Data Archiving and Networked ServicesLinked Census DataSemantics for Knowledge Discovery of thePastAlbert Meroño-Peñuela01/03/2013DANS is een instituut van KNAW en NWO
Main goal: cross queries ?
Main goal: requirements• Schema flexibility: do not commit to a specific schema• Linkage – Internally (e.g between tables), to make relations explicit – Externally • Harmonization datasets (e.g. HISCO, AC) • Enriching datasets (e.g. labour strikes, book publications)• Inference: of new knowledge (e.g. ink_manufacturer(X) & ink_manufacturer chemical |= chemical(X))• Publication: as open data for researchers on the Web (through Service Architectures)
Main goal: RDF datamodel
CEDAR development cycle,iteration 1• Gathering: only one file• Conversion: TabLinker, small table size• Querying: simple, ad-hoc SPARQL + trivial visualization
Iteration 1: conversion • Supervised Excel to RDF conversion • Python feat. xlutils, xlrd, rdflib libs • Intended for complex layouts that cannot be handled with automatic csv2rdf scripts • Maps workbooks to the RDF Data Cube vocabulary • Layout needs to be manually annotated https://github.com/Data2Semantics/TabLinker
CEDAR development cycle,iteration 2• Gathering: arbitrary number of files • But, what do we have?• Conversion: arbitrary table size, annotations• Querying: SPARQL with mappings, top level ontologies
Iteration 2: gathering Hey, what’s there?Inventory of the dataset•How many files do we have?•How many tables/sheets?•How many variables?•How many annotations? TabExtractor (Python feat. xlrd, Levenshtein libs)https://github.com/CEDAR-project/TabExtractor
Iteration 2: gatheringSubset of the dataset•Miniproject 1 – 1889 – Occupational census – Province Noord-Brabant – 1 table•Miniproject 2 – 1859, 1869, 1879, 1889 – Population census – Province Noord-Brabant – 4 tables
Iteration 2: conversion• Iteration 1 converted to RDF only Excel cells• Some cells have annotations attached – Value corrections: 5 8 – Explanations, descriptions: Number includes 2 people of unkown age – Inconsistencies: Sum does not add up• Iteration 2 produces proper named graphs for annotations
Iteration 2: conversion Annotations data model
Iteration 2: conversion Annotations data model
Iteration 2: conversion
Iteration 2: data quality• Annotations can improve data quality• Model has to be extended with actions – If sum doesn’t add up Retrieve numbers from other tables/sources – Appropriate vocabularies
Iteration 2: data quality• Measure of data quality? Benford’s Law – Data distributions in censuses meet Benford’s Law – Demo available!
Iteration 2: querying• Things to be mapped – Occupations (HISCO) – Municipalities (Amsterdamse Code) – Housing types – Religions – Etc.• Converted the HISCO and AC mappings to RDF (https://github.com/CEDAR-project/Harmonize) – Linked to the tables RDF
Iteration 2: linking HISCO
Iteration 2: linking AC
Iteration 2: linking
Iteration 2: linking• Issue: HISCO is too generic (top-down approach) – Class 21110 too abstract: General Manager – Visualization of SPARQL HISCO mappings• Issue: AC works at the municipality level – Other geographical harmonizations?• Need for year-level ontologies – Classification systems are different• R script to do bottom-up approach Classification extractor (https://github.com/albertmeronyo/OccupationOntology) – Automated removal of non-related cols and rows – Introduction of redundancy (‘Id.’ values) – Removal of totals – Work in progress: ontology merging
Concept drift ? ? t1 t2 tn• Models drift over time• Classes merge, split, change their properties (beroepenklassen)• Although, some core meaning remains (shoemakers)• Can we automatically identify and align drifted models?
Conclusion: milestones• Complete inventory of the dataset (w/ metadata generation)• Translation to RDF – Raw data – Annotations – Harmonization/linking• Successful data quality experiments (Benford’s Law)• Useful software – TabLinker (Excel/CSV to RDF) – TabExtractor (Excel/CSV metadata collector) – Harmonize (HISCO/AC to Census linker) – OccupationOntology (bottom-up occupation ontology extractor)
Conclusion: future work• Better software – TabLinker: automate mark-up process – TabExtractor: improve and publish inventory output – Harmonize: improve HISCO/AC datamodels – OccupationOntology: extend to housing types, religions, etc.• Concept drift literature on drifting models (Kuukkanen 2008, Gonçalves et al. 2009, Shenghui et al. 2010)• Semantic Web literature on modeling geographical change (Kauppinen 2010) – Integrate with AC dataset?• Link meaningful datasets with the census – Labour strikes – Book publications – More?
Thank you http://www.cedar-project.nl firstname.lastname@example.orgData Archiving and Networked Services (DANS)Anna van Saksenlaan 10 | 2593 HT Den HaagPostbus 93067 | 2509 AB Den Haag070 3446 484 | email@example.com | www.dans.knaw.nlKVK 54667089 | DANS is een instituut van KNAW en NWO