Data Integration Research Blogging

One way for RDF to help a bioinformatician build a database: S3DB

This post is part of the PLoS One syncroblogging day, as part of the PLoS ONE @ Two birthday celebrations. Happy Synchroblogging! Here’s a link to the paper on the PLoS One website.

Biological data: vitally important, determinedly unruly. This challenge facing the life-science community has been present for decades, as witnessed by the often exponential growth of biological databases (see the classic curve in the current graphs of UniProt1 and EMBL if you don’t believe me). It’s important to me, as a bioinformatics researcher whose main focus is semantic data integration, but it should be important to everyone. Without manageable data that can be easily integrated, all of our work suffers. Nature thinks it’s important: it recently devoted an entire issue to Big Data. Similarly, the Journal of Biomedical Informatics just had a Semantic Mashup special issue. Deus et al. (the paper I’m blogging about, published in PLoS One this summer) agree, beginning with “Data, data everywhere”, nicely encapsulating both the joy and the challenge in one sentence.

This paper describes work on a distributed management system that can link disparate data sources using methodologies commonly associated with the semantic web (or is that Web 3.0?). I’m a little concerned (not at the paper, just in general) at the fact that we seem to already have a 3.0 version of the web, especially as I have yet to figure out a useful definition for semantic web vs Web 2.0 vs Web 3.0. Definitions of Web 3.0 seems to vary wildly: is it the semantic web? Is it the -rwx- to Web 1.0’s -r– and Web 2.0’s -rw– (as described here, and cited below)? Are these two definitions one and the same? Perhaps these are discussions for another day… Ultimately, however, I have to agree with the authors that “Web 3.0” is an unimaginative designation2.

So, how can the semantic web help manage our data? That would be a post in itself, and is the focus of many PhD projects (including mine). Perhaps a better question is how does the management model proposed by Deus et al. use the semantic web, and is it a useful example of integrative bioinformatics?

Their introduction focuses on two types of integration: data integration as an aid to holistic approaches such as mathematical modelling, and software integration which could provide tighter interoperability between data and services. They espouse (and I agree) the semantic web as a technology which will allow the semantically-meaningful specification of desired properties of data in a search, rather than retrieving data in a fixed way from fixed locations. They want to extend semantic data integration from the world of bioinformatics into clinical applications. Indeed, they want to move past “clandestine and inefficient flurry of datasets exchanged as spreadsheets through email”, a laudable goal.

Their focus is on a common data management and analysis infrastructure that does not place any restrictions on the data stored. This also means multiple instances of light-weight applications are part of the model, rather than a single central application. The storage format is of a more general, flexible nature. Their way of getting the data into a common format, they say, is to break down the “interoperable elements” of the data structures into RDF triples (subject-predicate-object statements). At its most basic, their data structure has two types of triples: Rules and Statements. Rules are phrases like “sky has_color”, while statements add a value to the phrase, e.g. “today’s_sky has_color blue”.

They make the interesting point that the reclassification of data from flat files to XML to RDF to Description Logics starts to dilute “the distinction between data management and data analysis”. While it is true that if you are able to store your data in formats such as OWL-DL3, the format is much more amenable to direct computational reasoning and inference, perhaps a more precise statement would be that the distinction between performance of data management tasks and data analysis tasks will blur with richer semantic descriptions of both the data and their applications. As they say later in the paper, once the data and the applications are described in a way that is meaningful for computation, new data being deposited online could automatically trigger a series of appropriate analysis steps without any human input.

A large focus of the paper was on identity, both of the people using it (and therefore addressing the user requirement of a strong permissions system) and of the entities in the model and database (each identified with some type of URI). This theme is core to ensuring that only those with the correct permissions may access possibly-sensitive data, and that each item of information can be unambiguously defined. I like that the sharing of “permissions between data elements in distinct S3DB deployments happens through the sharing the membership in external Collections and Rules…not through extending the permission inheritance beyond the local deployment”. It seems a useful and straightforward method of passing permissions.

