Refining the FAIR principles

During the 2015 Biohackathon, Mark Wilkinson and I and a few others got together to discuss and refine the FAIR principles, as originally published on the Force11 website. Our goal was to clarify the principles so that they are naturally orthogonal and could be used to assess an implementation and determine the degree to which it conforms to the principles.  Find below a first draft of these revised principles, subject to further elaboration.

FAIR Principles  (proposed)

Preamble

One of the grand challenges of data-intensive science is to facilitate knowledge discovery by assisting humans and machines in their discovery of, access to, integration and analysis of, task-appropriate scientific data and their associated algorithms and workflows. Here, we describe FAIR - a minimal set of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable.

To be Findable:

F1. (meta)data are assigned a globally unique and eternally persistent identifier

F2. data are described with rich metadata

F3. (meta)data are registered or indexed in a searchable resource

F4. metadata specify the data identifier

To be Accessible:

A1  (meta)data are retrievable by their identifier using a standardized communications protocol

A1.1 the protocol is open, free, and universally implementable

A1.2 the protocol allows for an authentication and authorization procedure, where necessary

A2 metadata are eternally accessible, even when the data are no longer available

To be Interoperable:

I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.

I2. (meta)data use vocabularies that follow FAIR principles

I3. (meta)data include qualified references to other (meta)data

To be Re-usable:

R1. meta(data) have a plurality of accurate and relevant attributes

R1.1. (meta)data are released with a clear and accessible data usage license

R1.2. (meta)data are associated with their provenance

R1.3. (meta)data meet domain-relevant community standards

Update: We have updated the FAIR principles on the Force11 page to the ones you see here.

Evaluation of ontology-powered scientific research as a means to assess and improve ontology quality

Gave a talk today on the ontology forum. The bottom line is that the structure, content and use of an ontology affects scientific research and that improvements in ontology quality can be task-driven.

Evaluation of ontology-powered scientific research as a means to assess and improve ontology quality from Michel Dumontier

Google Maps: break in the road bug!

Dear Google Maps,
  I'm trying to plan a trip [1] in and around the Acatama Desert while i'm here in Chile, but have found an interesting bug. Basically, there is a break in the road (between A and B) which prevents maps from using that road: see here to reproduce http://g.co/maps/zwq8y

There's also a break on the road directly east of that break point.

I'd report the problem to you, if only you had the "Report a Problem" link anywhere on this page, despite your claims [2] to the contrary.

Best,

m.

[1] http://g.co/maps/3zwqt
[2] http://maps.google.com/support/bin/answer.py?hl=en&answer=162873&topic=1687362

Provenance: what is it and how should we formalize it?

As a testament to the growing recognition of provenance for (e-)science, i'm glad to see that the W3C incubator group worked hard to think about the issues and make it possible to establish a W3C provenance interchange Working Group.

a good starting point:

"provenance is often represented as metadata, but not all metadata is necessarily provenance"

http://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Provenance_and_Metadata

but

"Descriptive metadata of a resource only becomes part of its provenance when one also specifies its relationship to deriving the resource."

does not provide adequate description for identifying the conditions.  

and 

"Provenance of a resource is a record that describes entities and processes involved in producing and delivering or otherwise influencing that resource"

contains elements that are undefined (record), uncertain (are processes not also entities?), narrow (producing/delivering) and broad (influencing).

Of course, I appreciate the difficulty in crafting a good definition, and I understand that this is a definition from which useful work can be achieved.  I will take the opportunity to express my thoughts on the matter.

i think there are two key aspects to provenance (not unlike what is suggested here: http://www.springerlink.com/content/edf0k68ccw3a22hu/)

1. how did the resource come about? (relates to creation and justification)

 - important for reproducibility (which is an element of science)

 - includes attribution (who created the resource), creation (process that generated the resource), reproduction (process in which a copy was made), derivation (process in which the resource was generated from some resource or portion of a resource), versioning (process of keeping count of sequential derivations)

2. what is the history of the resource (from the point of creation)

 - important for authenticity 

 - includes origin, possession and the acts of transfer

Both have implications for trust, and can be used for accountability, among other things. 

