Integrating Heterogeneous Data Sources in the Web of Data

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1 Integrating Heterogeneous Data Sources in the Web of Data Franck Michel 1

2 More data sources More opportunities 2

3 Example: study history of zoological knowledge First Natural History Encycloedia, Conservation biology* Archaeological excavation * 3

ontologies, Conservation biology* Archaeological excavation

4 Example: study history of zoological knowledge Knowledge formalizations Controlled vocabularies, taxonomies domain ontologies, Conservation biology* Archaeological excavation * 4

5 LOD Cloud: 10K datasets, 150B Statements On the Web In RDF Under open licences Interlinked Linking Open Data cloud diagram A. Abele, J.P. McCrae, P. Buitelaar, A. Jentzsch and R. Cyganiak. 5

6 Publishing legacy data in RDF raises tricky questions Metadata Vocabularies? Create links? Raw data? Data Translate into RDF? 6

7 Agenda Describe the translation of heterogeneous data into RDF Choose vocabularies to represent RDF data Access the RDF data produced The key importance of metadata 7

8 Agenda Describe the translation of heterogeneous data into RDF Choose vocabularies to represent RDF data Access the RDF data produced The key importance of metadata 8

9 Data Sources Have Heterogeneous Data Models Documents Relational DB ID NAME Native XML DBs Object-Oriented Graph 9

10 Agenda Describe the translation of heterogeneous data into RDF HTML data with RDFa CSV data Relational data NoSQL data Choose vocabularies to represent RDF data Access the RDF data produced The key importance of metadata 10

11 Agenda Describe the translation of heterogeneous data into RDF HTML data with RDFa CSV data Relational data NoSQL data Choose vocabularies to represent RDF data Access the RDF data produced The key importance of metadata 11

RDFa: RDF in HTML attributes https://www.w3.

org/"> <div resource="/jdev2017" typeof="event"> <h2

property="startdate">2017-07-04</span></p>.

<a property="url href="http://devlog.cnrs.

12 RDFa: RDF in HTML attributes <body vocab=" <div resource="/jdev2017" typeof="event"> <h2 property="title">jdev 2017</h2> <p>date: <span property="startdate"> </span></p>... <p>t2 - Ingénierie et web des données. <a property="url href=" </a> </p> </div> </body> prefix sch: < < rdf:type sch:event ; sch:title "JDEV 2017"; sch:startdate " " ; sch:url < 12

13 Agenda Describe the translation of heterogeneous data into RDF HTML data with RDFa CSV data Relational data NoSQL data Choose vocabularies to represent RDF data Access the RDF data produced The key importance of metadata 13

CSVW: CSV on the Web https://www.w3.org/2013/csvw/ GET trees.

Content-Type: text/csv Link: <http://example.org/trees.

org/ns/csvw", {"@language": 2, Emerson "en"}], St, Liquidambar styraciflua, 2010/06/02 "url": "trees.

14 CSVW: CSV on the Web GET trees.csv ID, Street, Species,Trim Cycle, Inventory Date 1, Addison Av, Celtis australis, 2010/10/18 2, Emerson St, Liquidambar styraciflua, 2010/06/02 Content-Type: text/csv Link: < rel=" " ID, Street, Species,Trim Cycle, Inventory Date 1, Addison Av, Celtis australis, 2010/10/18 { "@context":[" {"@language": 2, Emerson "en"}], St, Liquidambar styraciflua, 2010/06/02 "url": "trees.csv", "dc:title": "Trees", "dc:license": { "@id": " "dc:modified": {"@value": " ", "@type": "xsd:date"}, "tableschema": { "columns": [ { "name": "ID", "titles": ["ID", "Identifier"], "datatype":"string", "required": true }, { "name": "Street", "titles": "On Street", "dc:description": " ", "datatype": "string" },... ], "primarykey": "ID", "abouturl": "#id-{id}" }} 14

15 Agenda Describe the translation of heterogeneous data into RDF HTML data with RDFa CSV data Relational data NoSQL data Choose vocabularies to represent RDF data Access the RDF data produced The key importance of metadata 15

16 Direct Mapping of a RDB to RDF Table: PEOPLE ID FNAME WROTE (FK BOOK/ID) 7 Catherine 22 8 Olivier 22 <PEOPLE/ID=7> rdf:type <PEOPLE>. <PEOPLE/ID=7> <PEOPLE#FNAME> "Catherine". <PEOPLE/ID=7> <PEOPLE#WROTE> <BOOK/ID=22>. <PEOPLE/ID=8> rdf:type <People>. <PEOPLE/ID=8> <PEOPLE#FNAME> "Olivier". <PEOPLE/ID=8> <PEOPLE#WROTE> <BOOK/ID=22>. 16

Custom Mapping of a RDB to RDF Table: PEOPLE ID FNAME WROTE (FK BOOK/ID) 7 Catherine 22 8 Olivier 22 <http://unice.fr/staff/7> rdf:type ex:teacher. <http://unice.fr/staff/7> foaf:name "Catherine".

