave

Semantic Flow is a set of conventions and helpful utilities for managing IRI namespaces and RDF dataset distributions on github or gitlab

chatgpt

Got it. If you need any specific conventions or utility examples fleshed out for Semantic Flow, just let me know!

SFNamespaceRepo

dave

I'll abbreviate references to Semantic Flow as "sf". Instead of "ManagedNamespaceRepo", maybe "SFNamespaceRepo"? Thinking about the sf mechanics, I want multiple git repos to be able to contribute their NamedIndividuals to a namespace, so within a SFNamespaceRepo, the hierarchy looks like:

• /docs (the generated semantic flow site that github pages points to)
• /contributing-repos (a folder where checked out repos live; they'll be scanned for IRIs in the SFNamespaceRepo
• / (the root which contains configuration files and the static site generator code)
• /templates (contains templates which the SSG applies to NamedIndividuals based on configuration)

Regarding templates, the contributing-repos should also be able to supply templates and template-mappings, but the SFNamespaceRepo can choose whether to honor those, use its own mappings, or try to combine things.

chatgpt

This layout sounds modular and flexible, especially with the SFNamespaceRepo’s control over templates and mappings. This should work well for accommodating multiple contributors while keeping control over the namespace’s presentation and structure. If you want any specifics on managing template precedence or mappings across repositories, I can help brainstorm solutions.

eleventy vs metalsmith

dave

Trying to choose an SSG. metalsmith-ldschema actually seems close to what I want. Help me choose eleventy, metalsmith or some other javascript-based ssg.

chatgpt

If you're leaning toward RDF compatibility, here’s a breakdown of Metalsmith, Eleventy, and other options:

1. Metalsmith (with metalsmith-ldschema):

   • Pros: Metalsmith’s plugin-driven architecture allows you to process RDF with plugins like metalsmith-ldschema, which can apply linked data schema transformations. It’s particularly suited for structured data handling and could be customized to parse and mint IRIs directly from RDF datasets in your SFNamespaceRepo.
   • Cons: While flexible, Metalsmith might lack some of the markdown features and built-in templates that others offer out-of-the-box, making it more of a DIY solution.

2. Eleventy:

   • Pros: Eleventy is popular for its simplicity and speed. Its templating support is extensive (Nunjucks, Liquid, etc.), and it’s highly configurable for generating static pages. While Eleventy doesn’t directly support RDF, you could preprocess RDF datasets and output JSON/Markdown for Eleventy to use.
   • Cons: Requires more setup for RDF and linked data, as you’d need to add custom scripts for RDF parsing, potentially complicating your workflow.

3. Other Options:

   • Gatsby: If you want React, Gatsby has plugins for loading JSON and handling complex data, which could be useful if you're willing to preprocess RDF to JSON. It’s more opinionated than Metalsmith, though.
   • Hexo: Lightweight and easy for generating Markdown-based sites. Limited linked data support, so it would require significant customization to integrate RDF.

Recommendation: Given your focus on RDF and structured data, Metalsmith with metalsmith-ldschema is likely the best fit, as it’s closest to the semantic and data-handling capabilities you need. If you want a more templating-friendly solution without RDF specifics, Eleventy paired with a custom RDF-to-JSON preprocessor would be a solid alternative.

eleventy doesn't have a plug-in architecture?

Eleventy does have a plugin architecture, but it’s more limited than some others (like Gatsby or Metalsmith). Eleventy plugins are essentially JavaScript modules that you can import and configure to add specific functionality, but it lacks a central plugin registry, and the community plugins are not as extensive or modular.

Instead, Eleventy encourages adding functionality directly through JavaScript frontmatter files, filters, shortcodes, and custom collections, which can approximate plugins in functionality but don’t have the same plug-and-play simplicity.

For more complex data transformations or external data ingestion, you can still achieve a lot by writing your own data processing scripts in Eleventy, but it may involve more manual integration for your needs than Metalsmith’s plugin-based approach, especially for processing RDF data.

how does the metalsmith plug-in architecture work?

Metalsmith's plugin architecture is built around a flexible pipeline, where each plugin is a function that processes files and metadata in sequence. Here’s how it works:

1. Core Structure:

   • The Metalsmith pipeline loads files from a source directory and transforms them with a sequence of plugins.
   • Each plugin receives the current files (an object representing each file and its metadata) and metadata (global metadata shared across all files) and can read, modify, or generate files.
   • Plugins are run in the order they’re registered, allowing you to structure a processing flow from raw input to final output.

2. Plugin Configuration:

   • You can configure and chain plugins directly in Metalsmith’s configuration file or setup script, typically a build.js file.
   • Each plugin is initialized with options, allowing specific behavior customization based on parameters or conditions.

3. Creating Custom Plugins:

   • Plugins are simply JavaScript functions (or npm packages) that follow the Metalsmith plugin API, where files and metadata are passed as parameters.
   • You can make a custom plugin by defining it as a function, then integrate it directly into the pipeline.