I find this part on recommendations of a provenance framework quite nice:

http://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#A_Roadmap_for_Provenance_on_the_Web

but get less excited when i see the collection of "provenance concepts" 

http://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations (section 4)

particularly because we need to simplify the discourse such that we consider

an event (for 1 above) 

 - participants (and their roles; e.g. agents, targets, products)

 - locations

 - time instants (e.g. action timestamps) and durations (processual attributes)

and a sequence of events (for both 1 and 2 above)

this would certainly help to generate a specification with a minimal set of classes and relations to express this kind of information.

now, i'm writing this late at night, and I appreciate that I may not have considered all the issues that the provenance group has (along with others that have written about the subject), but perhaps there is still some good discussions to be had wrt provenance and how we formally represent it, as it is of strategic importance to the HCLSIG in our current and future efforts.

A letter to gmail: attachments

Dear Gmail team.

First, thank you for making it possible for me to see my unread mail - i sent you this idea some time ago, and i'm glad that you listened.

Second, I'm now somehow at 50% of my allocated capacity, and what i need is a way to filter my mails by attachment (which i can do!), but also order them from largest to smallest (which I can't do). Once i can order attachments by size, i can start deleting the big ones and free up more room! YAY!

m.

On the topic of ontology evaluation

 With only a cursory look at the literature pertaining to the evaluation of ontologies, I already get the feeling that the current measures completely miss the point. The answer doesn't lie in the syntax (format) or structure of the ontology (the number of classes and properties, subsumed classes, axioms, etc), but rather the effectiveness of an ontology ( a representation of knowledge ) is in whether the semantics can be used for some task. So what we really want, is to focus on the nature of the task, and whether ontology provides a competitive advantage over other technologies.

Off the top of my head, here are some tasks:

- search/browse (most sites using GO, etc)

- text annotation (gopubmed)

- data normalization and structured queries - bio2rdf

- answering questions that require background knowledge e.g. across a yeast database. 

- data integration (heterogeneous types of data; data from different sources, of differing granular detail)  (see my translational medicine paper)

- classification e.g. domains or chemicals 

- prediction e.g. predicting phenotypes

Perhaps others can suggest some?

New Charter for the W3C Health Care and Life Sciences Interest Group

Last week, the World Wide Web Consortium (W3C) approved a new charter for its Health Care and Life Sciences Interest Group (HCLSIG), in which I (Carleton University) along with Charlie Mead (NIH CBIIT) and Vijay Bulusu (Pfizer) were selected as co-chairs. This new charter directs us to develop, advocate and support the use of Semantic Web technologies for translational medicine and its three enabling domains: life sciences, clinical research and health care. While the core HCLSIG values - simplicity, pragmatism, effectiveness - remain firmly in place, Charlie, Vijay and I hope to make subtle changes to the operational strategy such that our efforts become increasingly recognized as critical in conferences and boardrooms across the globe.

As always, the HCLSIG will create both prototype implementations that demonstrate the value of formalizing and sharing knowledge using Semantic Web technologies. We will marshal our efforts towards fulfilling compelling use cases that have intrinsic value to not just W3C members, but ideally to a larger number of outside benefactors. Thus, our experts will now develop these use cases such that a priori we have a clearer picture of the rationale of the project, its resources, milestones and deliverables, and ultimately, which organizations and communities will directly and indirectly benefit. Coupled with an effective dissemination strategy including leverage our combined social networks, we hope to maximize the impact of the work of our members in this emerging area of knowledge management.

 As part of our dissemination strategy, we also intend to produce more member contributions that describe methods for basic and advanced tasks, in addition to publishing recommendations arising from consensus among our members. Such recommendations will endorse and specify the use of terminological resources in the long term context of semantic interoperability across the three core domains. Thus, participation in the HCLSIG will be critical for those wanting to advocate RDF-representations of data, OWL representations of ontologies, for the purposes of semantic annotation and large scale, semantic integration of biomedical data.

 With that, we invite non-members to join the W3C and work with our strong compliment of experts in what will surely be an exciting and productive time over the next few years for the W3C HCLSIG.

