BFO/DOLCE Primitive Relation Comparison (ISMB Bio-Ont SIG 2009)

A. Patrice Seyed

BFO is built for ontologies of sciences. BFO and RO are used in the OBO Foundry. DOLCE was built by Guarino. BFO Continuant/Endurant is synonymous with DOLCE’s Endurant/Perdurant(/Quality/Abstract are also included). For specific dependence, a dependent continuant ‘inheres in’ an independent continuant (relationship between particular and type). Specialized dependence relations are ‘quality of’, ‘function of’, and ‘role of’. In DOLCE, a quality can be a ‘quality of’ another quality, endurant or perdurant. There are still some questions over when to use function or role, as identified by a number of talks at today’s SIG. And from a BFO perspective, qualities only inhere in independent continuants.

The constitutes relation. X constitutes Y when there are properties of X which are accidental to X but essential to Y. BFO does not include consititution, but it does have ‘role of’, which is the closest it has. They want to find a way to continue to merge, and figure out how to integrate a conceptualist-centric ULO with a realist-centric ULO.

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Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

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CiTO, the Citation Typing Ontology and its use for the annotation of reference lists and visualization of citation networks (ISMB Bio-Ont SIG 2009)

David Shotton

They’ve added characterization to citations present on websites using CiTO. You can encode citation frequencies using CiTO, too. Another purpose is to characterize the cited works themselves. In doing so, he has adopted the FRBR entity model. For an example, they made FRBR entities for Gone with the Wind. The move, while based on the novel is a new creative work. The novel can have a variety of expressions. For these and more reasons it makes it a good example.

SWAP also uses the FRBR classification, and CiTO has adopted terminology and definitions from SWAP. However, SWAP is  concerned with the metadata describing a single work. CiTO describes aspects of scholarly works out of scope for SWAP (e.g. relations between citing and cited works). Another similar ontology is BIBO, but that deals with legal works, and BIBO lacks terms essential to CiTO. BIBO is essentially orthogonal with CiTO. Further, BIBO doesn’t use the FRBR classification. SWAN is another ontology designed to characterize rhetorical statements with text. It is limited in scope and still under development (just a cygnet!) but clearly relevant to CiTO. They’re starting a collaboration with Tim Clark.

What is the proper home for this? It’s not a biological ontology, so maybe doesn’t belong in OBO? They also want a nice authoring tool.

FriendFeed Discussion: http://ff.im/4xwI9

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

Annotation of SBML Models Through Rule-Based Semantic Integration (ISMB Bio-Ont SIG 2009)

Allyson Lister et al.

I didn’t take any notes on this talk, as it was my own talk and I was giving it. However, I can link you out to the paper on Nature Precedings and the Bio-Ontologies programme on the ISMB website. Let me know if you have questions!

You can download the slides for this presentation from SlideShare.

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Representing the Immune Epitope Database in OWL (ISMB Bio-Ont SIG 2009)

Jason Greenbaum et al. (Bjoern Peters presenting)

When a virus infects a mouse, the pieces of the virus end up on the cell surface where they are accessible to the immune cells.  Epitopes are the things that are recognized on the cell surface in this case. It is a role of a material entity that is realized when it binds to an adaptive immune receptor. Here, context is key: What immune receptor for the epitope? What host? What happened to the host previously? And remember, instances are not universals.

The goal of the IEDB is to catalogue and make accessible immune-epitope-related information. There are 10 full-time PhD-level curators, with 50,000 epitopes. They’ve completed about 99% of infectious disease and 90% allergies – next are autoimmune responses. This leads to large amounts of complex data which we have to deal with.

The IEDB development cycle is ontology development -> db (re)design -> content curation and back again. ONTIE = Ontology of Immune Epitopes at http://ontology.iedb.org. Ontie is intended to be superceded as other ontologies take up the terms present there. They’re database tables are aligned with the ontology, which relies very heavily on OBI. This is a method of “ontologic normalization” of the database. Data migration and consistency enforced by rule-based validation engine.

This alignment of ontology to db happened so we could have an easy db export to OWL.

