World Sense-Making using Linked Data.Faculty Seminar Talk at Birmingham City University, Birmingham, United Kingdom
- 1.World Sense-Making using Linked Data Tope Omitola(joint work
with Prof. Nigel Shadbolt)Faculty Research Seminar Talk, Birmingham
City University, UKThurs 8 Dec. 2011 1
2. Thank You Thank you for inviting me. 3. World Sense-Making
using Linked DataTope Omitola 3 4. Talk Outline EnAKTing: Its story
From the Web to Semantic Web to Linked Data Public Sector Datasets:
Publication and Consumption Findability of Appropriate Data Sources
Service Descriptions Provenance and Trust in Linked Data 5. What is
EnAKTing? EPSRC-funded project. Addressing 3 key research problems;
(1) how to buildontologies quickly that are capable of exploiting
thepotential of large-scale user participation, (2) how wequery an
unbounded web of linked data, (3) how tovisualise, explore, browse
and navigate this mass ofdata. Project Leaders: Prof. Sir Tim
Berners-Lee, Prof. DameWendy Hall, and Prof. Nigel Shadbolt. 6.
From the Web to Semantic Web to Linked Data The Web of Data
Problems with the Web of Document RDF Linked Data 7. The Web of
Data (a.k.a Semantic Web/Linked Data) Traditional Web of Documents
Internet, Documents, Links Documents in HTML Links using URLs HTTP
for document access and transfer 8. Data Silos on the Current
WebAPIHTMLHTML XML 9. Some more problems with Web of Documents
Difficult to Integrate Data Example Use Case: Making a Travel Plan
Data Integration by looking and typing Slow Unproductive Workflow
Difficult for apps to make sense of HTML text 10. Solutions Use RDF
to give some structure to the data RDF subject predicate object RDF
links things, not just documents, and theyare typed 11. RDF is a
language (for data)Words URIsand literal textNouns and Verbs
Classes andPropertiesSentence structureRDF Statements
(triples)ParagraphsRDF GraphsFootnotes URIs[Domain Name
Service]DictionariesRDF Schemas Generic grammar for languages of
description Functions as native language, second language, or
pidgin. 12. RDF and Ontology The AAA Slogan: Anyone can say
Anythingabout Any topic. s po . (subject predicate object .) "Tony
Benn . RDF is used to build ontologies; a formalrepresentation of
shared knowledge by a setof concepts within a domain and
therelationships between them Examples: Finance ontology;
MusicBrainz,music ontology; GO, gene ontology, etc 13. What is
Linked Data? Data, data, everywhere: We are surroundedby data:
School performance, car fuelefficiency, etc Data help us to make
better decisions You can discern the shape and structure of
anentity by looking at the data it generates Data shapes
conversations and markets 14. What is Linked Data? Linked Data:
Framework where data is a first classcitizen on the Web Evolving
the current Web into a Global DataSpace TimBL: 4 principles of
Linked Data Use URIs as names for things, Use HTTP URIs,
Whensomeone looks up a URI, provide useful information,using the
standards (RDF, etc), Include links to otherURIs, so that they can
discover more things 15. The Web of Linked Data Link everything. No
silos.ThingThing ThingThingThing Thing 16. The Web of Linked Data
Linked Data (Semantic Web ) is a graphdatabase: 17. Linked Data
Advantage comes from linking the RDF(s)together.17 18. Some Linked
Datastores BBC NY Times Guardian DBpedia Geonames 18 19. Linking
(Linked) Open Data cloudlinkeddata.org Many of the datastores are
being linkedtogether to form a network/graph. 19 20. Linked Data In
summary: Linked Data provides: RDF A standardized data access
mechanism, HTTP Hyperlink-based data discovery, using URIs
Self-descriptive data, through using sharedvocabularies 21.
Government Linked Data Explosion of Government (Linked) Open Data
efforts and projects.data.gov, data.gov.uk, data.gov.au Examples:
22. Public Sector Datasets Inherent value in opening up public
government data Systems and Services can be tailored to citizens
priorities. Likely questions citizens may need answers to are:
Where can I find a good school, a good investmentadvisor, a good
employer? 23 23. Public Sector Datasets (contd.) Integration of
datasets enables more complexquestions to be asked and answered
Some examples: http://www.planningalerts.com/
http://ishortman.com/projects/expendituremap/ Governments freeing
up their data. Holy grail is information integration: Meshing.24
24. Issues we focus on Findability of appropriate data sources
SEARCH: Look at the data sources EXTRACT: Slicing of data sources
INTEGRATE: Unifying the views EXPLORE: Answering the questions. 25.
Examples of Government Public Data (csv) 26. Examples of Government
Linked Data (rdf) 27. Workflow Identify Dataset Design/ Select
Vocabularies Extract and convert data into RDFPublish as Linked
DataConsume Linked Data (Application) 28 28. Publishing your data
as Linked Data: Some Things toConsider How do you choose a good URI
to name things? There areguidelines for this. Examples:
http://dbpedia.org/resource/Wildlife_photographyTope Omitola @ Univ
of Southampton:http://id.ecs.soton.ac.uk/person/24123. Describing a
Data Set using: voiD (the Vocabulary of InterlinkedDatasets)
Choosing and Using Vocabularies to Describe Data (SKOS, RDFS,OWL,
scovo) Sourcing datasets: Where do you get the datasets from (e.g.