Bio2RDF: moving forward

  Last week we held our first virtual meeting towards re-invigorating the Bio2RDF project with a significantly larger and vested community. From discussions, we plan to establish 3 focus groups around :

A. policy (information, governance, sustainability, outreach)

B. technical (architecture, infrastructure and RDFization)

C. social (user experience and social networking)

The next step then is for groups to:

1. identify and certify discussion leads (responsibilities: set meeting times and agenda, facilitate and encourage discussion among members, draft reports)

2. identify additional people to recruit from the wider community that would provide additional expertise (interested, but didn't attend the first? sign up now !)

3. extend and prioritize discussion items (what exactly will this group focus its efforts on in the short and long term)

4. identify and assign bite-sized tasks (so we can get things done one step at a time :)

5. collate results and present to the wider community

I suggest that groups self-organize a first meeting in the next two weeks to deal with items 1-4, and either meet again or use the Google documents to collaboratively report findings.

Finally, I'd like for us to hold another meeting with times that are much more accommodating for Europe + North America ;)  Please fill the doodle poll (http://www.doodle.com/fsuz6mgs5cztf2e2)

 

As always, feel free to contact me if you have any questions, and please sign up to the Bio2RDF mailing list for all future discussions.

Scientific Publishing: We're not quite there yet...

I recall how discussions with Pascal Hitzler at ISWC2008 in Karlsruhe eventually led to the creation of a new journal: Semantic Web in 2009. It had most of the elements I wanted :

 1- immediately availability of the manuscript

 2- a transparent review process

 3- a high standard of quality.

 4- low cost of publication

 The immediately availability of the manuscript is important because publishing in a peer-reviewed journals can often take more than 1 year - so this is problematic when making claims about who did what and when. A transparent review process is important to create an incentive to carefully formulate constructive reviews and weed out spurious reviews dealt under the veil of anonymity. In addition, we can establish the reviewer quality and reliability - important when asking for future review requests. Another other other aspect of open review is to enable the acknowledgement of reviewer contributions to improving the quality of the manuscript. In this respect, Semantic Web is stellar - it acknowledges the editors and the reviewers on the front page of the published manuscript.

  One concern was whether reviewers would be sufficiently forthcoming about the failings of a paper such that it lead to the strong rejection of the manuscript. From all indications (see the paper that Pascal and Krzysztof wrote http://t.co/twDASAN9), the journal not only rejects a significant number of manuscripts that don't meet expectations for publication, but also does so in a constructive manner such that it invites authors to submit a revised manuscript with indicating how they have addressed the reviewer's comments.  Indeed, this aspect of open review also means that it authors attempt to shop the failed manuscript to other journals, a simple web search should uncover the reviews associated with it. This saves precious editor and reviewer time and really pushes the authors to make substantial changes.

 While this is all good, the major point of content is the business model. From any which way I look at it, I get the feeling that the tables are turned upside down. First you have the authors creating intellectual content, which they submit to a journal, and is reviewed by people who don't get paid to do so. The journal then turns around and tells the authors that if they want it freely available to the public, then the authors should cough up the money. This is ridiculous. Even a simple advertising based model could easily make a return on investment for articles of increasing impact. The other aspect is that the publisher needs to cover the costs of a print publication, but really, who reads print? I haven't for over 10 years now!  In the case of the Journal, they exact a cost in typesetting, but we're used to doing this for our workshop and conference submissions - so we really don't need people to do this anymore. It would be nice to see a break down of costs for digital publishing systems today (perhaps you can point us to one).

 So what is the business model of the modern scientific publisher?  Well, i think it lies in aggregating and creating new content, whether editorials or comprehensive reviews, which give readers a viewpoint or summary of where the field is heading.  Perhaps low-cost subscriptions (e.g. $100/yr) or pay per view ($5), one could recoup costs if the work is sufficiently meritorious. It's definitely time to think about the next evolution in scientific publishing.

