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Posts Tagged ‘Linked Data’

E-Science 2014: The longest Journey

Posted by dgarijov on October 31, 2014

After a few days back in Madrid, I have finally found some time to write about the eScience 2014 conference, which took place last week in Guarujá, Brasil. The conference lasted for 5 days (the first two days with workshops), and it got attendants from all over the world. It was especially good to see many young people who could attend thanks to the scholarships awarded by the conference, even when they were not presenting a paper. I found a bit unorthodox that the presenters couldn’t apply for these scholarships (I wanted to!), but I am glad to see this kind of giveaway. Conferences are expensive and I was able to have interesting discussions about my work thanks to this initiative. I think this is also a reflection of Jim Gray’s will: pushing science into the next generation.

We were placed in touristic resort in Guarujá, at the beach. This is what you could see when you got out of the hotel:

Guarujá beach

Guarujá beach

And the jungle was not far away either. After a 20 minute walk you were able to arrive at something like this…

The jungle was not far from the beach either

The jungle was not far from the beach either

…which is pretty amazing. However, the conference schedule was packed with interesting talks from 8:30 to 20:30 most of the days, and in general we were unable to do some sightseeing. In my opinion they could have reduced one workshop day and relax the schedule a little bit. Or at least remove the parallel sessions in the main conference. It always sucks to have to choose between two different interesting sessions. That said, I would like to congratulate everyone involved in the organization of the conference. They did an amazing job!

Another thing that surprised me is that I wasn’t expecting to see many Semantic Web people, since the ISWC Conference occurred at the same time in Italy, but I found quite a few. We are everywhere!

My talks at the conference were two, which summarized the results I achieved during my internship at the Information Sciences Institute earlier this year. First I presented a user survey quantifying the benefits of creating workflows and workflow fragments and then our approach to detect automatically common workflow fragments, tested in the LONI Pipeline (for more details I encourage you to follow the links to the presentations). The only thing that bothered me a bit was that my presentations were scheduled at strange hours. I had the last turn before the dinner for the first one, and then I was the first presenter the last day at 8:30 am for the second one. Here is a picture of the brave attendants who woke up early the last day, I really appreciated their effort :):

The brave attendants that woke up early to be at my talk at 8:30 am

The brave attendants that woke up early to be at my talk at 8:30 am

But let’s get back to the workshop, demos and conference. As I introduced above, the first 2 days included workshop talks, demos and tutorials. Here are my highlights:

Workshops and demos:

Microsoft is investing on scientific workflows!: I attended the Azure research training workshop, were Mateus Velloso introduced the Azure infrastructure for creating and setting up virtual machines, web services, webs and workflows. It is really impressive how easily you are able to create and run experiments with their infrastructure, although you are limited to their own library of software components (in this case, a machine learning library). If you want to add your own software, you have to expose it as a web service.

Impressive visualizations using Excel sheets at the Demofest! All the demos belonged to Microsoft (guess who was one of the main sponsors of the conference) although I have to admit that they looked pretty cool. I was impressed by two demos in particular, the Sanddance beta and the Worldwide Telescope. The former is used to load Excel files with large datasets to play with the data, select, filter and plot the resources by different facets. Easy to use and very fluid in the animations. The latter was similar to Google Maps, but you were able to load your excel dataset (more than 300K points at the same time) and show it on real time. For example, in the demo you could draw the itineraries of several whales in the sea at different points in time, and show their movement minute after minute.

Microsoft demo session. With caipirinhas!

Microsoft demo session. With caipirinhas!

New provenance use cases are always interesting. Dario Oliveira introduced their approach to extract biographic information from the Brazilian Historical Biographical Dictionary at the Digital Humanities Workshop. This included not only the life of the different persons collected as part of the dictionary, but also each reference that contributed to tell part of the story. Certainly a complex and interesting use case for provenance, which they are currently refining.

Paul Watson was awarded with the Jim Gray Award. In his keynote, he talked about the social exclusion and the effect of digital technologies. Having a lack of ability to log online may stop you from having access to many services, and ongoing work on helping people with accessibility problems (even through scientific workflows) was presented. Clouds play an important role too, as they have the potential for dealing with the fast growth of applications. However, the people who could benefit the most from the cloud often do not have the resources or skills to do so. He also described e-Science Central, a workflow system for easily creating workflows in your web browser, with provenance recording and exploring capabilities and the possibility to tune and improve the scalability of your workflows with the Azure infrastructure. The keynote ended by highlighting how important is to make things fun for the user (“gamification “ of evaluations, for example), and how important eScience is for computer science research: new challenges are continuously presented supported by real use cases in application domains with a lot of data behind.

