20110725 ibc xml

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This document discusses a schema for describing and exchanging the content of taxonomic publications in a way that allows both human and machine access. It proposes using semantic markup like XML to tag elements in publications like names, descriptions, and references in a way that links related data across sources. This would allow content to be more accessible for tasks like data mining while maintaining context. The schema is part of ongoing work by Plazi to apply semantic markup to digitize existing publications and structure new ones for improved dissemination and reuse of taxonomic knowledge.

Technology

A Schema for Description and Exchange of TaxonomicPublication's Content Donat Agosti, Terry Catapano, Lyubomir Penev & Guido Sautter Plazi, Bern, Switzerland 25. July 2011, IBC, Melbourne

“JSTOR's the one that should be in prison, man, for locking up knowledge.”Hufpost Politics, July 19, 2011http://www.huffingtonpost.com/2011/07/19/huffpost-hill----gang-vio_n_904027.html

An example from the Neurocommons text mining pilot: ,[object Object]

extracted graph of 30,000+ relationships and 5,500 genes and proteins“protein-protein interaction networks” John Wilbanks, Neurocommons

27,266 papers 128,437 papers 41,985 papers 4,563 papers 10,365 papers In a semantic Web environment (where machines talk to each other and do most of our work), data need to be able to talk to each other: “protein-protein interaction networks” John Wilbanks, Neurocommons

“protein-protein interaction networks” John Wilbanks, Neurocommons It will open up scientific literature for data mining

It is about digesting millions of pages: >>100 M pages taxonomic literature25M scientific publications / year25K journals>2K with zoological taxonomic descriptions18K descriptions of new species / year

<tax:treatment> <tax:nomenclature> <tax:name> <tax:xid source="HNS" identifier="193329"/> <tax:xmldata> <dc:Genus>Mystrium</dc:Genus> <dc:Species>leonie</dc:Species> </tax:xmldata> Mystrium leonie Bihn & Verhaagh, new species </tax:name> <tax:status>n. sp.</tax:status> Fig 1 D - F </tax:nomenclature> <tax:div type="description"> <tax:p>HOLOTYPE WORKER: TL 3.95, HL 1.02, HW 0.95, CI 93, SL 1.30, SI 137, PW 0.73, ML 0.38. Mandible outer margin strongly curving to a sharp apical tooth, the apex parallel to the anterior clypeal margin. (Holotype with material in mandibles, so mandibles and anterior clypeus described below from paratypes.) Median clypeus .... </treatment>

Azteca instabilis Would then read like <tax:name> <tax:xid source=“LSID" identifier=“urn:lsid:biosci.ohio-state.edu.osuc_concetps:13452"/> Link to external database <tax:xmldata> Normalization of data <dc:Genus>Azteca</dc:Genus> <dc:Species>instabilis</dc:Species> </tax:xmldata> Azteca instabilis </tax:name>

transformations from XML html print pdf archiving rdf database

Plazi workflow: GoldenGate editor based mark up and linking - Get LSID from Hymenoptera Name Server for names; ZooBank? ,[object Object],- Get bibliographic Guids from bioguid (or EDIT?) - Get bibliographic Metadata from HNS (MODS) ,[object Object]

.....- Get geographic long/lat from geonames.org Legacy publications

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This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).

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Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots. To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.

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1. A Schema for Description and Exchange of TaxonomicPublication's Content Donat Agosti, Terry Catapano, Lyubomir Penev & Guido Sautter Plazi, Bern, Switzerland 25. July 2011, IBC, Melbourne

2. WHY?

3. disseminate access knowledge

4. New York Times, July 19, 2011

5. “JSTOR's the one that should be in prison, man, for locking up knowledge.”Hufpost Politics, July 19, 2011http://www.huffingtonpost.com/2011/07/19/huffpost-hill----gang-vio_n_904027.html

6. Open Access

8. CNS classified abstracts: 874,727

9. text mining recognized: 368,688

10. text mining processed: 94,381

11. extracted graph of 30,000+ relationships and 5,500 genes and proteins“protein-protein interaction networks” John Wilbanks, Neurocommons

12. 27,266 papers 128,437 papers 41,985 papers 4,563 papers 10,365 papers In a semantic Web environment (where machines talk to each other and do most of our work), data need to be able to talk to each other: “protein-protein interaction networks” John Wilbanks, Neurocommons

13. “protein-protein interaction networks” John Wilbanks, Neurocommons It will open up scientific literature for data mining

14. HOW?

15. accessfor human ANDmachine

16. It is about digesting millions of pages: >>100 M pages taxonomic literature25M scientific publications / year25K journals>2K with zoological taxonomic descriptions18K descriptions of new species / year

17. PDF is not enough

18. data and information in context

19. semantic markup

20. context of content

21. XMLeXtended Markup Language

22. <tax:treatment> <tax:nomenclature> <tax:name> <tax:xid source="HNS" identifier="193329"/> <tax:xmldata> <dc:Genus>Mystrium</dc:Genus> <dc:Species>leonie</dc:Species> </tax:xmldata> Mystrium leonie Bihn & Verhaagh, new species </tax:name> <tax:status>n. sp.</tax:status> Fig 1 D - F </tax:nomenclature> <tax:div type="description"> <tax:p>HOLOTYPE WORKER: TL 3.95, HL 1.02, HW 0.95, CI 93, SL 1.30, SI 137, PW 0.73, ML 0.38. Mandible outer margin strongly curving to a sharp apical tooth, the apex parallel to the anterior clypeal margin. (Holotype with material in mandibles, so mandibles and anterior clypeus described below from paratypes.) Median clypeus .... </treatment>

23. content in a complex e-environment

24. linking

25. Azteca instabilis Would then read like <tax:name> <tax:xid source=“LSID" identifier=“urn:lsid:biosci.ohio-state.edu.osuc_concetps:13452"/> Link to external database <tax:xmldata> Normalization of data <dc:Genus>Azteca</dc:Genus> <dc:Species>instabilis</dc:Species> </tax:xmldata> Azteca instabilis </tax:name>

26. definition of XML tags DTD schema

27. transformations from XML html print pdf archiving rdf database

28. legacyTaxonXTaxpubprospective

29. how to use XML?

30. legacy publications

31.

32. CBOL

33. NCBI

34. specimen

35. images

36. .....- Get geographic long/lat from geonames.org Legacy publications

37. linked data