Sabbatical Interview

Earlier this year I was interviewed about my sabbatical plans, and we did a photo shoot as well. Have a look at the article, which includes my rabbits featured on the monitor screen :)

http://cualumni.carleton.ca/magazine/summer-2011/parting-shots/

Breaking Down the Sabbatical

It's hard to believe that it's already been 7 years since I started at Carleton University. I remember thinking about going on sabbatical - a major feature of becoming a University professor. Well, now the time has come, and along with the proposal , I've scheduled quite a bit of travel to visit with colleagues and to take some well deserved holiday time. So here's the tentative schedule

July 3-14: Cambridge, UK (EBI - Dietrich Rebholz-Schuhmann, University of Cambridge  - Robert Hoehndorf)
July 14-18: Vienna, Austria (Bio-ontologies, ISMB Tutorial)
July 18-August 7: Malta+Italy (Valletta, Catania, Salerno, Pompeii, Rome, Florence, Venice, Bologna, Pisa)
August 7-10: Finland (Tempere, Helsinki)
August 10-18: Iceland
August 18-29: Kyoto + Tokyo, Japan (Biohackathon 2011)
August 29-September 3: Madrid (Ontology Engineering Group : Alexander De Leon)
September 3-7: Heidelberg, Germany (COMBINE)
September 7-12: Ottawa (defense: Leonid Chepelev -> success!)
September 13-17: Nancy, France (INRIA/LORIA: Adrien Coulet)
September 19: Volendam (OpenPHACTS/Gen2Phen meeting on open data)
September 17-October 1: Paris, St Malo, Bordeaux, London (travel with parents)
October 1-11: Ottawa, Canada (defense: Natalia Villaneuva-Rosales -> success!)
October 11-December 1: Conception, Chile (Universite de Conception : Leo Ferres)
December 1-December 23: Santiago, San Pedro de Atacama, Mendoza, Buenos Aires, Colonia, Punta del Diablo and Montevideo)
December 24-January 31: Toronto, Ottawa
February - March: Stanford university (Nigam Shah, Mark Musen)
April - June : India, Nepal, Bhutan, Thailand, Singapore ?

Sabbatical 2011-2012: Formalizing Scientific Discourse

Objective: The goal of my research program is to enable biologists to compose and evaluate scientific hypotheses using a diverse set of informational sources (ontology, database, text, equations, and web services). The purpose of my 2011-2012 sabbatical is to develop expertise in formalizing scientific discourse, with a particular focus on formalizing textual descriptions and mathematical equations such that they interoperate with knowledge represented in databases and structured documents. In particular, I am interested in using high quality facts derived from text and dynamic computation from formalized equations to answer questions and provide evidence for scientific hypotheses.

Background: Advancing knowledge in the life sciences involves experimentally testing hypotheses and interpreting the results based on prior scientific work. In generating a valid hypothesis, biologists face the overwhelming challenge of collecting, evaluating and integrating large and increasing amounts of different kinds of information about organisms, cells, genes and proteins from thousands of articles, hundreds of databases and dozens of tools. A biologist’s ability to efficiently construct and evaluate a hypothesis over current knowledge requires that i) knowledge, data and hypotheses are formally represented so they may be reasoned about, ii) adequate software exists to manage and query formal knowledge, and iii) data can be obtained by searching relevant databases and invoking the right analytical tools. The inability to efficiently discover relevant information can negatively impact scientific research directions and proposed activities. Methods for facilitating the construction and evaluation of hypotheses against the current state of knowledge could translate into greater scientific insight and increased productivity. Innovative approaches for knowledge discovery could be applied to data on the emerging Semantic Web and be transformative on a global scale.


Proposed Activities
1. Text to triples: The purpose of this leg of the sabbatical is to gain an understanding of the current state of the art in natural language processing and develop skill in producing high quality triples from parsing scientific text.
Time Frame: July 2011-September 2011
Location: European Bioinformatics Institute, Hinxton, UK.
Host: Dr. Rebholz-Schuhmann

2. Formalizing equations: The purpose of this leg of the sabbatical is to investigate the ontology of equations, represent scientific equations using Semantic Web technologies (principally the Rule Interchange Format), and implement semantic web services that serve to compute over formalized scientific equations.
Time Frame: October 2011-December 2011
Location: Universidad de Concepcion, Concepcion, Chile.
Host: Dr. Leo Ferres

3. Formalizing Research Hypotheses: The purpose of this leg of the sabbatical is to explore the formalization of hypotheses concerning disease. Specifically, I will extract meaningful facts from AlzForum and integrate these with resources from the National Centre for BioOntology (NCBO) and Bio2RDF, our large scale Semantic Web project.