I liked the three dreams for eScience of the “strategic importance of eScience” panel:

  1. Find and support the misfits, by addressing those people with needs in escience.
  2. Support cross domain overlap. Many communities base their work on the work made by other communities, although the collaboration rarely happens at the moment.
  3. Cross domain collaboration.
First panel of the conference

First panel of the conference

Conference general highlights:

Great discussion in the “Going native Panel”, chaired by Tony Hey, with experts from chemistry, scientific workflows and ornithology (talk about domain diversity). They analyzed the key elements of a successful collaboration, explaining how in their different projects they have a wide range of collaborators. It is crucial to have passionate people, who don’t lose the inertia after the grant from the project has been obtained. For example, one of the best databases for accessing chemicals descriptions on the UK came out from a personal project initiated by a minority. In general, people like to consume curated data, but very few are willing to contribute. In the end what people want is to have impact. Showing relevance and impact (or reputation, altmetrics, etc.) will grant additional collaborators. Finally, the issue of data interoperability between different communities was brought up for discussion. Data without methods is in many cases not very useful, which encourages part of the work I’ve been doing during the last years.

Awesome keynotes!! The one I liked the most was given by Noshir Contractor, who talked about “Grand Societal Challenges”. The keynote was basically about how to assemble a “dream team” of people for delivering a product/proposal, and all the analyses that had been done to determine which factors are the most influential. He started by talking about the Watson team, who built a machine capable of beating a human on TV, and continued by presenting the tendencies people have when selecting people for their own teams. He also presented a very interesting study of videogames as “leadership online labs”. In videogames very heterogeneous people meet, and they have to collaborate in groups in order to be successful. The takeaway conclusion was that diversity in a group can be very successful, but it is also very risky and often it ends in a failure. That is why people tend to collaborate with people they have already collaborated with when writing a proposal.

The keynote by Kathleen R. McKeown was also amazing. She presented a high level overview of the work in NLP developed in their group concerning summarization of news, journal articles, blog posts, and even novels! (which IMO has a lot of merit without going into the detail). She presented co-reference detection of events, temporal summarization, sub-event identification and analysis of conversations in literature, depending on the type of text being addressed. Semantics can make a difference!

New workflow systems: I think I haven’t seen an eScience conference without new workflow systems being presented 😀 In this case the focus was more on the efficient execution and distribution of the resources. Dispel4py and Tigres workflow systems were introduced for scientists working in Python.

Cross domain workflows and scientific gateways:

Antonella Galizia presented the DRIHM infrastructure to set up Hydro-Meteorological experiments in minutes. Impressive, as they had to integrate models for meteorology, hydrology, pluviology and hydraulic systems, while reusing existent OGC standards and developing a gateway for citizen scientists. A powerful approach, as they were able to do flooding predictions on in certain parts of Italy. According to Antonella, one of the biggest challenges on achieving their results was to create a common vocabulary which could be understood by all the scientists involved. Once again we come back to semantics…

Rosa Filgueira presented another gateway, but for vulcanologists and rock physicists. Scientists often have problems to share data among different disciplines, even if they belong to the same domain (geology in this case). This is because every lab often records their data in a different way.

Finally, Silvia Olabarriaga gave an interesting talk about workflow management in astrophysics, heliophysics and biomedicine, distinguishing the conceptual level (user in the science gateway), abstract level (scientific workflow) and concrete level (how the workflow is finally executed on an infrastructure), and how to capture provenance at these different granularities.

Other more specific work that I liked:

  • A tool for understanding the copyright in science, presented by Richard Hoskings. A plethora of different licenses coexist in the Linked Open Data, and it is often difficult to understand how one can use the different resources exposed in the Web. This tool helps on guiding the user about the possible consequences of using a given resource or another in their applications. Very useful to detect any incompatibility on your application!
  • An interesting workflow similarity approach by Johannes Starlinger, which improves the current state of the art by making efficient matching on workflows. Johannes said they would release a new search engine soon, so I look forward to analyzing their results. They have published a corpus of similar workflows here.
  • Context of scientific experiments: Rudolf Mayer presented the work made on the Timbus project to capture the context of scientific workflows. This includes their dependencies, methods and data under a very fine granularity. Definitely related to Research Objects!
  • An agile annotation of scientific texts to identify and link biomedical entities by Marcus Silva, with the particularity of being capable of loading very large ontologies to do the matching.
  • Workflow ecosystems in Pegasus: Ewa Deelman presented a set of combinable tools for Pegasus able to archive, distribute simulate and re-compute efficiently workflows. All tested with a huge workflow in astronomy.
  • Provenance is still playing an important role in the conference, with a whole session for related papers. PROV is being reused and extended in different domains, but I still have to see an interoperable use across different domains to show its full potential.
Conference dinner and dance with a live band