4. Example: Here’s a basic example that demonstrates how Metalsmith works with plugins:

   const Metalsmith = require('metalsmith');
   const markdown = require('@metalsmith/markdown'); // Markdown plugin
   const layouts = require('@metalsmith/layouts'); // Templating plugin
   const ldschema = require('metalsmith-ldschema'); // RDF-linked data plugin
   
   Metalsmith(__dirname)
     .source('./src')
     .destination('./build')
     .use(markdown()) // Converts Markdown files to HTML
     .use(ldschema({ schemaFile: './schema.json' })) // Processes RDF data
     .use(layouts({ engine: 'nunjucks' })) // Applies layouts
     .build(function(err) {
       if (err) throw err;
       console.log('Build complete!');
     });
   

5. Using metalsmith-ldschema:

   • This plugin allows parsing linked data using a defined schema, which you could use to parse RDF, extract IRIs, or generate additional metadata. By chaining this with other plugins, you can create a semantic transformation pipeline tailored for RDF data in your SFNamespaceRepo.

Metalsmith’s flexibility and plugin-based approach make it well-suited for complex or unusual build processes, like RDF and linked data handling, especially when you want more granular control over data transformations and output generation.

Once an IRI is minted, it should be semi-permanent, only removed, eg. in case of legal requirements. So even if the only referring "contributing repo" is missing, pre-minted IRIs should persist.

Got it. Making IRIs semi-permanent will ensure consistency and prevent broken links or lost references, even as contributing repos change. This approach should work well for stable and reliable referencing across multiple sources and use cases.

ideally, sf ssg can keep track of whether a repo has been modified, and only scan modified/added files

Efficiently tracking modifications will be a smart optimization, especially as your namespaces grow. This could be implemented by storing a file-level metadata state (e.g., checksums or timestamps) for each repo and scanning only when changes are detected, possibly with something like a git status or diff to identify modified files. Let me know if you want help designing the exact tracking mechanism!

Seems like it might be difficult or impossible to use relative URI literals in an RDF dataset

Yes, using relative URIs in RDF can be tricky and often isn’t recommended due to interoperability issues:

1. Lack of Consistent Resolution: Relative URIs depend on a base IRI, which isn’t always guaranteed when RDF datasets are used in different contexts or systems. Many RDF tools and triple stores expect fully qualified IRIs and may not handle relative URIs predictably, especially if the dataset is accessed in different locations or protocols.

2. Serialization Specificity: Some RDF serializations like Turtle and RDF/XML support base IRIs, allowing relative URIs to be resolved correctly within a specific file. However, this resolution may break when the data is processed outside that file or in different systems, losing its relative context.

3. DCAT and Cataloging: If the goal is to simplify or modularize IRIs across different namespaces, you could use DCAT catalogs with explicit base IRIs for datasets. This keeps namespace organization clear and minimizes the reliance on relative paths.

4. Custom Parsing or Base Management: Some systems handle relative URIs in controlled environments, like certain APIs or custom parsing frameworks that ensure a consistent base IRI. But it’s often simpler to ensure all IRIs are absolute for maximal portability.

For Semantic Flow, defining a clear base IRI policy and consistently minting absolute IRIs would help maintain reliability across datasets, even if they’re hosted or reused in different environments.

Sorry, when I say relative URIs, I mean like "./index.trig"^^xsd:anyURI

Ah, got it! Using xsd:anyURI literals like "./index.trig"^^xsd:anyURI for relative paths can work in RDF, but it does come with a few challenges:

1. Literal Interpretation: RDF systems treat these as literals, not actual IRIs. This means they won’t resolve or dereference like IRIs do, and any tool processing these literals would need to interpret them manually (e.g., prepend a base URI).

2. Limited Native Support: Since xsd:anyURI literals are simply treated as strings, RDF databases and query engines usually won’t handle them as links. Most systems won’t automatically resolve or follow these relative paths, which could reduce interoperability.

3. Custom Handling: If you need relative paths to be resolved contextually (e.g., by Semantic Flow or a compatible system), you can write custom scripts or queries to manage the resolution process by concatenating a base IRI. This could involve:

   • Including a metadata statement that specifies the base for these xsd:anyURI literals.
   • Adding a custom parser in Semantic Flow to interpret and expand these paths before processing.

For portability, many RDF systems use absolute IRIs by default, but xsd:anyURI literals with relative paths can work if you're controlling the environment and handling resolution manually.

every individual (including the top level sf:Namespace) should have at least one catalog. So e.g. "/ns/cat/default" would be the "default catalog" for the ns namespace.

