Mappings between existing data and RDF triples

[javadch]

• [x] Choice of Mapping Tool
• [x] Learning R2RML language
• [x] Autmotaing R2RML
• [x] Scaling the mapping rules for all tables
• [x] Mapping tables columns to Ontology
• [x] Push Owl file for each tables

BD_table | Priority (10 is high) | short description | Foreign Keys

1. • [x] cell_morphology | 10 | morphology data like size/shape/gram stain and motility | ID_strains/ID_reference
2. • [x] colony_morphology | 10 | Colony shape and colour, incubation time, hemolysis | ID_strains/ID_reference
3. • [x] culture_pH | 10 | data on pH values | ID_strains/ID_reference
4. • [x] culture_temp | 10 | data on temperature values | ID_strains/ID_reference
5. • [x] halophily | 10 | halophilic data | ID_strains/ID_reference
6. • [x] strains | 10 | central table on strain data including species name, culture collection numbers and type strain status
7. • [x] enzymes | 8 | data on enzyme activity | ID_strains/ID_reference
8. • [x] met_antibiotica | 8 | Antibiotica data | ID_strains/ID_reference
9. • [x] met_production | 8 | Metabolite production data | ID_strains/ID_reference
10. • [x] met_util | 8 | Metabolite utilization data | ID_strains/ID_reference
11. • [x] origin | 8 | data on the origin and enrichment of a culture, sample type is the basis for the Isolation Source TAGS | ID_strains/ID_reference
12. • [x] oxygen_tolerance | 8 | data on oxygen relation | ID_strains/ID_reference
13. • [x] reference | 8 | Metadata for the references
14. • [x] risk_assessment | 8 | data on pathogenicity and risk assessment | ID_strains/ID_reference
15. • [ ] spore_formation | 8 | data on spore formation ability of the bacteria | ID_strains/ID_reference
16. • [x] culture_medium | 7 | Medium data for cultivation, not standardized data | ID_strains/ID_reference
17. • [ ] met_test | 7 | Metabolite test data: methyl red, Voges-Proskauer, Indole and Citrate | ID_strains/ID_reference
18. • [ ] nutrition_type | 7 | Nutrition type, rather general data on the nutrition of a bacterium | ID_strains/ID_reference
19. • [ ] GC_content | 6 | GC content of the DNA | ID_strains/ID_reference
20. • [ ] multicellular_morphology | 6 | data on multicellular complex building ability, not standardized | ID_strains/ID_reference
21. • [ ] pigmentation | 6 | data on pigmentation of the bacteria | ID_strains/ID_reference
22. • [ ] sequence | 6 | metadata on sequences > might be split into Genome and 16S sequence in the near future | ID_strains/ID_reference
23. • [ ] FA_meta | 5 | metadata for fatty acid profiles
24. • [ ] FA_profile | 5 | fatty acid profiles, connect metadata with FK_FA_META >PK FA_meta | FK_FA_META/ID_strains/ID_reference
25. • [ ] IS_cat1 | 5 | Vocabulary of the Isolation Source TAGS Cat1 (highest)
26. • [ ] IS_cat2 | 5 | Vocabulary of the Isolation Source TAGS Cat2 (middle) | FK_Cat1 |  
27. • [ ] IS_cat3 | 5 | Vocabulary of the Isolation Source TAGS Cat3 (lowest) | FK_Cat2 |  
28. • [ ] IS_link | 5 | Isolation Source Tag data | Cat1_link/Cat2_link/Cat3_link/ID_strains/ID_origin
29. • [ ] met_antibiogram | 4 | Antibiogram test data | ID_strains/ID_reference
30. • [ ] met_antibiogram_meta | 4 | Metadata for antibiogram tests
31. • [ ] murein | 4 | Murein (cell wall) data | ID_strains/ID_reference
32. • [ ] tolerance | 4 | Data on tolerances against compounds, non standardized data | ID_strains/ID_reference
33. • [ ] strain_history | 4 | data on the history of a strain | ID_strains/ID_reference
34. • [ ] compound_production | 3 | not so well structured data on compound production, can be later moved to metabolite and enzyme tables | ID_strains/ID_reference
35. • [ ] kit_api_20A | 3 | Test data from API 20A | ID_strains/ID_reference
36. • [ ] kit_api_20A_meta | 3 | Metadata for API 20A
37. • [ ] kit_api_20E | 3 | Test data from API 20E | ID_strains/ID_reference
38. • [ ] kit_api_20E_meta | 3 | Metadata for API 20E
39. • [ ] kit_api_20NE | 3 | Test data from API 20NE | ID_strains/ID_reference
40. • [ ] kit_api_20NE_meta | 3 | Metadata for API 20NE
41. • [ ] kit_api_20STR | 3 | Test data from API 20STR | ID_strains/ID_reference
42. • [ ] kit_api_20STR_meta | 3 | Metadata for API 20STR
43. • [ ] kit_api_50CHac | 3 | Test data from API 50CHac | ID_strains/ID_reference
44. • [ ] kit_api_50CHac_meta | 3 | Metadata for API 50CHac
45. • [ ] kit_api_50CHas | 3 | Test data from API 50CHas | ID_strains/ID_reference
46. • [ ] kit_api_50CHas_meta | 3 | Metadata for API 50CHas
47. • [ ] kit_api_CAM | 3 | Test data from API CAM | ID_strains/ID_reference
48. • [ ] kit_api_CAM_meta | 3 | Metadata for API CAM
49. • [ ] kit_api_coryne | 3 | Test data from API Coryne | ID_strains/ID_reference
50. • [ ] kit_api_coryne_meta | 3 | Metadata for API Coryne
51. • [ ] kit_api_ID32E | 3 | Test data from API ID32E | ID_strains/ID_reference
52. • [ ] kit_api_ID32E_meta | 3 | Metadata for API ID32E
53. • [ ] kit_api_ID32STA | 3 | Test data from API ID32STA | ID_strains/ID_reference
54. • [ ] kit_api_ID32STA_meta | 3 | Metadata for API ID32STA
55. • [ ] kit_api_LIST | 3 | Test data from API LIST | ID_strains/ID_reference
56. • [ ] kit_api_LIST_meta | 3 | Metadata for API LIST
57. • [ ] kit_api_NH | 3 | Test data from API NH | ID_strains/ID_reference
58. • [ ] kit_api_NH_meta | 3 | Metadata for API NH
59. • [ ] kit_api_rID32A | 3 | Test data from API rID32A | ID_strains/ID_reference
60. • [ ] kit_api_rID32A_meta | 3 | Metadata for API rID32A
61. • [ ] kit_api_rID32STR | 3 | Test data from API rID32STR | ID_strains/ID_reference
62. • [ ] kit_api_rID32STR_meta | 3 | Metadata for API rID32STR
63. • [ ] kit_api_STA | 3 | Test data from API STA | ID_strains/ID_reference
64. • [ ] kit_api_STA_meta | 3 | Metadata for API STA
65. • [ ] kit_api_zym | 3 | Test data from API ZYM | ID_strains/ID_reference
66. • [ ] kit_api_zym_ec | 3 | Metadata for API ZYM
67. • [ ] ncbi_all | 3 | Help table for matching to NCBI | ID_strains
68. • [x] observation | 3 | Unstructured, not standardized data that does not fit into other data fields | ID_strains/ID_reference
69. • [x] biosample | 2 | NCBI Biosample data | ID_strains
70. • [x] countries | 1 | help table for translating ISO/Country
71. • [x] culture_collection | 1 | help table for structuring and analyzing culture collection numbers