Time Frame: January 2012-March 2012
Location: Stanford University, Palo Alto, California, USA.
Host: Dr. Mark Musen

4. Integrated Framework for Knowledge Discovery: The last leg of the sabbatical will be focused towards maximizing interoperability between text, equations, ontologies and database-derived facts. I will use SADI, our platform semantic web services framework, towards achieving this objective.
Time Frame: April 2012-June 2012
Location: India, Thailand, Singapore


Scientific Value and Broader Beneficial Impacts 
The development and application of efficient strategies for knowledge discovery is a major goal in bioinformatics. My research into new strategies for the representation and evaluation of scientific hypotheses using ontologies, scientific text, data and bioinformatic services will create a novel platform that will significantly contribute to scientific productivity and ultimately improve our understanding of biology. The proposed sabbatical will provide me with new skills that will be used to train a future training of young scientists in the areas of formal knowledge representation, text mining and the Semantic Web. Ultimately, it is expected that the sabbatical will cultivate new partnerships with leading scientists and open new doors to work with industry and government agencies.

SADI

(modified from an email that Mark Wilkinson sent)

SADI is a very lightweight "standard" (set of best-practices, really) for modeling and providing Web Services. It uses standards from the W3C Semantic Web initiative - in particular, it uses OWL for types, and RDF for instance data.

SADI is used to expose "resources" to the world in a manner that can be discovered automatically, and accessed automatically, by software. Those resources might be data inside databases (where SADI replaces the traditional Web Query page), or they might be analytical algorithms that consume data, chug away on it, and return output data. In both cases, the interfaces are structurally identical, so from the perspective of the client software, it doesn't have to know or care whether it is trying to get data out of a database or out of an analytical tool - the question/query structure is the same, and moreover, it is completely predictable.

This is critical advantage #1 for SADI over traditional Web Services frameworks - in traditional XML-based Web Services, you still must code your client software to access each service, since the service interfaces cannot be interpreted by the machine. In SADI, we can design ONE piece of software to access all resources exposed as SADI services - "one ring to rule them all!". (and we already have several different "rings" that expose SADI data in different ways)

Critical advantage #2 is a bit more obscure and hard to describe, but is likely to be the more important in the long-run. In SADI, data is "grounded" in explicit semantics. This means that all data in SADI carries with it information about what TYPE of data it is, and how that data relates to other data (e.g. genes transcribed into transcripts translated into proteins which regulate genes: Gene, Transcript, Protein are all data types, and "transcribed", "translated", "regulate" are relationships between them). With this explicit (and extensive!) grounding in semantics, we can start asking our machines to do a lot of the interpretation for us. For example, "what gene regulates gene X" is a nonsensical question biologically, but it's a question that biologists ask all the time! With a solid grounding in semantics, the machine would be able to follow the logical pathway above and say "well, to answer that question, I am going to have to go through transcripts and proteins to get there" and then automatically construct the pipeline of services that get to the answer. This is just one example of how Semantics can be used to facilitate question-answering.

There are several tutorials available.

for what it can do: http://www.slideshare.net/markmoby/sadi-swsip-09

then go to http://sadiframework.org to find the more specific tutorials on how to deploy services.

The current list of available services is at http://sadiframework.org/registry/services/ and that list will be growing rapidly over the next year (we have committed to having at least 400 more services, but I suspect that we'll go far beyond that number!)

Getting SNORQL to work with Virtuoso

SNORQL is an AJAX SPARQL browser that makes it easy to i) see if your queries work and ii) navigate your linked data. SNORQL comes packaged with D2R server, but one has to make a few modifications to make it work when a) installed in a directory or port that is different than the SPARQL endpoint.

You need 3 things to do the following to make SNORQL work with some host-located endpoint:

1. Download and install SNORQL. SNORQL comes as part of the D2R distribution. Download this and extract the snorql folder from the webapps director into some folder on your host, preferably one that is already accessible by the web server (e.g. in the htdocs directory). If you want to put SNORQL in a folder different than that, you must add an entry to the http.conf file.