Understood. Setting up a default catalog for each namespace as a standard will help ensure consistent organization and provide a reliable entry point for referencing or extending data within each namespace. If you need help formalizing how these default catalogs are linked or accessed, just let me know!

where DatasetSeries and Datasets fit in the hierarchy

dave

I'm having trouble clarifying the relationship between DatasetSeries and Dataset and how they would translate into an IRI hierarchy. I'm guessing each NamedIndividual could optionally have one or more datasets or DatasetSeries "embedded" within it. Kosmion might have a recommended best-practice for this IRI layout, although sf should be neutral.

chatgpt

For Kosmion, here’s one possible best-practice layout for Datasets and DatasetSeries that could keep things consistent without imposing structure via Semantic Flow:

1. NamedIndividual IRI Structure:

   • A NamedIndividual could be the top-level resource, e.g., https://example.org/IndividualX/.
   • The DatasetSeries and Datasets would nest under this IRI, allowing all related resources to stay within the individual’s namespace.

2. DatasetSeries Layer:

   • DatasetSeries IRIs could group Datasets by versioning or logical subsets, e.g., https://example.org/IndividualX/dataset-series/seriesA/.
   • This series might represent one dimension of the resource’s history (like different chronological series or thematic collections).

3. Dataset Layer:

   • Within each DatasetSeries, individual Datasets could have their own IRIs, like https://example.org/IndividualX/dataset-series/seriesA/v1/ or https://example.org/IndividualX/dataset-series/seriesA/v2/.
   • Each Dataset could be a version within the series, capturing a snapshot or update.

4. Catalog Layer (if applicable):

   • If Kosmion recommends catalogs, these could reside at https://example.org/IndividualX/catalogs/, linking all DatasetSeries or specific datasets.

This hierarchy maintains flexibility for different levels of granularity (e.g., a Dataset directly under an individual or nested within a series), and Semantic Flow can stay neutral by supporting varied configurations of these IRIs. This approach also gives clarity in Kosmion when recommending practices for temporal or thematic organization of data.

CatalogSeries

dave

This setup aligns well with the ECS (Entity Component System) pattern and allows clear separation and versioning of data by defining all non-individual components as DatasetSeries. Introducing ko:CatalogSeries as a subclass will give you an easy way to distinguish catalogs from other DatasetSeries, allowing specific handling for catalog structures while keeping everything unified under the DatasetSeries model.

This approach also opens up flexibility for defining additional subclasses, like ko:TemporalSeries or ko:ThematicSeries, if you need specialized groupings.

chatgpt

This setup aligns well with the ECS (Entity Component System) pattern and allows clear separation and versioning of data by defining all non-individual components as DatasetSeries. Introducing ko:CatalogSeries as a subclass will give you an easy way to distinguish catalogs from other DatasetSeries, allowing specific handling for catalog structures while keeping everything unified under the DatasetSeries model.

This approach also opens up flexibility for defining additional subclasses, like ko:TemporalSeries or ko:ThematicSeries, if you need specialized groupings.

PAV

dave

Is PAV ontology obsoleted by dcat v3? It's got pav:hasCurrentVersion would would be useful, I don't think dcat has an analog

chatgpt

Using PAV alongside DCAT sounds like a strong approach here, especially if you're tracking precise versions or maintaining current and previous versions in a detailed way. If you need further granularity in versioning control within Semantic Flow or Kosmion, PAV’s properties would complement DCAT’s cataloging structure well.

what's are good abbreviations for dataset and datasetseries?

Good abbreviations for Dataset and DatasetSeries that stay compact while being clear might be:

• Dataset: ds or dt (e.g., ko:ds for kosmion:Dataset)
• DatasetSeries: dss or dts (e.g., ko:dss for kosmion:DatasetSeries)

If you’re using both frequently and need a sharp distinction, ds for Dataset and dss for DatasetSeries could be intuitive, with the added s helping indicate "series." Alternatively, dt and dts add clarity if distinguishing the two across systems or contexts.

Would either of those work in the context of Semantic Flow?

graph element in IRI namespace

dave

So each individual's dss/default can just contain "best of" selections from current catalog and other datasets. Any issues with putting that data within corresponding named graphs inside the current "default" dataset? And then it can either be duplicated or "owl:imports"-ed. But I think we'll need a "graphs" element in the IRI hierarchy.

Structuring each individual’s dss/default as a “best of” selection within named graphs inside the default dataset should work well, especially if each named graph is accessible by a graphs path element in the IRI hierarchy. This setup would allow flexible referencing, with each graph acting as a modular unit that can be selectively imported or duplicated.

A few considerations for this approach:

1. Data Redundancy: Duplicating data across datasets for consistency checks is practical but could add overhead. Using owl:imports strategically would reduce redundancy, though it requires careful version management.

2. Granularity of Graphs: If each graph under graphs is clearly delineated by purpose (e.g., catalog-based selections, curated views), this will make querying and maintenance easier.

3. IRI Clarity: Ensuring the graphs element has a consistent and predictable structure across datasets will keep this hierarchy intuitive.

With a graphs element in place, you’ll have a solid foundation for both selective data inclusion and versioned views, adding a lot of flexibility to Semantic Flow’s design.