I enjoyed the introduction, background, and conclusions. Their description of the Semantic Web and how it could be employed in the life sciences is well-written and useful for newcomers to this area of research. Their description of the management model as composed of subject-predicate-object RDF triples plus membership and access layers was interesting. Their website was clear and clean, and they had a demo that worked even when I was on the train4. It’s also rather charming that “S3DB” stands for Simple Sloppy Semantic Database – they have to get points for that one5! However, the description of their S3DB prototype was not extensive, and as a result I have some basic questions, which can be summarized as follows:

  • How do they determine what the interoperable elements of different data structures are? Manually? Computationally? Is this methodology generic, or does it have to be done with each new data type?
  • The determination of the maturity of a data format is not described, other than that it should be a “stable representation which remains useful to specialized tools”. For instance, the mzXML format is considered mature enough to use as the object of an RDF triple. What quality control is there in such cases: in theory, someone could make a bad mzXML file. Or is it not the format which is considered mature, but instead specific data sets that are known to be high quality?
  • I would have like to have seen more detail in their practical example. Their user testing was performed together with the Lung Cancer SPORE user community. How long did the trial last? Was there some qualitative measurement of how happy they were with it (e.g. a questionnaire)? The only requirement gathered seems to have been that of high-quality access control.
  • Putting information into RDF statements and rules in an unregulated way will not guarantee a data sets that can be integrated with other S3DB implementations, even if they are of the same experiment type. This problem is exemplified by a quote from the paper (p. 8): “The distinct domains are therefore integrated in an interoperable framework in spite of the fact that they are maintained, and regularly edited, by different communities of researchers.” The framework might be identical, but that doesn’t ensure that people will use the same terms and share the same rules and statements. Different communities could build different statements and rules, and use different terms to describe the same concept. Distributed implementations of S3DB databases, where each group can build their own data descriptions, do not lend themselves well to later integration unless they start by sharing the same ontology/terms and core rules. And, as the authors encourage the “incubation of experimental ontologies” within the S3DB framework, chances are that there will be multiple terms describing the same concept, or even one word that has multiple definitions in different implementations. While they state that data elements can be shared across implementations, it isn’t a requirement and could lead to the problems mentioned. I have the feeling I may have gotten the wrong end of the stick here, and it would be great to hear if I’ve gotten something wrong.
  • Their use of the rdfs:subClassOf relation is not ideal. A subclass relation is a bit like saying “is a”, (defined here as a transitive property where “all the instances of one class are instances of another”) therefore what their core model is saying with the statement “User rdfs:subClassOf Group” is “User is a Group”. The same thing happens with the other uses of this relation, e.g. Item is a Collection.  A user is not a group, in the same way that a single item is not a collection. There are relations between these classes of object, but rdfs:subClassOf is simply not semantically correct. A SKOS relation such as skos:narrower (defined here as “used to assert a direct hierarchical link between two SKOS concepts”) would be more suitable, if they wished to use a “standard” relationship. I particularly feel that I probably misinterpreted this section of their paper, but couldn’t immediately find any extra information on their website. I would really like to hear if I’ve gotten something wrong here, too.

Also, although this is not something that should have been included in the paper, I would be curious to discover what use they think they could make of OBI, which would seem to suit them very well6. An ontology for biological and biomedical investigations would seem a boon to them. Further, such a connection could be two-way: the S3DB people probably have a large number of terms, gathered from the various users who created terms to use within the system. It would be great to work with the S3DB people to add these to the OBI ontology. Let’s talk! 🙂

Thanks for an interesting read!

1. Yes, I’ve mentioned to the UniProt gang that they need to re-jig their axes in the first graph in this link. They’re aware of it! 🙂
2. Although I shouldn’t talk, I am horrible at naming things, as the title of this blog shows
3. A format for ontologies using Description Logics that may be saved as RDF. See the official OWL docs.
4. Which is a really flaky connection, believe me!
5. Note that this expanded acronym is *not* present in this PloS One paper, but is on their website.
6. Note on personal bias: I am one of the core developers of OBI 🙂

Helena F. Deus, Romesh Stanislaus, Diogo F. Veiga, Carmen Behrens, Ignacio I. Wistuba, John D. Minna, Harold R. Garner, Stephen G. Swisher, Jack A. Roth, Arlene M. Correa, Bradley Broom, Kevin Coombes, Allen Chang, Lynn H. Vogel, Jonas S. Almeida (2008). A Semantic Web Management Model for Integrative Biomedical Informatics PLoS ONE, 3 (8) DOI: 10.1371/journal.pone.0002946
Z. Zhang, K.-H. Cheung, J. P. Townsend (2008). Bringing Web 2.0 to bioinformatics Briefings in Bioinformatics DOI: 10.1093/bib/bbn041