Conference dinner and dance with a live band

In summary, I think the conference has been a very positive experience and definitely worth the trip. It is very encouraging to see that collaborations among different communities are really happening thanks to the infrastructure being developed on eScience, although there are still many challenges to address. I think we will see more and more cross domain workflows and workflow ecosystems in the next years, and I hope to be able to contribute with my research.

I also got plenty of new references to add to the state of the art of my thesis, so I think that I also did a good job by talking to people and letting others know of my work. Unfortunately my return flight was delayed and I missed my connection back to Spain, converting my 14 hour flight home to almost 48 hours. Certainly the longest journey from any conference I have assisted to.

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Elevator pitch

Posted by dgarijov on February 16, 2014

While being a PhD student, many people have asked me about the subject of my thesis and the main ideas behind my research. As a student you always think you have very clear what you are doing, at least until you have to actually explain it to someone who is not related to your domain. In fact, it is about using the right terminology. If you say something like “Oh yeah, I am trying to detect abstractions on scientific workflows semi-automatically in order to understand how they can better be reused and related to each other”, people will look at you as if you didn’t belong to this planet. Instead, something like “detecting commonalities in scientific experiments in order to study how we can understand them bettermight be more appropriate.

But last week the challenge was slightly different. I was invited to give an overview talk about the work I have been doing as a PhD student. And that is not only what I am doing, but why am I doing it and how is it all related without going into the details of every step. It may appear as an easy task, but it kept me thinking more than I expected.

As I think some people might be interested in a global overview, I want to share the presentation here as well: http://www.slideshare.net/dgarijo/from-scientific-workflows-to-research-objects-publication-and-abstraction-of-scientific-experiments. Have a look!

Posted in e-Science, Linked Data, Provenance, Research Object, scientific workflows, Taverna, Tutorial, Wings | Tagged: , , , , , , , , , | Leave a Comment »

How to (properly) publish a vocabulary or ontology in the web (part 6 of 6)

Posted by dgarijov on November 11, 2013

And we finally arrive to the last part of the tutorial, which is a set of guidelines on how to reuse other vocabularies (i.e., how your vocabulary should link to other vocabularies). Reuse is not only related to publication, but also to the design of your own vocabulary. As a researcher, everyone knows that it is better not to reinvent the wheel. If an existent vocabulary covers with its terms part of what you want to cover in your competency questions (or system requirements), why should you redefine the same terms again and again?

In order to avoid this issue, you can either import that vocabulary into yours, which will bring the whole imported vocabulary as part of your ontology (like a module), or you could either extend only those properties and classes that you are going to reuse, without adding all the terms of the reused vocabulary as part of your ontology.

Which way is better? It depends: on one hand, I personally like to extend the vocabularies that I reuse when the terms being expanded are not many. Importing a vocabulary often makes it more difficult to present, and for someone loading the ontology, it could be very confusing to browse across many terms not being used in my domain.

Reusing concepts from other ontologies simplifies your domain model, as you import just those being extended.

Reusing concepts from other ontologies simplifies your domain model, as you import just those being extended.

On the other hand, if you plan to reuse most of the vocabulary being imported, for example by creating a profile of a vocabulary for a specific domain, the import option is the way to go.

Importing an ontology makes it on one hand easy to reuse, but on the other (in some cases) it makes your ontology more difficult to understand.

Importing an ontology makes it on one hand easy to reuse, but on the other (in some cases) it makes your ontology more difficult to understand.

Another advice is to be careful with the semantics. I personally don’t like to mess up with the concepts defined by other people. If you need to add your own properties taking as domain or ranges classes defined by other people, you should specialize those classes in your ontology. Imagine an example where I want to reuse the generic concept from the PROV ontology prov:Entity for refering to the provenance of digital entities (which is my sample domain). If I want to add a property that has domain digital entity (like hasSize), then I should specialize the term prov:Entity with a subclass for my domain (in this case digitalEntity subClassOf Entity). If I just assert properties on the general term (prov:Entity) then I may be overextending my property to other domains than those I may have thought, and what is worse: I may be modifying a model which I haven’t defined originally.