I think that "releasing" a new version of any component of a namespace requires a version bump all the way up the chain

Bumping versions up the chain when any graph or component is updated will help keep the namespace versioning clear and prevent inconsistencies in referencing. It also establishes a strict version history, making it easier to trace data lineage and manage dependencies. This approach is especially beneficial if you’re using catalogs or datasets with shared elements across multiple graphs—each version change in a subcomponent would propagate up, ensuring that consumers of the namespace always have a coherent snapshot of the data structure at any given version.

Do you plan to automate this versioning chain, or would it be handled manually?

automation all the way!

Full automation will streamline the versioning process nicely! Implementing a trigger-based system, where any modification in a graph flags its parent for a version increment, would keep the whole namespace in sync without manual intervention. If you want suggestions on specific tools or scripts to automate that, just let me know.

default graph: union, merge, or intersection

dave

I know there's some debate about whether the default graph should include the union or the ??? of the named datasets. ??? isn't combination but something close.

chatgpt

The term you’re looking for might be the "merge" or "intersection" of named datasets:

1. Union: This includes all triples from each named graph in the default graph, but it risks combining unrelated data, which can lead to ambiguity.

2. Merge: Unlike a union, a "merge" would consolidate overlapping triples, ideally eliminating duplicate or redundant data without changing its meaning. This approach respects triples that appear in multiple graphs without duplicating them in the default graph.

3. Intersection: This is sometimes applied in cases where you want only the shared triples (those appearing in all named graphs) to appear in the default graph. It’s more restrictive and can help to ensure consistency across datasets, though it’s rarely used as the default behavior.

Whether you choose union, merge, or intersection depends on how you want to handle overlap and redundancy in the default graph. Merge is likely the term that best captures your intent if you're aiming to unify but also clarify without duplicating across named datasets.

is there common vocab for NamedGraph class?

There isn't a universally adopted vocabulary specifically for a NamedGraph class in RDF, but a few approaches are common:

1. RDF Data Shapes (SHACL):

   • SHACL doesn't define NamedGraph directly but allows grouping statements and applying shapes to subsets of data. SHACL could, therefore, support defining graph shapes if you treat each named graph as a data shape.

2. PROV-O (Provenance Ontology):

   • prov:Bundle is often used to describe named graphs in RDF. In PROV-O, a Bundle is a set of statements grouped with shared metadata, much like a named graph. You could apply prov:Bundle to represent named graphs with metadata on provenance, contributors, etc.

3. SPARQL Service Description:

   • The SPARQL Service Description vocabulary provides sd:NamedGraph, which is useful for describing graphs in a SPARQL dataset. sd:NamedGraph can include metadata about the graph and its relationships within the dataset, though it's often specific to SPARQL endpoint documentation.

4. DCAT (Data Catalog Vocabulary):

   • Although DCAT doesn’t specify a NamedGraph class, dcat:Dataset or dcat:Distribution can sometimes be applied to represent a named graph as a discrete entity, particularly when using DCAT to catalog subsets of RDF data.

5. Custom Ontology:

   • For your use case, a custom NamedGraph class (e.g., sf:NamedGraph) could be effective, especially if you’re planning to include Semantic Flow-specific metadata, relationships, or processing steps for each graph. This approach allows maximum flexibility and alignment with your system’s semantics.

For broader compatibility, using prov:Bundle or sd:NamedGraph would be the most common if you're aiming for interoperability with other RDF tools.

named graph lack

dave

The wikipedia article on named graphs says "Named graphs are a simple extension of the RDF data model[2] through which graphs can be created but the model lacks an effective means of distinguishing between them once published on the Web at large." what is the lack?

chatgpt

The "lack" Wikipedia refers to is the RDF model’s absence of built-in, standardized mechanisms for distinguishing or authenticating named graphs once they are published on the Web. Specifically, RDF named graphs face two key limitations:

1. Unique Identification of Named Graphs Across Datasets:

   • RDF uses IRIs to identify named graphs within a specific dataset, but once the graph is published or shared on the Web, other datasets might reuse the same IRI without any inherent mechanism for differentiating between identical names in different contexts. This means that while the IRI can act as an identifier within a local or controlled environment, it doesn’t guarantee global uniqueness without external validation or namespaces.

2. Lack of Semantic and Contextual Distinctions:

   • Named graphs themselves don’t carry semantic context about their origin, author, or source, meaning there’s no intrinsic way to know whether two identically named graphs come from the same authoritative source, differ by version, or represent identical information. Without external metadata, like provenance information from prov:wasAttributedTo or prov:generatedAtTime, it’s difficult to ensure that graphs published under the same name hold consistent or trusted data.

This lack of intrinsic distinction creates challenges in Web contexts, especially when graphs from multiple sources must be trusted, authenticated, or identified independently. This is why provenance ontologies (like PROV-O) and SPARQL Service Description are commonly used to add metadata about the graph’s origin, timestamp, and authority, distinguishing it meaningfully in decentralized environments.

authority

dave

ideally there could be some kind of signature that affirms "the dataset published to this IRI was authorized by a person who has control of the namespace" but maybe its existance at that URL speaks for itself.

chatgpt

You’re right; having a signature or verification mechanism would be ideal for authenticating that a dataset is legitimate and authorized by the namespace owner. Here’s a couple of ways this could be approached:

1. Digital Signatures:

   • A digital signature approach could attach a cryptographic signature to the RDF dataset or graph file itself. Tools like RDF Dataset Canonicalization and Linked Data Proofs (part of the decentralized web standards) support signing RDF graphs so that they can be verified by any consumer against the namespace owner’s public key. This makes it clear the dataset was authorized by the rightful owner.