Alias /snorql /usr/var/snorql

<Directory "/usr/var/snorql">

Options None

AllowOverride None

Order allow,deny

Allow from all

</Directory>

2. Configure the Apache server as a proxy to the endpoint. If the port of the apache server and the endpoint are different, you need to make them appear the same for the AJAX to work. Add this to your http.conf file

ProxyRequests Off

<Proxy *>

Order deny,allow

Allow from all

</Proxy>

ProxyPass /sparql http://localhost:8890/sparql

ProxyPassReverse /sparql http://localhost:8890/sparql

3. Configure SNORQL to use the endpoint.

Edit the snorql.js file and replace

this._endpoint = document.location.href.match(/^([^?]*)snorql\//)[1] + 'sparql';

with

this._endpoint = 'http://localhost/sparql';

4. Open your browser to the SNORQL URL (e.g. http://localhost/snorql) to query and navigate the results :-)

Compute Canada and the future of HPC computing

Compute Canada is hosting a series of town hall meetings to discuss the future of high performance computing in Canada. Here are some thoughts:

In order to increase Canada’s HPC capability and make them more relevant for today’s scientific computing needs, it will have to embrace new computing models.

The next generation in computing is cloud computing. Compute Canada should embrace this model as part of its service offering such that researchers can use the cloud across Compute Canada infrastructure. Importantly, it must be possible for researchers to grow their cloud from local private clouds (we already have one setup in our lab), into the Compute Canada cloud and ultimately into commercial clouds (such as Amazon EC2), if necessary. Compute Canada also needs to invest in data storage, and create the means by which such storage may be accessed using data access standards (e.g. Amazon S3) and provisioned through networks (e.g. CANARIE <-> university <-> commodity networks). Compute Canada should endeavor to use open standards and ensure interoperability for any deployment.

A major issue with Canada’s HPC centers is that we cannot currently (AFAIK) host public services on them. As a bioinformatician, it would be invaluable for me to have Compute Canada host a server using my image, thereby ensuring scalable capacity and continuity for the software tool (as reported in publications). Importantly, some of our services require on-the-fly compute resources to accomplish their task, and it would be ideal if we could setup asynchronous services that use cloud facilities to compute, and then provide a link to users as to where they can find their results (stored on an S3 compatible store). Being able to do this would be a game changer for bioinformatics (and I suspect in other computing fields), and would create a new paradigm for open service provisioning across the world.

While full fledged cloud computing software is currently available as both open source and commercial solutions, this would present an opportunity to acquire such software 'en masse' to get started, while also creating new capacity in developing cloud computing solutions. I can imagine training my 4th year project/honours students to develop bioinformatic software using Compute Canada’s compute/storage resources – but not until we see cloud computing.

The trouble with ontology is ... the lack of "shared understanding"

This kind of analysis goes to the heart of one of the problems i have with monolithic enterprises that trivially assume agreement will come from some shared understanding of "reality". Nothing could be further from reality:

http://www.nytimes.com/2009/07/21/science/21angier.html?_r=1&ref=science

Setting out clear Ontology Design Principles

I've documented them here:

http://code.google.com/p/semanticscience/wiki/ODP

Critique of OBO Foundry Principles

The OBO Foundry aims to create a suite of orthogonal interoperable reference ontologies in the biomedical domain. They have outlined their principles here: 

http://www.obofoundry.org/wiki/index.php/OBO_Foundry_Principles

In reading some of these I found that they poorly expressed true principles in ontology design. I provide here a brief critique on some of the contentious points:

"3. The ontologies possesses a unique identifier space within the OBO Foundry. The identifier uniquely and persistently identifies a definition, which itself unambiguous identifies some type of biological entity. The identifier is for the definition: it is NOT the name and it is NOT an identifier for the name.

There are systems that use alphanumeric id's - eg MetaCyc. This should be dis-encouraged, especially as these have semantic content."

This mixes up a number of issues. An identifier is a symbol for an entity, which should guarantee uniqueness in the lexical space, unlike human readable names which are not required to be unique. So it doesn’t matter whether the identifier is numeric, alphanumeric or alphabetic and thus the latter part of this principle, referring to alphanumeric MetaCyc ids, is pure nonsense. It is the description of the entity that *matters*, and that the textual description is arguably unchanging (What does OBOF say about when a description changes by even one word? Should a new identifier be crafted? How does one assess whether the previous identifier is in fact compatible with the new one? Should one be directed to use the new identifier – is it possible that the semantics are *fundamentally* different? These are far more important questions to address)

"6. The ontology must be orthogonal to other ontologies already lodged within OBO. For each domain, there should be convergence upon a single reference ontology that is recommended for use by those who wish to become involved with the Foundry initiative"

This is a contestable claim. Given that there is no universal agreement on many biological terms, any given ontology will not necessarily capture the semantics of what one wants to express. Anyone familiar with the word "gene" can easily demonstrate this as a case in point.

