A list

• 10 things that have consequences for how we model anatomy
• Focus on requirements of taxonomy
• Not all doom and gloom

0 - An example item

0 - ... and its Consequences

• One
• Two
• * A consequence that requires no action
• ? A (more) poorly thought out point

1 - Taxonomists have published in natural language for over 200 years

1 - Consequences

• * No model is needed?
• A model must support what they do (diagnose species)
• A model must let taxonomists flow naturally from NL observations to a formal representation
• A majority(?) of statements in the model will be lossy

2 - Life is complex

2 - Consequences

• Giant empty matrix, with few links
• Linking nodes must be very carefully thought out
• Models must isolate labels from concepts
• RDF labels are not enough to identify "the same" nodes in disparate named graphs
• Model must support evolving refinement

3 - Life is observed once, described, then ignored

Most anatomical descriptions will never be revised

3 - Consequences

• Integrating previously described species will require NL processing
  • Let's be real- for most species descriptions will not be redone in a "native" semantic format, we simply don't have the time/resources
• ? While our model's semantics must evolve, the statements/observations behind them won't
• ? Our model must have "versioning" to reference the NL algorithm that were used
• Our model must differentiate NL processed statements from "native" statements

4 - Taxonomists present species descriptions

4 - Consequences

• Is it important to provide a model that does more than what taxonomists want to do?
• Few, though growing numbers of taxonomists, uniquely identify the specimens in their study
  • If we can't identify specimens, how are we going to identify their parts
• An instance anatomy sensu our workshop has never been published

5 - Reference ontologies (for gross anatomy) do not exist for most of life

5 - Consequences

• ? Instance anatomies can't be merged at finer levels of granularity
• ? Search/filter will need to be done on values, and therefor be variously unsatisfying
• We must tackle the problem from top down and bottom up

6 - Converting human NL to a model is lossy process

6 - Consequences

• The model should emphasize minimizing loss of meaning
• No one model will fix this issue
• Similarly, conversion between representation models will be lossy, also suggesting minimizing loss is a goal- how to ensure this with model semantics?

7 - Humans can't agree

7 - Consequences

• Semantics need to be fuzzy enough to draw conclusions across independent observations
• It is unlikely we can have 1 graph of observations (e.g. instance anatomy) per entity being described
• Merging/syncing data from the same, or different models remains, as always (sigh), the hardest problem

8 - Model organisms are described differently

8 - Consequences

• Models must account from difference from "normal"/"wild type" type statements
• We must work hard to escape from this relative approach less it persist into a more general usage

9 - All models need interfaces

9 - Consequences

• Interfaces bias what and how models get used
• Semantic models could be completed buried behind the symbolic representations that are used to capture data
• Should our model be built to pre-adapt attributes/properties to the "visual" interface that will capture their instances?

10 - People want to use semantic phenotypes for AI, VR, and other buzzworthy things

10 - Consequences

• Data may need specific attributes to make them useful for AI and other approaches
  • We should talk with Jim/the SCATE project
• We need 3D coordinates for anatomy terms
  • See our vronto project

11 - ? Data are always generated with a purpose in mind

11 - Consequences

• ? Published descriptions are not "inaccurate" (sensu our discussion in the workshop), they have fulfilled their purpose (and been accepted by a community of peers)
• ? Even if we model data, we can't escape the baggage that is its original purpose?
• ? Even in a universal model, some argue that data derived for one purpose are not suitable for another purpose

12 - URIs/IRIs

12 - Consequences

• ? It is hard to maintain and generate resolvable URIs at scale
  • ? A universal model requires unique ids, this comes with significant issues such as services that ensure minted URIs are indeed unique
• It is almost certain that we need centrally managed data "lakes"/"oceans"/repositories that our data can find their way to