Of particular relevance to our poster was another presented by Cam Webb. Cam has created an OWL-compatible version of Darwin Core which can be used to describe specimen metadata in RDF. We made similar use of Darwin Core in our poster, but we are looking into adopting Cam’s Darwin-SW for this part of the model.

Overall there was a lot of interest in semantic technologies at TDWG, ranging from the initial meeting of an RDF/OWL working group to other projects that are not using semantic technologies but seem well suited for RDF.

With the support of funding from DeepFin, I started my internship with Phenoscape at the National Evolutionary Synthesis Center (NESCent) in August 2011. My three months here at NESCent have flown by and even though it is my last day, I am just as excited about the project as the day I started! Working with Wasial Dahdul, Peter Midford and Jim Balhoff has enabled me to learn and understand a great deal about databases, collaboration and morphology. Phenoscape has completely changed the way I think about phenotypic characters. Breaking them down into logical statements in Phenex really allows you to understand a character as it fits in the bigger picture. I was able to work with Wasila in forging interdisciplinary ties by contributing to other ontologies and databases, such as PATO and PaleoDB. Additionally, working to assist in the expansion of Phenoscape to incorporate all vertebrates taught me a lot about the origins of vertebrates and the plethora of prehistoric life I did not realize existed- including my new personal favorite prehistoric fish, Jagorina!

NESCent is an amazing place. Being one of the few people here without a higher degree or a long list of publications under their belt, I was initially a little intimidated. However, the informatics group, post-docs and professors have been great and pushed me to participate in seminars and intellectual discussion. This is a stimulating environment that facilitates thinking outside the box and looking at bigger picture issues in evolutionary biology.

I am excited to continue my work on Phenoscape offsite back at the Smithsonian and I hope to contribute throughout my graduate career in Dr. Brian Sidlauskas’s (former NESCentian and Phenoscape tester and contributor) lab at Oregon State University.

Ben Frable
Graduate Student, Oregon State University
Student Researcher, Smithsonian National Museum of Natural History

You might say it is the best of times and the worst of times for semantic web enthusiasts, in that reasoning and query engines that can be used on large collections of RDF have in the last few years become a reality (one of the Challenge Tracks provided contestants with a *billion* triples to work with).  But some see clouds on the horizon. The web search titans (Bing, Google and Yahoo!) are now pushing schema.org, a microformat and vocabulary standard for web content that some worry may threaten the development of richer semantic web technology.  Still, most treated the news positively, happy to know that these organizations now seem to agree on the importance of semantics.  In fact, Yahoo! described at the conference how they are trying to build a “Web of Objects” that takes advantage of scheme.org, together with more extensive internal vocabularies, to regroup knowledge pieces that are scattered around the Web.

Conference chair Natasha Noy showed a revealing pair of tag clouds comparing the abstracts from the first year of the conference in 2001 to today — the terms “semantic” and “web” have shrunk in importance and “data” is now king! 

Ivan Herman’s blog gives a good sampling of the flavor of talks presented at the meeting.  I especially enjoyed the Industry Track, since these applications are less familiar to me than the academic/scientific ones, and  I was particularly impressed by the importance of semantic technologies to the news media and other content industries.  These technologies are being deployed by news organizations with great enthusiam (e.g. the BBC).  I also came away with a strong sense that semantic technologies are helping to create demand, and drive a revolution in the use of, Open Government Data; there were a number of demonstrations of useful real-world applications, particularly to environmental monitoring.

With my Phenoscape hat on, I attended a Linked Open Data for Science (LISC) satellite workshop prior to the main conference.  The event included both presentations and discussions from a variety of perspectives about the opportunities and challenges of this new technology.  A diversity of fields were represented (social science, linguistics, geosciences, biomedicine, etc.).  But, it is clear that uptake of linked open data as an alternative means of publication is still in its infancy within the sciences.  This despite the fact that the bioinformatics data centers account for nearly a quarter of the real estate in the famous linked data cloud diagram.  Some of the most exciting opportunities, in my opinion, come from the ability to allow radically decentralized data publication, and this is something that we might wish to pilot in a modestly distributed data curation environment like Phenoscape.  Another observation: I was surprised to discover at the meeting how much the utility of the linked data cloud (and, by extension, the semantic web) depend on the social convention by which everyone provides links into a relatively small number of large ‘concept repositories’ like DBPedia (which was originally a Master’s project, BTW).

The breakout discussion sessions at LISC  highlighted how scientific practice will place difficult demands on linked data with respect to provenance, context, granularity, distributed authority, etc.  This resonated with the message of our own contribution to the workshop, which outlined some of the particular challenges in making context-dependent links between scientific objects, when the descriptions of those objects are scattered across different resources, and when the similarities between objects are spread weakly over many properties [1].  Another important question that hit home for a number of us coming from the bioinformatics and biodiversity informatics world is how scientists are going to be able to take advantage of the innovations now going on in the commercial sector (including some of the exhibitors at the main conference) within the constraints and DIY culture of small individual university-based research grants.

