Turning three thesauri into a Global Agricultural Concept Scheme
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Cynthia Parr, National Agricultural Library, at RDA 5th Plenary Meeting, IG Agriculture Data Interoperability Session in San Diego (CA, US) on the 9th of March 2015
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Turning three thesauri into a Global Agricultural Concept Scheme
- 1. Turning three thesauri into a Global Agricultural Concept Scheme March 9, 2015 Research Data Alliance Session II: Good Practices towards opening data in agriculture Cynthia Parr, National Agricultural Library @cydparr, cynthia.parr@ars.usda.gov
- 2. Outline 1. Background 2. Starting point: three thesauri 3. Creating GACS 4. Challenges 5. Next steps and future of GACS
- 3. Background ● Food and Agriculture Organization of the UN ● CABI (UK) ● National Agricultural Library (US) Each organization maintains a thesaurus of terms and concepts related to agriculture -- concepts like rice, ricefield aquaculture, and plant pests.
- 4. Separate thesauri, separate databases Create GACS as a glue linking them together
- 5. Global Agricultural Concept Scheme (GACS) agreement October 2013 to conduct feasibility study 1. To improve the semantic interoperability of thesauri maintained by FAO, CABI, and NAL. 2. To identify and provide core concepts broadly supported across the three thesauri. 3. To achieve efficiencies of scale by maintaining the core concepts in cooperation.
- 6. Consultants Osma Suominen (Finland) osma.suominen@helsinki.fi Tom Baker (Germany) tom@tombaker.org
- 7. Creating GACS Phase One: Analysis of Thesauri
- 8. AGROVOC CAB Thesaurus NAL Thesaurus 140,000 concepts, >1.4M terms 32,000 concepts, >1.2M terms 53,000 concepts, >200k terms English, Spanish, Portuguese, German, Czech, Persian, Polish, Hindi, French, Italian, Russian, Japanese, Hungarian, Chinese, Slovak, Thai, Lao, Turkish, Korean, Arabic, Telugu ... English, Spanish, Portuguese, Dutch + many languages with lower coverage English, Spanish All thesauri represented using SKOS
- 9. Overlap estimate Obtained via automatic mappings created using AgreementMakerLight
- 10. Long tail distribution (in AGRIS) 10,000 concepts cover nearly 99% of occurrences in metadata
- 11. Requirements and Wishes 1. An integrated view and bridge of existing thesauri 2. Reuses thesaurus development work, incl. translations 3. Compatible with existing databases 4. Based on RDF technologies: URIs, SKOS etc. 5. Available as Linked Open Data Currently building GACS Beta, a proof-of-concept implementation attempting to fulfill most requirements
- 12. Creating GACS Phase Two: Proof of Concept
- 13. Selection of top 10,000 concepts Each partner organization provided the 10,000 concepts most frequently used in their respective databases. These lists of concepts were modified as follows: ● added all countries (from AGROVOC) ● added organisms hierarchy all the way to the top
- 14. Automated mappings Created using AgreementMakerLight software between the full thesauri, for completeness AgreementMakerLight was top performer at OAEI 2014 ontology mapping competition!
- 15. Human evaluation of mappings Created Google Docs spreadsheets using the lists of selected concepts and the auto-generated mappings. Three sheets with circa 10,700 rows each. Mappings manually evaluated by staff of partner organizations. Evaluated 60 to 150 rows/hour, total evaluation time over 300 hours so far. Currently projected to take 500-600 hours for GACS Beta.
- 16. Forming GACS concepts by merging the source concepts and aggregating their information rice UF paddy UF paddy rice cereals UF feed cereals UF small grain cereals (grain) Oryza sativa UF Oryza glutinosa UF Oryza indica UF Oryza japonica UF Oryza sativa … (subsp, var etc.) Oryza UF Padia UF rice (plant) agrovoc:c_5435 cabt:82917 nalt:56271 exactMatch agrovoc:c_5438 cabt:82935 nalt:56277 exactMatch agrovoc:c_1474 cabt:26247 exactMatch agrovoc:c_6599 cabt:101613 nalt:56293 exactMatch (actually we use SKOS, not traditional thesaurus tags)
- 17. Size of GACS GACS GACS Beta will have around 14,000 of the most used concepts
- 18. Quality evaluation Using the qSKOS and Skosify tools that can find and correct problems in SKOS vocabularies [1], we can detect ● missing, invalid or overlapping concept labels ● anomalies in concept hierarchy, e.g. cycles ● ...and many other kinds of problems. Many problems are expected due to merging of concepts within GACS, but most should be automatically corrected. [1] Osma Suominen and Christian Mader: Assessing and Improving the Quality of SKOS Vocabularies. JoDS, 3(1) 2014.
- 19. Demo of GACS Alpha in Skosmos http://bit.ly/1Gjf5jl
- 20. Additional mapping rounds Need to perform 2-3 more smaller mapping rounds in order to ensure that all necessary concepts have been fully mapped between all source thesauri
- 21. Lessons already learned ● It is hard to sustain focus on mapping beyond circa five hours per day. ● Mapping reveals issues with both the source and target thesauri -- areas for improvement, or errors, fixable in collaboration. ● Starting with the 10,000 most-used concepts shines a light on parts of thesauri that may long have lacked attention. ● Starting small, with a core, avoids the potential stress of over-committing resources. ● Mapping provides an incentive to adopt open-data technologies that have proven beneficial in other areas.