2. Signature Metadata:

   • Another option is to add a metadata statement within the dataset, such as prov:wasAttributedTo or a custom property like ex:signedBy, that links the dataset to a verifiable identity or public key (e.g., a DID). This way, consumers can trace the dataset back to the namespace authority.

3. Trust-by-Location:

   • As you mentioned, in practice, the mere presence of the dataset at the IRI often signals authenticity, especially if the IRI is tightly controlled (e.g., via GitHub Pages for your namespace). However, in decentralized contexts, this approach might not always convey trustworthiness since URLs can be spoofed or hijacked.

For Semantic Flow, using digital signatures or a trust-by-location model should work well. If you’re managing namespaces on a platform like GitHub, the hosting URL itself provides some level of assurance, especially if there are no intermediary redirections, though adding a lightweight verification (like a public signing key in metadata) would boost long-term confidence.

changes at IRI root

dave

maybe changes happen at the current dataset location, and the commit triggers a copying of that data to a versioned dataset into a dataset series and updates catalog data as appropriate.

chatgpt

This workflow would keep the current dataset easily editable while ensuring each versioned dataset in the series captures a snapshot at specific points in time. Automating the catalog update when a new version is created will keep all references and catalogs consistent. It also streamlines version tracking by maintaining a single point for real-time edits and committing changes through a versioning mechanism that feeds into the series. This setup should work smoothly, especially with commit-triggered automation to minimize manual intervention.

HistoryDatasetSeries location 1

For a HistoryDatasetSeries associated with a NamedIndividual like https://djradon.github.io/ns/dave-richardson, a logical IRI could be:

• https://djradon.github.io/ns/dave-richardson/dss/history

Here’s a breakdown of this structure:

• /dss/: A path segment dedicated to DatasetSeries (dss), making it clear this part of the hierarchy is for different DatasetSeries associated with dave-richardson.
• /history: Specifies this particular series is the history series, implying a chronological or revision-focused dataset.

If you might have multiple types of dataset series (e.g., other types like “temporal” or “metadata”), you could further specialize with something like:

• https://djradon.github.io/ns/dave-richardson/dss/history/versions

This would enable separate paths within history to capture specific versions or chronological records as needed.

HistoryDatasetSeries location 2

dave

I was considering https://djradon.github.io/ns/dave-richardson/history for the HistoryDatasetSeries and the HistoricalDataset https://djradon.github.io/ns/dave-richardson/history/v1

chatgpt

Your approach keeps the IRI structure clean and straightforward, especially by using history directly under the NamedIndividual IRI. This layout clearly distinguishes between the HistoryDatasetSeries (at /history) and its individual versions (/history/v1, /history/v2, etc.), making it easy to extend with additional datasets as needed.

This structure will also work well for a queryable, versioned setup, where consumers can access the series itself or specific historical snapshots without confusion.

distribution IRIs

dave

And then for the distribution IRIs, https://djradon.github.io/ns/dave-richardson/dave-richardson.ttl and https://djradon.github.io/ns/dave-richardson/dave-richardson.ttl, and then https://djradon.github.io/ns/dave-richardson/history/v1/dave-richardson-v1.ttl and https://djradon.github.io/ns/dave-richardson/history/v1/dave-richardson-v1.jsonld

chatgpt

This setup provides a clear and organized structure for accessing different dataset formats. Each distribution IRI is specific and easy to navigate, ensuring that users and applications can reliably retrieve datasets in the preferred format, whether they’re accessing the latest data or a specific historical version. This layout will also streamline content negotiation if you expand to other formats in the future.

catalog IRIs

dave

and then the catalog IRIs: https://djradon.github.io/ns/dave-richardson/catalog/ with distributions https://djradon.github.io/ns/dave-richardson/catalog/dave-richardson-catalog.ttl and https://djradon.github.io/ns/dave-richardson/catalog/dave-richardson-catalog.jsonld, and then for catalog versions, https://djradon.github.io/ns/dave-richardson/catalog/history/v1/ with distributions https://djradon.github.io/ns/dave-richardson/catalog/history/v1/dave-richardson-catalog-v1.ttl and https://djradon.github.io/ns/dave-richardson/catalog/history/v1/dave-richardson-catalog-v1.jsonld

chatgpt

This structure for catalog IRIs and distributions provides a robust and clear versioning system. Each catalog and its distributions are consistently accessible by both format and version, supporting ease of navigation and retrieval for current and historical catalog data. This setup will work well for managing updates over time while keeping everything within a logical IRI hierarchy.

automated discovery files with owl:imports

dave

So still to resolve: the distributions have the actual data... Should I have an "https://djradon.github.io/ns/dave-richardson/index.ttl" file that owl:imports the distribution and catalog? When humans try to dereference the IRI, they get an index.html "dereference page" so I feel like software should get an analagous page to aid discovery.

chatgpt

Creating an index.ttl that owl:imports the distribution and catalog makes sense, as it provides a central reference point for software. This setup allows software to quickly identify available data and resources through a single, discoverable entry point. Just like index.html for humans, index.ttl can serve as the software’s entry, linking to the relevant distributions and catalogs and aiding automated processes in finding the right datasets and catalogs efficiently.