There is no denying the explosion in linked data resources out there (comparisons of the growth in the cloud diagram are about as common as graphs showing the growth in sequence data at a biology conference).  But another recurrent theme of the meeting was that unfortunately much of that content is missing semantics (i.e. a lack of use or availability of ontologies for many concepts, and lack of links between content at different endpoints), and generating semantically annotated triples needs to be easier that it currently is (a message certainly relevant to those of us developing curation tools).

One of the keynotes, from Frank van Harmelen, generated quite a bit of buzz.  He looked back on 10 years of the semantic web, asking what theoretical principles we can learn from the experience so far, and his annotated slides are well worth a look.

The conference was a great mix of different formats.  In addition to the keynotes and regular talks, there are a host of workshops and tutorials, challenges, panel discussions (including one billed as a ‘Death Match’), and even a special competition for the best “Outrageous Ideas”.  The winner of that one was a proposal to bring linked data to the non-networked portion of humanity.  A particularly nice feature of the meeting was the ‘Minute Madness’ preceding the poster session in which each of the poster presenters gave a short timed pitch with to all the attendees – it was a very entertaining and informative way to ‘see’ every poster and allowed everyone to quickly pick out which ones to hit during the session.

For more, see the excellent day-by-day summary of the meeting from Juan Sequeda, where there are links to all the winning presentations and challenge entries.  [Ironically, the conference website is down temporarily while it is being moved, so come back later if the links to the papers hang].  The next ISWC will be November 11-15, 2012 in Boston.

Reference:

[1] Vision T, Blake J, Lapp H, Mabee P, Westerfield M (2011) Similarity Between Semantic Description Sets: Addressing Needs Beyond Data Integration, in Proceedings of the First International Workshop on Linked Science, Bonn, Germany, October 24, 2011, Tomi Kauppinen, Line C. Pouchard, Carsten Kessler (eds), published in CEUR Workshop Proceedings, Volume 783.

We used the Synchronization Tool (an Obo-Edit plugin) to compare XAO to several external ontologies. The tool made it efficient to find and add missing cross-references and terms, and to resolve conflicting data (e.g., differing definitions). By the end of the week, we updated XAO with all relevant terms from the Amphibian Anatomy Ontology (AAO), Vertebrate Anatomy Ontology (VAO), Uber Ontology (UBERON), and Common Anatomy Reference Ontology (CARO).

Erik, VG, and Christina worked hard the rest of the week to make XAO is_a complete and to update definitions, relationships, and synonyms for existing terms. With XAO’s new release, the ontology is now 25% larger than before. The newest version of XAO will be available for downloaded soon from the OBO Foundry.

We presented talks at the Facilitating Anatomy Ontology Interoperability workshop. Jim’s talk was on representing taxa as individuals in OWL, an alternative to the common representation of taxa as classes, which facilitates annotation of phenotypic data involving polymorphism and evolutionary reversals.  I presented a lightning talk on the anatomy ontology synchronization requirements for linking evolutionary and model organism phenotypes.  Other presentations from the workshop are available here. We also presented a poster describing the reasoning used in the Phenoscape Knowledgebase.

The main conference included interesting talks on a broad range of topics including the application of ontologies to proteins, diseases, biological mechanisms, and electronic health records. Presentations can be downloaded here.

1. Development of computational and statistical methodology for measuring semantic similarity between sets of phenotypes, in order to support searches within extremely large phenotype datasets.
2. Development and testing of methods for automatically generating ontologically based phenotype expressions from structured excerpts of natural language.

The position is based in the informatics group at the National Evolutionary Synthesis Center (NESCent), and will be administered through the University of North Carolina at Chapel Hill (UNC-CH) under the supervision of Hilmar Lapp at NESCent and Dr. Todd Vision at UNC-CH.   The research will be in collaboration with Dr. Chris Mungall at Lawrence Berkeley National Lab and Dr. Hong Cui at the University of Arizona.  The project also includes biologists and bioinformaticists from the University of South Dakota, the University of Chicago, the University of Kansas, in addition to the model organism databases for mouse (MGD), zebrafish (ZFIN), and Xenopus (Xenbase).

Applicants should have a PhD in bioinformatics, computational biology or a related field. Prior experience with machine reasoning using ontologies is strongly preferred. The position is for two years, pending satisfactory performance and availability of funds.  To apply, please provide a cover letter, CV, and contact information for three references.  Inquiries and applications may be sent to Hilmar Lapp at hlapp@nescent.org.  The post is open immediately and will remain open until filled.