- 22. Challenges
- 23. Differences in modeling Q: Are taxonomic organism names (e.g. ‘Bos taurus’) different concepts than the common names (‘cattle’)? ● sometimes there is no 1:1 match and/or context of use is different ● the source thesauri all have different policies No final answer yet...
- 24. Lumps clusters of concepts mapped one-to-several, several-to-one, or in spirals
- 25. Next steps and future of GACS
- 27. Beyond GACS Beta? Q: Can GACS replace existing agricultural thesauri? ● definitely not with GACS Beta due to smaller scope/size ● a future GACS may be an alternative for some scenarios, but not all uses of existing thesauri because o they cover areas beyond agriculture o existing systems and processes (publication, automatic indexing…) depend on current thesauri In future, more partners are expected and the scope of GACS can be adjusted.
- 28. Thank you Reports available on the FAO AIMS site: http://aims.fao.org/community/agrovoc/blogs/phase-one-gacs-approved-read-reports GACS Alpha: http://tester-os-kktest.lib.helsinki.fi/gacsdemo/en/ Slides prepared by Osma Suominen and Tom Baker osma.suominen@helsinki.fi tom@tombaker.org @cydparr
Editor's Notes
- I am presenting this talk on behalf of the GACS partnership – I myself am not directly involved but none of them could be here. In particular, I should credit up front Osma Suominen and Tom Baker who prepared the presentation, and Lori Finch from the NAL who helped me understand and prepare. I will do my best to try to answer your questions or can help direct you to one of those who is directly involved.
- Each organization has its own thesaurus which they maintain, and one or more databases that make use of the concepts within that thesaurus. Currently no semantic compatibility - cannot do cross-database queries.
- The three partner organizations hired consultants to do most of the work that is being presented here one from Finland, one from Germany currently teaching in Korea but from Germany
- The three thesauri vary a lot. The CAB thesuaurs has a huge number of unique concepts compared to the other two, though because Agrovoc expresses those concepts Ina large number of languages, it has almost as many term as CAB Thesaurus. NAL thesaurus, relatively new, has a smaller number of terms but more concepts than Agrovoc. Note however, that all three share two languages: English & Spanish
- To determine what else was shared. OsmaSuominen used an automated open source tool for ontology matching: AgreementMakerLight. The “common core” shared by all three thesauri is about 13,500 concepts. Here we show some of the concepts that are not shared (you can see in the CAB thesaurus many of these are scientific names). Some examples of the terms that ARE shared are shown here.
- Interestingly, if you look at metadata records from AGRIS, the FAO database of publications and other materials, and what AGROVOC concepts are used in those records, you can see it is a very long tail distribution – of the 32K concepts in Agrivoc, 10,000 concepts cover nearly 99% of the occurences. Similar results were found for CAB abstracts and Agricola.
- So the conclusion of Phase 1 is that phase two should focus on integrating and bridging these existing thesauri in order to re-use the existing work (including the translations into other languages). There was a desire to maintain compatibility with the existing databases – good pipelines and tools already exist, so there is no desire to replace the thesauri currently being used by the three partners. The partners decided the work should be based on RDF and made available as linked open data. So, Phase 2 would be to create a proof of concept GACS beta impelmentation.
- The partnership is now nearing the end of phase 2. So I will now describe how that proof of concept is being developed.
- First, each organism took their top 10Kf concepts In case they were not in the top 10K, the partner also added all Country names from Agrovoc and, if the core concept was an organism additional names were added to support the taxonomic hierarchy all the way to the top.
- OAEI ontology alignment evaluation initiative Pairwise in this direction: NALT-AGROVOC, AGROVOC-CABT, and CABT-NALT
- For example, Oryza (the genus for rice) has exact match in each of three, so that term will receive its own GACS URIC Cereal is a broader term than rice, Oryza is a broader term than O. sativa, rice and Oryza sativa have an associative relation – not as specific as an exactMatch. If you want to know about Oryza sativa you may also be interested in the concept rice. UF is "used for" in SKOS it is "alt label"
- Might have errors in mapping that Vascular tissues -- is that of animal or plant systems? does it truly map to these (the code on the arrow is the row in themapping spreadsheet) should overweight and obesity map be an exact match AC1501 (agrivoc to cabi spreadsheet line 1501)(1 = exact match) Partners will be resolving some of these specific cases as well the larger policy questions by the end of march when there’s a meeting in Wallingford UK. Half of the problems are taxonomy problems, not surprising given the difficulty of staying up to date and the different authorities and sources.
- anticipate phase 3 to address infrastructure for auto processes editorial environment publishing platform (using SKOSMOS right now)
- CABI includes CAB health (beyond agricutlure) CABI uses theres for publishing NAL uses theirs for automated indexing ... More partners expected, eg. CGIAR...
- I will tweet these