17 Custom Mapping of a RDB to RDF Table: PEOPLE ID FNAME WROTE (FK BOOK/ID) 7 Catherine 22 8 Olivier 22 < rdf:type ex:teacher. < foaf:name "Catherine". < dc:contributor < < rdf:type ex:teacher. < foaf:name "Olivier". < dc:contributor < Existing vocabularies 17

Custom Mapping of a RDB to RDF with R2RML <#MapPeople> rr:logicaltable [ rr:tablename "PEOPLE" ]; rr:subjectmap [ rr:template "http://unice.

18 Custom Mapping of a RDB to RDF with R2RML <#MapPeople> rr:logicaltable [ rr:tablename "PEOPLE" ]; rr:subjectmap [ rr:template " rr:class ex:teacher; ]; rr:predicateobjectmap [ rr:predicate foaf:name; rr:objectmap [ rr:column "FNAME" ]; ]. < rdf:type ex:teacher. < foaf:name "Catherine". < rdf:type ex:teacher. < foaf:name "Olivier". 18

19 Agenda Describe the translation of heterogeneous data into RDF HTML data with RDFa CSV data Relational data NoSQL data Choose vocabularies to represent RDF data Access the RDF data produced The key importance of metadata 19

Mapping of a NoSQL DBs to RDF with xr2rml { "id": 106, "firstname": "John", "emails": [ "john@foo.com", "john@example.org" ] } xr2rml <#MapMbox> xrr:logicalsource [ xrr:query "db.people.

20 Mapping of a NoSQL DBs to RDF with xr2rml { "id": 106, "firstname": "John", " s": [ "john@foo.com", "john@example.org" ] } xr2rml <#MapMbox> xrr:logicalsource [ xrr:query "db.people.find({' s':{$ne: null}})" ]; rr:subjectmap [ rr:template " ]; rr:predicateobjectmap [ rr:predicate foaf:mbox; rr:objectmap [ xrr:reference "$. s.*"; rr:termtype rr:literal ] ]. < foaf:mbox "john@foo.com". < foaf:mbox "john@example.org". 20

21 Many methods for many types of data sources HTML RDFa, Microformats XML AstroGrid-D, SPARQL2XQuery, XSPARQL CSV/TSV/Spreadsheets XLWrap, Linked CSV, CSVW, RML JSON TARQL, JSON-LD, RML Relational Databases D2RQ, R 2 O, Ultrawrap, Triplify, SM R2RML: Morph-RDB, ontop, Virtuoso NoSQL xr2rml (MongoDB), ontop (MongoDB), [Mugnier et al, 2016] Multiple formats RML, TARQL, Apache Any23, DataLift, SPARQL-Generate M.L. Mugnier, M.C. Rousset, and F. Ulliana. Ontology-Mediated Queries for NOSQL Databases. In Proc. AAAI

22 Agenda Describe the translation of heterogeneous data into RDF Choose vocabularies to represent RDF data Access the RDF data produced The key importance of metadata 22

23 Direct mapping: create my own vocabulary Can be derived from an existing schema May seem easier: I do whatever I want But no added semantics, need to link my vocabulary with other ones 23

24 Custom Mapping: reuse existing vocabularies Large variety of existing vocabularies But may be difficult to find the appropriate one Partial coverage of the domain Granularity: too high (cumbersome), too low (useless) Different points of view Frequently, a mixed approach is used 24

25 Agenda Describe the translation of heterogeneous data into RDF Choose vocabularies to represent RDF data Access the RDF data produced The key importance of metadata 25

26 Two approaches to translate existing data sources in RDF Graph Materialization (ETL like) SPARQL ID NAME Query rewriting SPARQL Virtual Graph 26

27 A large variety of approaches Direct Mapping Custom Mapping Graph Materialization AstroGrid-D CSVW RDFa Linked CSV DataLift XSPARQL JSON-LD R2RML RML Any23 Query Rewriting TARQL Ultrawrap R 2 O XLWrap Triplify Virtuoso D2RQ SPARQL2XQuery R2RML ontop xr2rml SPARQL-Generate 27

28 Agenda Describe the translation of heterogeneous data into RDF Choose vocabularies to represent RDF data Access the RDF data produced The key importance of metadata 28

29 Making datasets discoverable and reusable requires high-quality, comprehensive metadata 29

30 30

31 Metadata are the key to enable dataset reuse Context Access Meaning Identification, authors, dates, license, version, reference articles Format, structure, location (dwld), query method What do the data represent? What concepts, entities, semantics? Interpretation Units (cm or inches, left/right) Provenance Statistics Acquired with which equipment, parameters, protocols? Derived from which dataset? With which processing? Dataset-level or entity-level provenance Number of triples per property of class, links to other datasets 31

32 Several works normalize metadata descritpions and how to use metadata CSVW: CSV on the Web DCAT: Data Catalog Vocabulary DCAT extensions, application profiles W3C Dataset Exchange Working Group VoID: Vocabulary of Interlinked Datasets HCLS: Health Care & Life Sciences Dataset Profile 32

33 Thank you! 33

A Generic Mapping-based Query Translation from SPARQL to Various Target Database Query Languages

A Generic Mapping-based Query Translation from SPARQL to Various Target Database Query Languages F. Michel, C. Faron-Zucker, J. Montagnat I3S laboratory, CNRS, Univ. Nice Sophia 1 Towards a Web of Data