A nice tutorial for Knowledge Graph Construction Using Declarative Mapping Rules is also available at https://github.com/oeg-upm/kgc-tutorial-iswc2020

In this step, we map from multiple sources using the developed ontology. Then we generated an RDF file and store it as a knowledge graph.

A more description is given at https://github.com/TIBHannover/diaspora/issues/2

[Gautamshahi]

write 3-4 items and itemise the number what we need to do, eg-table choosing, curate data, things which output is important.

[Gautamshahi]

Choice of mapping tool

With the advancement of the Knowledge graph, several approaches and tools came up to convert the relational database and unstructured data into RDF triples.

We have tested several tools which suit our requirement like r2rml-parser-master(https://github.com/nkons/r2rml-parser), Rocket RML (https://semantifyit.github.io/RocketRML/). Due to lack of proper documentation and ease of uses, we decided to use the SDM-RDFzier tool (https://github.com/SDM-TIB/SDM-RDFizer). The SDM-RDFizer, an interpreter of mapping rules that allows the transformation of (un)structured data into RDF knowledge graphs.

[Gautamshahi]

Learning R2RML language

To write a mapping file, we need to understand the syntax of the mapping language. Following the W3C standard, R2RML is well suited for our task. Detailed documentation is available at https://www.w3.org/TR/r2rml/

[Gautamshahi]

Autmotaing R2RML

Till now, there is no way to write the mapping rule automatically, we need to write the mapping rules manually. Covering each table and its properties is time-consuming so we also use Mapeathor (https://github.com/oeg-upm/Mapeathor). Mapeathor translates your mapping rules specified in spreadsheets to a mapping language.

[javadch]

dev of the following items will continue on #21

• Mapping tables columns to Ontology
• Mapping tables columns to Ontology