Would you like to include additional metadata or other helpful discovery properties in index.ttl?

automated discovery files with references

dave

Instead of using owl:imports, I could just reference the files with custom properties. owl:imports requires another round-trip for the software anyhow, so might as well be specific? Or better to to assume that software won't know my custom properties and use owl:imports instead?

chatgpt

Using custom properties for referencing files directly can be more efficient because it avoids the extra round-trip that owl:imports requires. However, this approach does come with some trade-offs:

1. Efficiency vs. Interoperability:

   • Custom properties streamline discovery since you can directly point to specific datasets or catalogs without relying on additional requests.
   • But, if software expects owl:imports for compatibility, it might skip over custom properties unless specifically programmed to recognize them.

2. Balancing Standards with Specificity:

   • You could achieve both by using owl:imports in cases where compatibility is crucial and adding custom properties like :hasCatalog or :hasDatasetDistribution to link directly to distributions. This allows any specialized software (like tools within Semantic Flow) to recognize and act on the specific properties while still supporting general-purpose tools expecting owl:imports.

3. Example Hybrid Approach:

   • In index.ttl, you might have:

     :dave-richardson a owl:NamedIndividual ;
       owl:imports <https://djradon.github.io/ns/dave-richardson/catalog> ;
       :hasCatalog <https://djradon.github.io/ns/dave-richardson/catalog/dave-richardson-catalog.ttl> ;
       :hasDatasetDistribution <https://djradon.github.io/ns/dave-richardson/dave-richardson.ttl> .
     

   This way, owl:imports maintains broader compatibility, while custom properties allow efficient, direct access. If your system doesn’t rely on third-party tools expecting owl:imports, though, then custom properties alone would be perfectly fine.

Custom properties requires a round trip too.

You're right; whether using owl:imports or custom properties, both approaches would still require a round trip if the importing system has to fetch the referenced resources.

Here are some alternative ideas to minimize round trips:

1. Embed Key Metadata: Include enough metadata within index.ttl itself to make it a standalone discovery document. This way, even if software doesn’t fetch additional resources immediately, it has a complete picture of the datasets, catalogs, and distributions available.

2. Concatenated File: For maximum efficiency, you could create a combined .ttl or .jsonld file that includes the index content as well as the core contents of the main catalog and datasets. This eliminates the need for multiple fetches at the cost of a potentially larger single file.

3. Incremental Loading with Shallow References: You could use a minimal form of owl:imports with shallow loading—loading only essential references initially, then selectively importing further if needed. For example, index.ttl might load just summary metadata of each dataset, with specific files like catalogs and distributions fetched later if necessary.

Would you be interested in a specific structure for an embedded or concatenated document?

special IRIs

dave

I feel like the "history" and "catalog" IRIs are special, and might cause namespace clashes. How can we make them special? I believe it's hard to start IRI local parts with special characters.

chatgpt

Yes, you’re right—it’s tricky to avoid namespace clashes with common terms like "history" and "catalog" if they’re used directly as path segments. Here are some ways to make these IRIs distinctive:

1. Prefix with a Unique Identifier:

   • Use a consistent prefix, like _ or @ (though it can be tricky for some systems) to mark “special” paths:
     • https://djradon.github.io/ns/dave-richardson/_history
     • https://djradon.github.io/ns/dave-richardson/_catalog
   • This approach sets these terms apart as unique or reserved paths without risking collision.

2. Suffix-Based Specialization:

   • Another option is to add a recognizable suffix for special IRIs:
     • https://djradon.github.io/ns/dave-richardson/history-ds (for "dataset series")
     • https://djradon.github.io/ns/dave-richardson/catalog-reserved
   • Suffixes can also distinguish specific versions or dataset types without altering the core structure.

3. Designate a Distinct "Special" Segment:

   • Create a unique segment solely for reserved paths, so only certain terms appear under it:
     • https://djradon.github.io/ns/dave-richardson/special/history
     • https://djradon.github.io/ns/dave-richardson/special/catalog
   • This “special” segment would clarify that all terms within it are reserved, reducing confusion.