But where to start looking if you want to reuse a vocabulary? There are several options:

  • Linked Open Vocabularies (LOV ): A set of common vocabularies that are distributed and organized in different categories. Different metrics for each vocabulary are displayed regarding its metadata and reuse, which will help you to determine whether it is still in unse or not.
  • The W3C standards: When building a vocabulary it is allways good to look up if a standard on that domain already exists!
  • Swoogle and Watson will allow you to search for terms on your domain and suggest you existent approaches.

With this post the tutorial ends. I hope it served to clarify at least a couple of things regarding vocabulary/ontology publication in the web. If you have any questions please leave them on the comments and I’ll be happy to help you.

Do you want more information regarding ontology importing and reuse? Check out these papers (thanks Maria and Melanie for the pointers):

This is part of a tutorial divided in 7 parts:

  1. Overview of the tutorial.
  2. (Reqs addressed A1(partially), A2, A3, A4, P1) Publishing your vocabulary at a stable URI using RDFS/OWL.
  3. (Reqs addressed P2, P3). How to design a human readable documentation.
  4. Extra: A tool for creating html readable documentation
  5. (Reqs addressed P4). Derreferencing your vocabulary
  6. (Reqs addressed A1 (partially)). Dealing with the license
  7. (Reqs addressed A5, P5). Reusing other vocabularies. (This post)

Posted in Linked Data | Tagged: , , , , | 3 Comments »

How to (properly) publish a vocabulary or ontology in the web (part 4 of 6)

Posted by dgarijov on October 7, 2013

(Update: purl.org seems to have stopped working. I recommend you to have a look at my latest post for doing content negotiation with w3id)

After a long summer break in blogging, I’m committed to finishing this tutorial. In this post I’ll explain why and how to dereference your vocabulary when publishing it in the Web.

But first, why should you dereference your vocabulary? In part 2 I showed how to create a permanent URL (purl) and redirect it to the ontology/vocabulary we wanted to publish (in my case it was http://purl.org/net/wf-motifs). If you followed the example you would have seen that now when you enter the purl you created in the web browser it redirects you to the ontology file. However if you enter http://purl.org/net/wf-motifs you will be redirected to the html documentation of the ontology. When entering the same URL in Protégé, the ontology file will be loaded in the system. By dereferencing the motifs vocabulary I am able to choose what to deliver depending on the type of request received by the server on a single resource: RDF files for applications and nice html pages for the people looking for information about the ontology (structured content for machines, human readable content for users).

Additionally, if you have used the tools I suggested in previous posts, when you ask for a certain concept the browser will take you to the exact part of the document defining it. For example, if you want to know the exact definition for the concept “FormatTransformation” in the Workflow Motif ontology, then you can paste its URI (http://purl.org/net/wf-motifs#FormatTransformation) in the web browser. This makes the life easier for users when browsing and reading your ontology.

And now, how do you dereference your vocabulary? First, you should set the purl redirection as a redirection for Semantic Web resources (add a 303 redirection instead a 302, and add the target URL where you plan to do the redirection). Note that you can only dereference a resource if you control the server where the resources are going to be delivered. The screenshot below shows how it would look in purl for the Workflow Motifs vocabulary. http://vocab.linkeddata.es/motifs is the place our system admin decided to store the vocabulary.

Purl redirection

Purl redirection

Now you should add the redirection itself. For this I always recommend having a look into the W3C documents, which will guide you step by step on how to achieve this. In this case in particular we followed http://www.w3.org/TR/swbp-vocab-pub/#recipe3, which is a simple redirection for vocabularies with a hash namespace. You have to create an htaccess file similar to the one pasted below. In my case the index.html file has the documentation of the ontology, while motif-ontology1.1.owl contains the rdf/xml encoding. If a ttl file exists, you can also add the appropriate content negotiation. All the files are located in a folder called motifs-content, in order to avoid an infinite loop when dealing with the redirections of the vocabulary:

# Turn off MultiViews
Options -MultiViews

# Directive to ensure *.rdf files served as appropriate content type,
# if not present in main apache config
AddType application/rdf+xml .rdf
AddType application/rdf+xml .owl
#AddType text/turtle .ttl #<---Add if you have a ttl serialization of the file

