Dataset and API Discovery in Linked Data

I’ve been recently thinking about how applications can discover additional data and relevant APIs in Linked Data. While there’s been lots of research done on finding and using (semantic) web services I’m initially interested in supporting the kind of bootstrapping use cases covered by Autodiscovery.

We can characterise that use case as helping to answer the following kinds of questions:

  • Given a resource URI, how can I find out which dataset it is from?
  • Given a dataset URI, how can I find out which resources it contains and which APIs might let me interact with it?
  • Given a domain on the web, how can I find out whether it exposes some machine-readable data?
  • Where is the SPARQL endpoint for this dataset?

More succinctly: can we follow our nose to find all related data and APIs?

I decided to try and draw a diagram to illustrate the different resources involved and their connections. I’ve included a small version below:

Data and API Discovery with Linked Data

Lets run through the links between different types of resources:

  • From Dataset to Sparql Endpoint (and Item Lookup, and Open Search Description): this is covered by VoiD which provides simple predicates for linking a dataset to three types of resources. I’m not aware of other types of linking yet, but it might be nice to support reconciliation APIs.
  • From Well-Known VoiD Description (background) to Dataset. This well known URL allows a client to find the “top-level” VoiD description for a domain. It’s not clear what that entails, but I suspect the default option will be to serve a basic description of a single dataset, with reference to sub-sets (void:subset) where appropriate. There might also just be rdfs:seeAlso links.
  • From a Dataset to a Resource. A VoiD description can include example resources, this blesses a few resources in the dataset with direct links. Ideally these resources ought to be good representative examples of resources in the dataset, but they might also be good starting points for further browsing or crawling.
  • From a Resource to a Resource Description. If you’re using “slash” URIs in your data, then URIs will usually redirect to a resource description that contains the actual data. The resource description might be available in multiple formats and clients can content negotiation or follow Link headers to find alternative representations.
  • From a Resource Description to a Resource. A description will typically have a single primary topic, i.e. the resource its describing. It might also reference other related resources, either as direct relationships between those resources or via rdfs:seeAlso type links (“more data over here”).
  • From a Resource Description to a Dataset. This is where we might use a dct:source relationship to state that the current description has been extracted from a specific dataset.
  • From a SPARQL Endpoint (Service Description) to a Dataset. Here we run into some differences between definitions of dataset, but essentially we can describe in some detail the structure of the SPARQL dataset that is used in an endpoint and tie that back to the VoiD description. I found myself looking for a simple predicate that linked to a void:Dataset rather than describing the default and named graphs, but couldn’t find one.
  • I couldn’t find any way to relate a Graph Store to a Dataset or SPARQL endpoint. Early versions of the SPARQL Graph Store protocol had some notes on autodiscovery of descriptions, but these aren’t in the latest versions.

These links are expressed, for the most part, in the data but could also be present as Link headers in HTTP responses or in HTML (perhaps with embedded RDFa).

I’ve also not covered sitemaps at all, which provide a way to exhaustively list the key resources in a website or dataset to support mirroring and crawling. But I thought this diagram might be useful.

I’m not sure that the community has yet standardised on best practices for all of these cases and across all formats. That’s one area of discussion I’m keen to explore further.

5 thoughts on “Dataset and API Discovery in Linked Data

  1. Alex Tucker says:

    Hi Leigh,

    This is good stuff and a handy summary of the current state, nice one.

    For the bootstrap problem of finding SPARQL endpoints in a given domain, we’ve been using DNS Service Discovery for a few years with some success. Essentially, you start at a well-known DNS record, _sparql._tcp.some.domain.com and use the standard DNS SVR, PTR and TXT records to figure out the address, port, and path of your SPARQL endpoints.

    You can try it out using “avahi-browse -d floop.org.uk -r _sparql._tcp” or “avahi-browse -d sparql.openlinksw.com -r _sparql._tcp”

    The idea being to give just enough information to start using SPARQL to query along the lines you mention, or just grab the SPARQL service description and follow your nose.

    We’re in the process of updating our “information discovery” specs to use the SPARQL 1.1 service description vocabulary, so I’d be interested in your thoughts.

    Alex.

  2. [...] Leigh Dodds has drafted a diagram illustrating relationships between resources in an effort to understand and describe Dataset and API discovery in Linked Data. He writes, “While there’s been lots of research done on finding and using (semantic) web services I’m initially interested in supporting the kind of bootstrapping use cases covered by Autodiscovery. We can characterise that use case as helping to answer the following kinds of questions: Given a resource URI, how can I find out which dataset it is from? Given a dataset URI, how can I find out which resources it contains and which APIs might let me interact with it? Given a domain on the web, how can I find out whether it exposes some machine-readable data? Where is the SPARQL endpoint for this dataset?” [...]

  3. [...] my last post highlighted, I’ve been thinking about how we can find and discover datasets and their related [...]

  4. [...] explored some ways that we might find related data and services, as well as different definitions of “dataset”, I wanted to look at the topic of [...]

  5. […] also wanted to make the data and APIs discoverable and adopted a “completionist” approach to try and tick all the boxes for publishing and […]

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