We have also improved our software and data release processes so that the public Knowledgebase can easily keep up with new developments and the latest data updates from our curators. Looking forward, the next major feature to be added to the Knowledgebase is a faceted browsing interface which is currently under development. This interface should help provide an overview of how the data are organized within the various ontologies used in the Knowledgebase.

The first issue is that we are interested in changes in phenotype, not simply matching phenotypes.  For phenotypes associated with mutants of model organism mutants, it is understood that they vary with respect to the wild type.  For taxa, however, this means looking for taxonomic nodes where variation in a phenotype is observed among the children of the node.  For example, there are nine species within the genus Aspidoras with annotations for the shape of the opercle bone.  Of these, eight exhibit opercle bones with round shape, but the ninth (A. pauciradiatus) is annotated with a triangular opercle.  In contrast, all three annotated species of the related Hoplosternum are annotated with a triangular opercle.  Thus there is detectable variation in opercle shape within the children of Aspidoras, but not within  Hoplosternum - suggesting that change in opercle shape has occurred somewhere among the descendants of  Aspidoras. For our analysis, identifying variation among descendants is important.

Thus, our search for shared variation in phenotypes focuses on matching phenotypes associated with genes with phenotypes of taxa showing variation.  However we are looking for matches at a larger scale than single phenotypes; we are looking for matches across the set of phenotypes affected by a gene or the set of features that have changed among the descendants of a taxonomic node.   We refer to these sets of phenotypes as the ‘phenotypic profile’ of a gene or taxon, following a seminal paper by Washington et al. 2009.  Washington et al. propose four metrics (three based on ‘information content’) to score matches between the sets of phenotypes in a pair of profiles.

In the course of developing the search, we have encountered several important differences in curation approach between ZFIN and Phenoscape.  In some cases tehre are different uses of PATO to model the same phenotype, for example the absence of an entity.  In other cases ZFIN uses a quality ‘abnormal’ that applies to mutants, but not in a taxonomic, comparative sense, which means these phenotypes will be inaccessible to us.  Thus, implementing this search is helping us to better understand our data and our choices in modeling the data and how it interoperates with other ontology-based data.  Such reflection would have been difficult or impossible without the use of ontologies to represent the phenotypes.

This RCN envisions building a broad base of community knowledge and resources so as to maximize the research potential of web-based data.  Funding for participation in meetings, presentations and laboratory exchanges for students, postdocs and faculty from ontology and taxonomic domains (initially plants, arthropods, and vertebrates) is available through the RCN (see http://phenotypercn.org/opportunities/.

We are eager to have you join us!  Please sign up for our participant and mailing lists for further information (http://phenotypercn.org/participants/add/) and feel free to contact one of the PIs (Paula Mabee, pmabee@usd.edu; Andy Deans, andy_deans@ncsu.edu; Eva Huala, huala@acoma.stanford.edu; and Suzanna Lewis, selewis@lbl.gov).

Some proposed changes to PATO:

• Consolidate relational and monadic branches: One of the more major decisions was to remove the distinction between the “relational” and “monadic” branches of PATO. The relational terms can be descendants of the monadic terms which are pre-composed with a reference to a dependent entity. This change would be a major improvement helping to relate terms dealing with similar concepts and for inference using these concepts.
• Continuums: Add a relationship ‘ranges_from’ which can be used to specify a continuum of values between two indicated qualities.  Example: a color grading “from yellow to brown”.
• Position: Remove term ‘position’ after moving its children to more appropriate places.
• Enable directional references: Add classes describing directions a structure can be pointed.  These classes should include pre-composed logical definitions drawing on the spatial ontology. An example would be ‘directed posteriorly’.
• Spatial term review: Review all existing PATO terms referencing spatial aspects, and verify that they are based on logical definitions using the spatial ontology.
• Size vs. shape: Several children of ‘size’ were noted to actually be types of ‘shape’. Many of these were noted and several were immediately revised by George Gkoutos.  This discussion revealed that many free-text characters defined by biologists refer to size-sounding terms when they are actually describing changes in shape.
• Changes in various term definitions:
  • PATO:1485 should be obsoleted and two new separate terms, ‘condensed’ and ‘compressed’ (as a synonym of ‘flattened’) should be added. ‘Condensed’ is considered to be a type of ‘structure’, while ‘flattened’ is a ‘shape’ instead of a ‘curvature’. All children of ‘flattened’ should be reviewed to remove references to ‘curvature’.
  • Improved definition for ‘morphology’ referencing “shape or size or structure”.
  • Clarified terms ‘surface feature shape’ and ‘texture’. The parent term ‘surface shape’ was obsoleted while ‘surface feature shape’ is retained as a shape with a repeated feature on a surface. A ‘has_repeated_part’ relation should be added to be used in pre-composed subclasses of ‘surface feature shape’, linking them to specific shapes.
  • Add a term defining ‘spatial density’. The existing ‘density’ term refers to the physics concept.
• Spatial pattern: We proposed reworking the ‘spatial pattern’ term hierarchy to logically reference other terms which the given pattern is in respect to (structures, colors, etc.). The ‘color pattern’ term should be moved under ‘spatial pattern’.
• Synonyms: We discussed some community-specific term labels for various PATO classes.  There is an existing OBO standard for how to implement these as synonyms. Community-specific applications would need to support display of the appropriate synonym.
• Comparative relations: Generalize existing comparative relations in PATO.  For example, Instead of ‘increased_in_magnitude_relative_to’, it will be ‘increased_in_value_relative_to’.
• Qualitative branch: Clean up and better document ‘qualitative’ hierarchy (which is used for various logical “shortcuts”).

