Humbly Report: Sean Bechhofer

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Archive for the ‘linked data’ Category

All the World’s a Stage

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Jason Groth Wigs Out

Anyone who knows me is probably aware of the fact that I’m a keen amateur* musician. So I was very pleased to be able to work on a musical dataset while spending some sabbatical time at OeRC with Dave De Roure. The project has been focused around the Internet Archive‘s Live Music Archive. The Internet Archive is a “non-profit organisation building a library of internet sites and other cultural artifacts in digital form”. They’re the folks responsible for the Way Back Machine, the service that lets you see historical states of web sites.

The Live Music Archive is a community contributed collection of live recordings with over 100,000 performances by nearly 4,000 artists. These aren’t just crappy bootlegs by someone with a tapedeck and a mic down their sleeve either — many are taken from direct feeds off the desk or have been recorded with state of the art equipment. It’s all legal too, as the material in the collection has been sanctioned by the artists. I first came across the archive several years ago — it contains recordings by a number of my current favourites including Mogwai, Calexico and Andrew Bird.

Our task was to take the collection metadata and republish as Linked Data. This involves a couple of stages. The first is to simply massage the data into an RDF-based form. The second is to provide links to existing resources in other data sources. There are two “obvious” sources to target here, MusicBrainz, which provides information about music artists, and GeoNames, which provides information about geographical locations. Using some simple techniques, we’ve identified mappings between the entities in our collection and external resources, placing the dataset firmly into the Linked Data Cloud. The exercise also raised some interesting questions about how we expose the fact that there is an underlying dataset (the source data from the archive) along with some additional interpretations on that data (the mappings to other sources). There are certainly going to be glitches in the alignment process — with a corpus of this size, automated alignment is the only viable solution — so it’s important that data consumers are aware of what they’re getting. This also relates to other strands of work about preserving scientific processes and new models of publication that we’re pursing in projects like wf4ever. I’ll try and return to some of these questions in a later post.

So what? Why is this interesting? For a start, it’s a fun corpus to play with, and one shouldn’t underestimate the importance having fun at work! On a more serious note, the corpus provides a useful resource for computational musicology as exemplified by activities such as MIREX. Not only is there metadata about large number of live performances with links to related resources, but there are links to the underlying audio files from those performances, often in hgh quality audio formats. So there is an opportunity here to combine analysis of both the metadata and audio. Thus we can potentially compare live performances by individual artists across different geographical locations. This could be in terms of metadata — which artists have played in which locations (see the network below) and does artist X play the same setlist every night? Such a query could also potentially be answered by similar resources such as The presence of the audio, however, also offers the possibility of combining metadata queries with computational analysis of the performance audio data — does artist X play the same songs at the same tempo every night, and does that change with geographical location? Of course this corpus is made up of a particular collection of events, so we must be circumspect in deriving any kind of general conclusions about live performances or artist behaviour.

Who Played Where?

The dataset is accessible from There is a SPARQL endpoint along with browsable pages delivering HTML/RDF representations via content negotation. Let us know if you find the data useful, interesting, or if you have any ideas for improvement. There is also a short paper [1] describing the dataset submitted to the Semantic Web Journal. The SWJ has an open review process, so feel free to comment!


  1. Sean Bechhofer, David De Roure and Kevin Page. Hello Cleveland! Linked Data Publication of Live Music Archives. Submitted to the Semantic Web Journal Special Call for Linked Dataset Descriptions.

*Amateur in a positive way in that I do it for the love of it and it’s not how I pay the bills.


Written by Sean Bechhofer

May 23, 2012 at 1:23 pm

Posted in linked data, music, rdf

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Whale Shark 2.0

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The fishDelish project is a JISC funded collaboration between the University of Manchester, Hedtek Ltd and the FishBase Information and Research Group Inc. FishBase is a “a global information system with all you ever wanted to know about fishes.” FishBase is available as a relational database and the project is about taking that data and republishing as RDF/Linked Data. The project is nearing the end, and we now have the FishBase data in a triple store. I took a look at how we could generate some nice looking species pages. FishBase currently offers pages presenting information about species (for example the Whale Shark).

Whale Shark on FishBase

I wanted to try and replicate (some of) this presentation in as simple/lightweight a way as possible. The solution I adopted involves a single SPARQL query that pulls out relevant information about a species, and an XSL stylesheet that transforms the results of that query into an HTML page. The whole thing is tied together with a simple bit of PHP code that executes the SPARQL query (using RAP — a bit long in the tooth, but it does this job), requesting the results as XML. It then uses PHP’s DOMDocument to add a link to the XSL stylesheet into the results. The HTML rendering is then actually handled by the web browser applying the style sheet. The resulting species pages (e.g. the Whale Shark again) are not — to use the words of David Flanders, our JISC Programme Manager — as information rich as the original FishBase pages, but they are sexier.

