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Term suggestions from ENVIRONMENTS EOL #100
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GoogleCodeExporter
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pbuttigieg
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pbuttigieg |
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Hi @evangelospafilis, any new input? |
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New strategy: use ENVIRONMENTS-EOL results to auto-generate habitat classes.
or a count of ENVO classes:
@cmungall: Vangelis will provide the mapping of each EOL page to ENVO classes (similar to the quoted text above). We can then autopopulate classes such as "Heterodontus zebra habitat" defined with simple 'overlaps' relations. We may have to handle things like conditions as suggested below: Heterodontus zebra habitat Further, @cmungall, is there a way to add some sort of weight to relations? If the counts were not all '1' and |
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I assume this will be experiment outside the main ENVO to begin with (this will create 1000s of classes). Should just be a few lines of groovy code to make the ontology. Weighting relations: I think the easiest way is more specific relations. For marine biome, this should be part_of for all sharks I'm aware of. This would have to come from prior knowledge (or some kind of statistical weighting of results). If we want to weight continental shelf higher, what does that mean? That the shelf is a causal hub in the environmental system that supports the shark? If so we can have a more specific chain relation. Or just promote to Would we just do taxonomy leaves? For higher taxa we could use DL-Learner to learn the common features. |
evangelospafilis
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Sep 23, 2015
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The ENV-EOL raw dataset has just been updated and can be found under the "noParentTerms" version lists the ENVO terms as they result from the term identification in text. the larger dataset in addition to the "noParentTerms" includes also an extended version in which all parent terms (via IS_A, PART_OF traversal) for every match are reported. This dataset is in-sync with the EOL (Encyclopedia of Life) text contents of 2015-09-16 |
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Thanks @evangelospafilis! Which ENVO release was used for this? |
It would be very interesting to compare the results of the DL-Learner with the information mined from EOL's higher taxa descriptions. Do they match? We'll need to use a comparable taxonomy file to aggregate. This can and should be done downstream (not for the December milestone) and would be an interesting part of a project. I would be a fan of dealing with the leaves and then aggregating up. This is unlikely to recreate taxonomy (e.g. the case of the Galapagos penguins). |
evangelospafilis
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Sep 23, 2015
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@pbuttigieg Which ENVO release was used for this?
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These may just turn out to be a union of the features of the child taxa
yes, this would be experimental, no dependencies on this?
the kind of thing I would hope to see come out of DL-learner type approaches would be:
but this may require bringing in some geographic knowledge, possible species interactions too |
evangelospafilis
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Oct 21, 2015
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The new ENV-EOL dataset is now available under: NB: the format has been modified: citation related infomation has been added. |
GoogleCodeExporter commentedMar 28, 2015
Original issue reported on code.google.com by
lynn.sch...@gmail.comon 8 Apr 2014 at 6:15