# Rewrite engine setup
RewriteEngine On
RewriteBase /def

# Rewrite rule to serve HTML content from the vocabulary URI if requested
RewriteCond %{HTTP_ACCEPT} !application/rdf\+xml.*(text/html|application/xhtml\+xml)
RewriteCond %{HTTP_ACCEPT} text/html [OR]
RewriteCond %{HTTP_ACCEPT} application/xhtml\+xml [OR]
RewriteCond %{HTTP_USER_AGENT} ^Mozilla/.*
RewriteRule ^motifs$ motifs-content/index.html

# Rewrite rule to serve RDF/XML content from the vocabulary URI if requested
RewriteCond %{HTTP_ACCEPT} application/rdf\+xml
RewriteRule ^motifs$ motifs-content/motif-ontology1.1.owl [R=303]

# Rewrite rule to serve turtle content from the vocabulary URI if requested
#RewriteCond %{HTTP_ACCEPT} text/turtle
#RewriteRule ^motifs$ motifs-content/motifs_ontology-ttl.ttl [R=303]

# Choose the default response
# ---------------------------

# Rewrite rule to serve the RDF/XML content from the vocabulary URI by default
RewriteRule ^motifs$ motifs-content/motif-ontology1.1.owl [R=303]

Note the redirections when the owl is being requested. If you have a slash vocabulary, you will have to follow the aforementioned W3C document for further instructions.

Now it is time to test that everything works. The easiest way is just to paste the URI of the ontology in Protégé and in your browser and check that in one case it loads the ontology properly and in the other you can see the documentation. Another possibility is to use curl like this: curl -sH “Accept: application/rdf+xml” -L http://purl.org/net/wf-motifs (for checking that the rdf is obtained) or curl -sH “Accept: text/html” -L http://purl.org/net/wf for the html.

Finally, you may also use the Vapour validator to check that you have done the process correctly. After entering your ontology URL, you should see something like this:

Vapur validation

Vapur validation

Congratulations! You have dereferenced your vocabulary successfully 🙂

This is part of a tutorial divided in 7 parts:

  1. Overview of the tutorial.
  2. (Reqs addressed A1(partially), A2, A3, A4, P1) Publishing your vocabulary at a stable URI using RDFS/OWL.
  3. (Reqs addressed P2, P3). How to design a human readable documentation.
  4. Extra: A tool for creating html readable documentation
  5. (Reqs addressed P4). Derreferencing your vocabulary (this post)
  6. (Reqs addressed A1 (partially)). Dealing with the license. (To appear)
  7. (Reqs addressed A5, P5). Reusing other vocabularies. (To appear)

Posted in Linked Data, Tutorial | Tagged: , , , , , , | 9 Comments »

How to (properly) publish a vocabulary or ontology in the web (1 of 6)

Posted by dgarijov on May 27, 2013

Vocabularies and ontologies have been developed in the last years for modeling different use cases in heterogeneous domains. These vocabularies/ontologies are often described in journal publications and conferences, which reflect the rationale of the design decisions taken during their development. Now that everyone is talking about Linked Data, I have found myself looking for guidelines to properly publishing my vocabularies on the web, but unfortunately the required documentation is scattered through many different places.

First things first. What do I mean by properly publishing a vocabulary? By that I refer to making it an accessible resource, both human and machine readable, with documentation with examples and with its license specified. In this regard, there have been two initiatives for gathering the requirements I am trying to address in this tutorial: the 5-Star Vocabulary requirements by Bernard Vatant and the AMOR manifesto by Raúl García-Castro. Both of these approaches are based in Tim Berners Lee’s Linked Data 5 star rating, and complement each other. In this tutorial (which will be divided in 5 parts), I will cover possible solutions to address each of their requirements, further described below (quoting the original posts).

Requirements of the AMOR manifesto (A):

  • (A1) The ontology is available on the web (whatever format) but with an open licence
  • (A2) All the above, plus: available as machine-readable structured data (e.g., CycL instead of image scan of a table)
  • (A3) All the above, plus: non-proprietary format (e.g., OBO instead of CycL)
  • (A4) All the above, plus: use open standards from the W3C (RDF Schema and OWL)
  • (A5) All the above, plus: reuse other people’s ontologies in your ontology

– Requirements of the 5 start vocabulary principles (P)

  • (P1)Publish your vocabulary on the Web at a stable URI
  • (P2) Provide human-readable documentation and basic metadata such as creator, publisher, date of creation, last modification, version number
  • (P3) Provide labels and descriptions, if possible in several languages, to make your vocabulary usable in multiple linguistic scopes
  • (P4) Make your vocabulary available via its namespace URI, both as a formal file and human-readable documentation, using content negotiation
  • (P5) Link to other vocabularies by re-using elements rather than re-inventing.