Whale Shark on fishDelish

To a certain extent, that’s simply down to styling (I’m a big fan of Georgia), but the exercise did help to explore the usage of SPARQL and XSL on the FishBase dataset. The SPARQL queries and stylesheets developed will also be useful in conjunction with the mysparql libraries developed in fishDelish. mySparql is a service developed in fishDelish that allows the embedding of SPARQL queries into pages.

The first problem I faced was trying to understand the structure of the data in the triplestore. The property names produced by D2R are not always entirely, ermm, readable. As my colleague Bijan Parsia discussed in a blog post describing his fishing expeditions, the state of linked data “browsers” is mixed. I ended up using Chris Gutteridge’s Graphite “Quick and Dirty” RDF Browser to help navigate around the data set.

A second question was how to approach the queries. The species pages have a simple structure. They have a single “topic” (i.e. the species), and then display characteristics of that species. So constructing a species page can be seen as a form filling process where the attributes are predetermined. It’s possible to write a SPARQL query to get information about a species with a single row in the results. The stylesheet (e.g. for species) can grab the values out of those results and “fill in the blanks” as required. An alternative would be to use some kind of generic s-p-o pattern in the query and pull out all the information about a particular URI (i.e. the species). In the species case though, we already know what information we’re interested in getting out so the “canned” approach is fine.

I also produced some pages for Orders and Families (e.g. Rhincodontidae or Rajiformes). The SPARQL query here returns a number of rows, as the query asks for all the families in an order or species in a family. There is redundancy in the query result as the first few columns in each row are identical. A cleaner solution here might be to use more than one SPARQL query — one pulling out the family information, one requesting the family/species list. That would require more sophisticated processing though, rather than my lightweight SPARQL query + XSL approach. Again, this is something that the mysparql service would help with.

Overall, this was in interesting experiment and exercise in understanding the FishBase RDF data. Harking back to an earlier blog post from Bijan, as I’m already familiar with SPARQL and XSL, it was probably easier for me to produce these pages using the converted data, but it’s not clear whether that would be true in general. There’s actually very little in here that’s about Linked Data. This could have been done (as the current FishBase pages are done) using the existing relational db plus some queries and a bit of scripting. There was some benefit here in using the standardised protocols and infrastructure offered by SPARQL, RDF, XML and XSL though. It was also very easy for me to do all of this on the client side — all I needed was access to the SPARQL endpoint and some XML tooling. So the real benefit for this particular application is gained from the data publication.

It did help to illustrate the kinds of things we can now begin to do with the RDF data though, and puts us in a situation where we can look at further integration of the data with other data sets. For example it would be nice to hook into resources like the BBC Wildlife Finder pages, which are also packed with semantic goodness.

It was also fun, which is always a good thing! If only the Whale Sharks themselves were as easy to find…..

(This is an edited version of an fishDelish project blog post)

Written by Sean Bechhofer

March 10, 2011 at 10:48 am

Posted in linked data, rdf

Tagged with , ,

Gone Fishin’

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Roman Aquaduct, Segovia

A Fish

The project website is now online. is a collaboration between the University of Manchester School of Computer Science, the Freshwater Biological Association, King’s College London Centre for e-Research and Queen Mary, University of London River Communities Group funded under JISC’s Managing Research Data Programme. The project overview is as follows:

Motivated by the large quantity of diverse data in the freshwater biology community, FISH.Link will provide a demonstrator of the benefits of publishing data by illustrating how data can be combined, repurposed and reused with attribution and provenance information to promote data sharing. The project intends to support the sharing and integration of research data through the application of lightweight vocabularies and vocabulary mapping, facilitating integration of data sets, and moving towards the Web of Data that forms the current Linked Open Data vision.

A case study that addresses a real scientific question will be used to provide motivation, requirements and support evaluation. will produce tools that allow fresh water biologists to publish data in to the Linked Data Cloud. These tools will be integrated into the FISHnet platform that supports the data life cycle in fresh water science and will use SKOS for the representation of vocabularies.

The project will run for 12 months until July 2011.

Written by Sean Bechhofer

August 24, 2010 at 10:04 am

Posted in linked data, projects, skos

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