 

"7. The ontologies include textual definitions for all terms."

Textual descriptions aren't really useful unless they succinctly capture the essence of the entity in question.  For instance, definitions in the (OWL version) BFO are incomprehensible to many people (certainly to my undergrad students). In many other cases the textual descriptions can be shown to be either overly vague or constraining in unrealistic ways.

 

Einstein said "Make everything as simple as possible, but not simpler" - a good mantra in crafting term descriptions is "Be as accurate as possible, while not adding superfluous information or imposing unnecessary constraints.

 

"9. The ontology is well documented."

Be more specific - What does "well documented" mean?

"10. The ontology has a plurality of independent users."

This is another unreasonable demand. The defining characteristic is that for every ontology, there exists requirements (possibly in the form of use cases) that the ontology can be demonstrated to satisfy.  Ultimately, an ontology should have demonstrated utility. Paraphrasing Salinger - If you build it, (and have shown it to be useful) they will come.

 

"11. The ontology will be developed collaboratively with other OBO Foundry members."

A long standing myth is that ontologies need to be developed collaboratively - but in fact, we have found that such an approach is in fact wholly unproductive. What is productive is collecting use cases, undertaking focused development, and conducting a peer review and refinement process in which the needs of the community can be publicly solicited and addressed. This kind of procedure is in place at the W3C, and results in high quality standards. The OBO Foundry should consider setting up such a facility, with open calls for review across all relevant mailing lists, including quality assessment, additions/removals etc - particularly before things get published as a so-called "standard"

 

ChemAxon - Chemical Ontology

Posted at: http://wwmm.ch.cam.ac.uk/blogs/adams/?p=195

Making the distinction between a substance and a molecule is indeed important and valuable from an ontological perspective, particularly when it comes to reasoning about the domain. The distinction is often blurred simply because it is more pragmatic not to consider them different. Indeed, most chemists might agree that while there is a conceptual distinction, they don’t want to navigate through a set of high-brow concepts to find such simple (indirect) relationships. Have you considered the impact of representing knowledge in this way with respect to usability?

Another important issue is that of identity - you are making the argument that every different feature effectively warrants a different identifier. That if i want to make a statement about glucose in one form versus another, i need two different identifiers. This will lead to enormous numbers of different “concepts”, which may affect reasoning capability (especially in OWL!) and potentially also lead to sparsely populated knowledge bases. An alternative is to capture the semantics of non-structural features in relations to the main component. For instance, I could reuse the “Glucose” class by adding additional restrictions, in the context of some process or experimental result. e.g. in my experiment i found glucose to be in its chair form. Indeed, expressing the behaviour (or structural conformation in this case) wrt to the context leads itself to modular reuse and also improves our representation of knowledge.

A couple of pointers that you might find interesting:

1 - Contextual knowledge representation - http://dumontierlab.com/pdf/2008_OWLEDEU_MR.pdf

2 - Biochemical Identifiers - http://www.slideshare.net/micheldumontier/accurate-biochemical-knowledge-starting-with-precise-structurebased-criteria-for-molecular-identity

We’ve also worked on chemical ontologies in OWL - you can see them here:

http://dumontierlab.com/index.php?page=ontologies

it would be nice to merge the two and work towards a comprehensive OWL-based ontology for the chemistry domain. Let me know if that would interest you.

Wouldn't 48 terabytes be nice to have?

Apparently, SUN Microsystems is making an offer to ATLAS sites including those involved in the CERN LHC (Large Hadron Collider) project to purchase to extra storage space... $25,000 for the x4540, capable of storing 48 TERABYTES. The offer is exclusive to participants, but there's a catch - you have to buy 5 of them to get the good deal ;-)

I want one! This could be useful for hosting my Bio2RDF node...