4. Use a Non-Alphanumeric Character in the Path:

   • Although some systems may limit non-alphanumeric characters, hyphens or underscores are usually safe:
     • https://djradon.github.io/ns/dave-richardson/history-series/
     • https://djradon.github.io/ns/dave-richardson/catalog-data/

Of these, a prefix (like _history) or dedicated “special” segment could be the most effective for conveying the uniqueness and avoiding future clashes. Would either of those options work for your namespace?

Is it legal to start a IRI local part with an underscore?

Yes, it is technically legal to start the local part of an IRI (after the base path) with an underscore. However, there are some caveats:

1. IRI Syntax: IRIs, unlike XML element names, can legally start with an underscore, as they follow the URI specification (RFC 3987) which permits underscores in any position.

2. Semantic Web Conventions: While legal, starting an IRI local part with an underscore can sometimes cause issues in certain RDF tools or SPARQL queries, as underscores are conventionally used for blank nodes (e.g., _bnode). This can sometimes lead to confusion, although this isn’t a strict technical limitation.

3. Common Practice: It’s generally recommended to avoid leading underscores in IRIs unless you’re sure that downstream systems or users won’t interpret them incorrectly. Using _ within, but not as the first character, is often safer, e.g., history_series.

So while valid, it might be more interoperable to use another structure (like a prefix path segment) to avoid ambiguity.

blank nodes start with _:bnode so I'm leaning towards using it as a prefix, e.g. _history

Using _history as a prefix is a practical approach! This keeps it distinct from blank nodes (which use _:) while signaling that it’s a reserved path. It’s also unlikely to clash with common terms, making it easier to manage and interpret. This convention will help preserve clarity in both Semantic Flow and external systems.

does jsonld support named graphs

Yes, JSON-LD does support named graphs, using a @graph key within a defined context. Here’s how named graphs work in JSON-LD:

1. Basic Structure:

   • Named graphs in JSON-LD are typically represented by associating a @graph property with an IRI or a blank node identifier, allowing each named graph to contain its own set of triples.

2. Using @id for Named Graphs:

   • You specify the graph’s IRI by associating it with @id, and then add a @graph key that includes the triples for that named graph. Here’s an example:

     {
       "@context": {
         "ex": "https://example.org/"
       },
       "@graph": [
         {
           "@id": "ex:namedGraph1",
           "@graph": [
             { "@id": "ex:subject1", "ex:predicate": "Object1" },
             { "@id": "ex:subject2", "ex:predicate": "Object2" }
           ]
         },
         {
           "@id": "ex:namedGraph2",
           "@graph": [
             { "@id": "ex:subject3", "ex:predicate": "Object3" }
           ]
         }
       ]
     }
     

   • Each @graph within @graph entries represents a named graph, with the IRI given by @id.

3. Using Blank Nodes as Named Graphs:

   • You can also use blank nodes as identifiers for named graphs if the graph doesn’t need a global IRI. This can be done by omitting @id or using a blank node identifier, though it may be less interoperable if the graph needs to be uniquely referenced.

4. Nested Named Graphs:

   • JSON-LD doesn’t natively support deeply nested named graphs (e.g., graphs within graphs). Each @graph is treated as an independent entity and doesn’t naturally nest further within other @graph structures.

JSON-LD’s approach works well for representing collections of graphs, each with its own identity, and is compatible with RDF tools that interpret named graphs in JSON-LD.

should I use the GRAPH keyword in trig?

No, you shouldn't use the GRAPH keyword in TriG; it's not part of the TriG syntax. In TriG, named graphs are indicated by simply specifying the graph IRI directly before the { } block containing the triples, like this:

<https://example.org/namedGraph> {
  <https://example.org/subject> <https://example.org/predicate> "Object" .
}

TriG is designed specifically for representing named graphs in a compact form, so the GRAPH keyword from SPARQL isn’t necessary or valid. The graph’s identity is implicitly clear from the IRI preceding the { } block.

_default DatasetSeries

dave

okay, I'm reviving the "_default" datasetseries concept, which is the primary source for data about the NamedIndividual. "_history" is out, as the history is implicit with version numbers. NamedIndividual metadata will refer to the versioned dataset (e.g. _default/v1), Catalog metadata will refer to the catalogdataset (e.g. _catalog/v1). I think that takes care of endless recursion

chatgpt

This streamlined structure should work well for handling versioning and dataset references without falling into recursive loops. By anchoring each NamedIndividual to its _default series and avoiding a dedicated _history, you’re keeping the hierarchy clean and intuitive, with versioning handled explicitly by path. Referring directly to specific versions in metadata will also help maintain clarity across updates.

version-first or datasetseries first

dave

The question I have next is, should I use "dave-richardson/v1/_default" and "dave-richardson/v1/_catalog" or, "dave-richardson/_default/v1/" and ""dave-richardson/_catalog/v1/"

chatgpt

Both IRI structures have their strengths, so it depends on your priorities for organization and future flexibility. Here’s a breakdown:

1. dave-richardson/v1/_default and dave-richardson/v1/_catalog:

   • Pros:
     • Groups all version-specific data together under v1, which could make it easier to manage, archive, or snapshot by version.
     • Simplifies version browsing; any tooling or API could simply scan dave-richardson/v1/ to get all data for that version.
   • Cons:
     • Makes it less straightforward to locate the current _default or _catalog datasets independently, as they’re nested under version identifiers.