FriendFeed Discussion: http://ff.im/4xqyz

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

Modelling biomedical experimental processes with OBI (ISMB Bio-Ont SIG 2009)

Larisa Soldatova et al.

OBI was created to meet the need for a standardised vocabulary for experiments that can be shared across many experiment types. OBI is community driven, with over 19 communities participating. It is a candidate OBO Foundry ontology, is complementary to existing bio-ontologies, and reuses existing ontologies where possible. It uses various ULOs for interoperability: BFO, RO, and IAO. material_entity class was introduced into BFO on request of the OBI developers, for instance.

OBI uses relations from BFO, RO, and IAO as well as creating relations specific to OBI. OBI relations could be merged with other relations ontologies in future. They try to have as few relations as possible. Two use cases were outlined in this paper. Firstly, analyte measuring assay, where you draw blood from a mouse and determine the concentration of glucose in it. Use case 2 was a vaccine protection study, where you measure how efficiently a vaccine induces protection against virulent pathogen infection in vivo.

Allyson’s thoughts: Disclosure: I am involved in the development of OBI.

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Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

Simple, ontology-based representation of biomedical statements (ISMB Bio-Ont SIG 2009)

…through fine-granular entity tagging and new web standards

Matthias Samwald et al.

He’s trying to make sense of a very large number of complicated interactions and connections between molecular phenomena. He’s part of the SW’s HCLSIG as part of W3C. Example: huge queries in the neurocommons knowledgebase, where they span multiple data sources. But there are still very few tools suitable for end users. He came up with <a>Tag, or associative tags. Here, you tag statements, not documents. You tag with entities, and not strings. It’s implemented with a bookmarklet. There is more to the bookmarklet than meets the eye: it is RDFa + SIOC + domain ontologies / terminologies. RDFa – allow you to imbed OWL and RDF snippets within HTML. Doing things this way means we don’t need to build everything from scratch, as can use existing HTML tools, e.g. move to a wordpress blog. aTags can also be generated by NLP web services.

Linked-data paradigm: entities have URIs that can be resolved to yield further information. Developers need understandable and predictable data structures across distributed data sources. They also don’t want to reinvent the wheel, and develop GUIs simply. Balance semantics and pragmatics.

FriendFeed discussion: http://ff.im/4xj6c

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

RKB: a SW knowledge base for RNA (ISMB Bio-Ont SIG 2009)

Michael Dumontier et al.

Wants to capture the structural features and interactions of RNA. Capture types and their relations, represent dynamic / context-specific knowledge, populate the KB with PDB structural data and MC-Annotate interactions, and answer questions about RNA structure. Looked at textbooks, review articles, book chapters, expert knowledge. Their upper-level ontology (ULO) was NULO, based on BFO/RO.

Contextual modelling of nucleic acids: base stacking varies in different NMR etc models. Then he described the Leontis-Westhof Nomenclature, where you describe the edges of the base as participating in the reaction. So a more sophisticated nomenclature was developed that was based on this called LW+ Nomenclature, where they divvied up the edges into a set of faces.

They want to capture information about residues, edges/faces, cis/trans nucleotide orientations, and across parallel/antiparallel strands. Base stacking involves inter-nucleobase interactions that involve London forces. Wanted to capture both numbers and a description of what is going on. They’ve used two roles: FacingAwayRole and FacingTowardsRole. There is both an endo and exo role for sugar puckering. Situational modeling assures that objects are represented by a single entity throught their lifetime.

RKB is popoulated with PDB and MC-Annotate and it is all represented in RDF. The population involved 3 steps: assigningnames, asserting class membership, and ?. So they can then ask the database things using DL queries. RKB is also accessible via SPARQL.

They’re now working with the RNA Ontology Consortium. They want to publish as part of the Bio2RDF netowkr, and extend the structure description with backbone angles.

NULO: there is a logic mapping bewteen NULO and BFO. They’ve relaxed restrictions where it is unclear what BFO’s stance is. It was unclear if you made certain statements you would still fit in with the idea in BFO.

FriendFeed Discussion: http://ff.im/4xhmC

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!