EQ representation issues:

• Size bins: Relative size characters (small, medium, large) can be represented by creating (within a given annotation application) anonymous subclasses of size which are related to each other in the appropriate way using relations such as ‘increased_in_magnitude_relative_to’.  This will provide the appropriate relative size inferencing for the given character states, but, as in the original paper, not be readily comparable to size classes created for characters in other studies.
• Negation: When describing a phenotype that is simply “not something else”, e.g. ‘not round’, the complement_of operator should be used in an OWL class expression.
• Comparative/relative qualities should not be conflated with relational (or, better, dependent) qualities. Comparative relations such as ‘increased_in_value_relative_to’ can be used to relate one EQ to another.
• Phenex and other annotation tools should provide enhanced interfaces for these special representation issues: creating comparative EQs by simply entering a relative entity or taxon, a simple means to say things like ‘not round’, and a way to create local groups of relatively ordered qualities for a given character (e.g. small, medium, large).

When the TTO was first built, we followed the pattern of the NCBI taxonomic ontology that was generated from the NCBI taxonomy database. One design feature of this ontology was the inclusion of terms for taxonomic ranks (e.g., family, genus, etc.) as a separate ‘tree’ of terms with the same ontology. The ontology file contained two root nodes, one for taxon terms, the other for taxonomic rank terms. We had long felt that ranks should exist in a separate ontology (more correctly a vocabulary) that could be shared across ontologies for different taxonomic groups. After several rounds of discussion on the obo-discuss list, we were invited in January to add the taxonomic rank vocabulary to the OBO library of ontologies of interest.

This acceptance allowed us both to register the rank vocabulary and to finally strip out the tree of rank terms from TTO and replace the internal rank tags with ‘has_rank’ links to terms in the (external) rank vocabulary. However, the new rank vocabulary is more than just the set of ranks that we used in tagging taxa in TTO.  The rank vocabulary  incorporates rank terms from two additional sources: first the rank terms that appear in the NCBI taxonomy itself, and also terms from a rank vocabulary developed for TDWG.  We hope that other taxonomic ontologies will be able to make use of this vocabulary.

More recently, we have gone back to the NCBI taxonomy and added cross references between our terms and lexically identical names in NCBI.  As TTO’s names are mostly drawn from the Catalog of Fishes, the exact relation between TTO terms and NCBI names is not, in some cases clear, which lead to the decision to leave the relationship at the level of a cross reference.

In the same release (156), common names, contributed by FishBase were added as synonyms.  As of now, approximately 16,000 taxa have common names with cross references back to their source in FishBase.  We hope to be able to add more common names and eventually include appropriate language tags to these names.

I’ve already started work on our next integration target, but I’ll save that for a later post.

The second day focused on applications to biological data and web services.  A discussion on “taxonomic intelligence” was particularly illuminating.  It provided an example of how different communities can share a set of identifiers for species, for example, yet provide their own set of statements about the taxonomy relating those species.  Each community can draw conclusions relevant to its preferred taxonomy using data associated with the same species.

The afternoon focused on the SSWAP project, led by Damian Gessler.  SSWAP is a protocol which uses OWL documents to describe the inputs and outputs relevant to a web service.  Interestingly, users of these web services would submit their input in the very same OWL model used for service descriptions.

In Phenoscape, we are using OBO ontologies rather than RDF and OWL and storing our ontological annotations in OBD, a datastore tailored for OBO technologies which provides its own very effective reasoner.  However, this workshop provided a great opportunity to stay up to date with semantic web standards and explore how to make our data compatible with and part of the global semantic web.  In addition, St. John’s College was a great meeting location – it is a small college with a wonderful natural landscape in the hills outside of Santa Fe.