2. dave-richardson/_default/v1/ and dave-richardson/_catalog/v1/:

   • Pros:
     • Keeps _default and _catalog as independent concepts, making it clear they are distinct series with their own versions.
     • This structure might support future extensions if you need additional series without ambiguity.
   • Cons:
     • The top-level organization is less “version-centric,” which could make version-based queries slightly less streamlined compared to the first structure.

Recommendation:

If version-centric management (e.g., tracking all data for a single version) is your priority, go with dave-richardson/v1/_default and dave-richardson/v1/_catalog. Otherwise, if series independence is more critical and you expect _default and _catalog to evolve differently, then dave-richardson/_default/v1/ is the better choice.

Let me know if you’d like to explore the pros and cons further, or if one of these structures resonates more with your vision for Semantic Flow.

Could '_catalog' and '_default' be merged?

chatgpt

Yes, merging _catalog and _default could simplify the structure, especially if their roles overlap in significant ways. Here’s how it might work and some considerations:

Benefits of Merging _catalog and _default:

1. Simplified IRI Structure:

   • By combining them, you reduce the IRI complexity and keep all metadata under one primary path, like dave-richardson/_default/v1/.
   • You’d only need to manage one DatasetSeries for both metadata and main content, making it more straightforward for consumers to retrieve all relevant data.

2. Unified Versioning:

   • When _catalog and _default are combined, each version (e.g., v1, v2) inherently includes the catalog and metadata for that version, avoiding the need to cross-reference separate versions for data and catalog metadata.

3. Less Redundancy:

   • Catalogs generally organize and describe datasets, so if _default is the primary source of data about the NamedIndividual, it can naturally serve as its own catalog, minimizing the need for two parallel datasets.

Potential Drawbacks:

1. Less Granular Control:

   • Keeping _catalog separate gives you finer control over catalog-specific metadata (e.g., version tracking, description) versus primary data. Merging might limit your ability to manage or access catalog details independently.

2. Scaling Complexity:

   • If you anticipate needing to add significantly different types of catalog data (e.g., thematic collections, secondary catalogs), having separate datasets might still be beneficial for clarity.

Recommendation:

If the catalog’s primary purpose is simply to describe and reference the current state of data in _default, merging is practical. You could treat _default as the authoritative dataset, and include catalog metadata directly within each version’s file (e.g., dave-richardson/_default/v1.ttl). However, if cataloging needs will likely grow in complexity, keeping _catalog separate might ultimately provide better scalability.

we'll keep it separate

Keeping _catalog and _default separate provides the flexibility for future expansion and ensures each has its own clear, dedicated purpose. This approach should make version management and cataloging more straightforward in the long run. Let me know if you need further setup guidance or integration strategies!

should pav:importedFrom reference a distribution or a dataset IRI

chatgpt

The choice of whether pav:importedFrom should reference a distribution or a dataset IRI depends on the granularity and specificity of the provenance you need:

1. Reference a Distribution IRI:

   • Pros: If you reference a distribution, you get precise provenance, pointing to the exact file format or version used (e.g., Turtle, JSON-LD). This is particularly useful if you need to track the format or specific state of the imported data.
   • Cons: This can be overly specific if the distribution format doesn’t matter, or if you’d rather generalize provenance for all formats.

2. Reference a Dataset IRI:

   • Pros: Referencing the dataset IRI (e.g., https://example.org/my-dataset) is more abstract and can be useful for capturing the source concept without locking down format details. It’s ideal if the data’s origin matters more than the specific distribution.
   • Cons: This lacks the granularity to specify an exact version or format, which could limit traceability if you later need to identify precisely what was imported.

Recommendation:

Use pav:importedFrom to reference a distribution IRI if precision matters (e.g., https://example.org/my-dataset/ttl/v1). Otherwise, reference a dataset IRI for more generalized provenance. If both levels of detail are useful, consider including additional metadata, such as dct:format or pav:version, to enrich provenance for whichever IRI you choose.

namespace down a level

dave

tried metalsmith-ldschema, which underscored the point that the docs folder/site might need some javascript and templates and assets that could interfere with the namespace, so the actual namespace will probably need to live the next level down

chatgpt

This approach makes sense, as keeping the namespace a level below the docs folder will help avoid any conflicts with site assets. You’ll have a clean separation between the static site elements (JavaScript, templates, etc.) and the namespace resources, reducing the risk of unintended interference. Let me know if you need any guidance on structuring the namespace at that new level!

at least that opens up the possibility of multiple namespaces served at a single sflow site

chatgpt

That's a smart adjustment! With the extra layer, you can handle multiple namespaces on a single site, each with its own structure, assets, and potential customizations. This flexibility will make Semantic Flow more adaptable if you expand to serve diverse namespaces in the future.

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