The tutorial will be divided in 5 parts (plus this overview):

  1. Overview of the tutorial.
  2. (Reqs addressed A1(partially), A2, A3, A4, P1) Publishing your vocabulary at a stable URI using RDFS/OWL. (this post)
  3. (Reqs addressed P2, P3). How to design a human readable documentation.
  4. Extra: A tool for creating html readable documentation.
  5. (Reqs addressed P4). Derreferencing your vocabulary.
  6. (Reqs addressed A1 (partially)). Dealing with the license.
  7. (Reqs addressed A5, P5). Reusing other vocabularies.

Posted in Linked Data | Tagged: , , , , , , , | 16 Comments »

Towards a user friendly SPARQL

Posted by dgarijov on February 19, 2013

One of the main difficulties for using the SPARQL query language is that you have to be familiar with the ontologies or vocabularies in which the data is modeled for performing a query against an endpoint. A couple of weeks ago Machinalis released a new framework called Quepy, which is able to compose SPARQL queries from natural language. A snapshot with an example can be seen below, where I asked for information about Quentin Tarantino:

Quepy

Snapshot of the application

The proposal is very simple: you write your query and then you can test it against DBpedia, the most popular dataset in Linked Data. The types of queries supported at the moment are limited (simple statements taken from the most popular vocabularies), but I guess they will improve in the near future.

Quepy aims to fill the gap between users and the technology, which in my opinion is precisely what we should do in order to make people start using semantic technologies. However, the release of the framework generated some discussion in the lists. Some people didn’t find this useful, as they argued that the results obtained from the SPARQL queries generated by Quepy were not as accurate as those obtained by Google or Bing. This is true, although I don’t think it’s fair to compare a new released (and limited) framework with the results of two of the biggest companies in the market. Furthermore, the results depend on the dataset on which the queries are performed. Other datasets might give more accurate results, which is part of the beauty of the approach: you don’t depend on a single dataset.

Quepy is a nice initiative, but much work remains to be done in order to map complex natural language queries to languages like SPARQL. The potential of this kind of tool is clear, as it can provide an exact response to what the user is looking for, contrasting it against several distributed databases instead of independent silos of information (like Bing or Google). In our group some people are researching on this topic as well, by analyzing the most typical patterns performed when querying datasets in Linked Data. Mixing natural language processing  and common pattern approaches could improve the amount of queries covered by each system separately.

Posted in Linked Data, SPARQL | Tagged: , , , , | 1 Comment »

Provenance Corpus ready!

Posted by dgarijov on December 12, 2012

This week there was an announcement about the deadline extension for BIGPROV13. Apparently, some authors are preparing new submissions for next week. In previous posts I highlighted how the community has been demanding a provenance benchmark to test different analyses on provenance data, so today I’m going to describe how I have been contributing to the publication of public accessible provenance traces from scientific experiments.

It all started last year, when I did an internship in the Information Sciences Institute (ISI) to reproduce the results of the TB-Drugome experiment, led by Phil Bourne’s team in San Diego. They wanted to make accessible the method followed in their experiment in order to be reused by other scientists, for which it is necessary to publish sample traces of the experiment, the templates and every intermediate output and source. As a result, we reproduced the experiment with a workflow using the Wings workflow system, we extended the Open Provenance Model (OPM) to represent the traces as the OPMW profile, and we described here the process necessary in order to publish the templates and traces of any workflow as Linked Data. Lately we have aligned the previous work with the emerging PROV-O standard, providing serializations of both OPM and PROV for each workflow that is published. You can find the public endpoint here, and an exemplar application that loads into a wiki the data of a workflow(dynamically) can be seen  here.

I have also been working with the Taverna people in the wf4Ever project to create a curated repository of runs from both Taverna and Wings, compatible with PROV (since both systems are similar and extend the standard to describe their workflows). The repository, available here for anyone that wants to use it, has been submitted to the BIGPROV13 call and hopefully will get accepted.

So… now that we have a standard for representing provenance the big questions are: What do I do with all the provenance I generate? How do I interoperate with other approaches? At what granularity do I record the activities of my website? How do I present provenance information to the users? How do I validate provenance? How do I complete it? Many challenges remain to be solved until we can hit Tim Berners Lee’s OH Yeah? button of every web resource.

Posted in e-Science, Linked Data, Provenance, scientific workflows, Taverna, Wings | Tagged: , , , , , , , | Leave a Comment »