SemanticWeb search engines, manual search, etc) Choice of join
points: When you have different datasets, where do youjoin them
together Data normalization: using RDF make things easier.
Alignment of datasets 29. Architecture Infer new Dataconcepts and
Integration relationshipsSPARQL RDFGatherersDataand RDF
TriplestoreSources Extractors(4store) Services30 30. Data
Publication Challenges and Solutions Research Questions: In our
case, dealing with data that are centred aroundthe United Kingdoms
democratic system, Using geography data from the UKs Ordnance
Surveyas the join-point with data for criminal statistics,Members
of Parliament, mortality rates, etc. Sourcing the datasets Many
government data sets are in pdf, html, or xlsfiles, so automatic
discovery methods are not possible(yet), Went through manual
discovery process, searching forthem, We found some in pdf, html,
and in xls, We decided against pdf and html 31 31. Data Publication
Challenges and Solutions (contd.) We went for data in xls format.
Why? Ability to source from a wider range of public
sectordomains.Data SourceFormat DatasetPublicwhip.org.ukHTML MP
votes records, etcTheyworkforyou.com XML dump Parliament,
ParliamentexpensesHomeoffice.gov.ukExcelRecorded crime(England,
2008/09)Statistics.gov.ukExcelHospital Waiting List(England
2008/09)Performance.doh.gov.uk ExcelMortality rates(England
2008/09)Ordnancesurvey.co.uk Linked DataUKs mapping agency32 32.
Data Publication Challenges and Solutions (contd.) Data
normalisation. RDF as our standard model. Data conversion to RDF.
Python + Java. Modelling the datasets: Multi-dimensional,used
SCOVO. 33 33. Data Publication Challenges and Solutions (contd.)
Crime dataset:Table 7.03 Recorded crime by offence group by police
force area, English region andWales, 2008/09 RecordedNumbers
crimePolice force area, English Total Violence Sexual Robbery
Burglary OffencesOther FraudCriminal DrugOtherregion and
Walesagainst offences against theftanddamage offences offences 1the
vehicles offencesforgeryperson NumbersCleveland 55,09410,662566404
6,1755,224 13,697905 13,7462,636 1,079Durham45,074 7,435476170
6,2264,9409,674835 13,0271,327
964Northumbria105,23419,14798973211,418 11,620 24,0422,909
27,1785,166 2,033North East Region205,40237,2442,0311,30623,819
21,784 47,4134,649 53,9519,129 4,076 :TimePeriodrdf:typeowl:Class;
rdfs:subClassOfscovo:Dimension. :TP2008_09 rdf:type :TimePeriod.
:GeographicalRegionrdfs:subClassOfscovo:Dimension; dc:title "Police
force area, English region and Wales".
:CriminalOffenceTyperdf:typeowl:Class;
rdfs:subClassOfscovo:Dimension.34 34. Some Issues in Linked Data
Co-referencing, i.e. different sources referring tothe same
entities by different names. Cardiff
inDbpediahttp://dbpedia.org/resource/Cardiff
orhttp://dbpedia.org/resource/Cardiff_City Cardiff
inGeonameshttp://sws.geonames.org/2172349/ Which Cardiff shall we
use? Solution: sameas service from Southampton35 35. 36 36.
Alignment of datasets37 37. Alignment of Datasets (contd.) Asserted
owl:sameAs relations between dataset geoand O.S. (using string
matching) For example, the English county of Cumbria wasaligned as
the following:http://www.w3.org/2002/07/owl#sameAs. A few special
cases. Yorkshire and the HumberRegion vs Yorkshire & the Humber
NHS Trust were labelled differently: e.g. South Tyneside NHSTrust
had no equivalence in the OS. So used Google Maps. 38 38. Examples
of Government Public Data (csv) 39. Examples of Government Linked
Data (rdf) 40. Recap: Data Publication Sourcing : Many not in RDF
yet. Some in html,pdf, and xls. We chose xls. Selection of RDF as
the normal form. Used scovo to model multidimensional data. We used
owl:sameAs to assert equivalencesbetween geo regions. We used
string matching. Some did not work,e.g. Yorkshire and the Humber.
Some have noequivalent OS entities, so we had to go viaGoogle Maps
API41 41. Consuming Linked Data How do you visualize linked data
sets. Linked Data browsers, e.g. Disco, Tabulator. Linked Data
Search Engines, e.g. Sig.ma,Falcons, Sindice. Domain-specific
Applications and Mashups,e.g. dayta.me(from Southampton), US
GlobalForeign Aid Mashup. 42. Data Consumption Application acts as
an aggregator ofinformation based on users postal (zip) code.
Generates data views based on geographicalregion of postal code.
Shows political representatives (MPs) forconstituencies, their
voting records, and theirexpenses.43 43. Data Consumption
(contd.)44 44. Data Consumption(contd.) Challenges: The lack of UIs
to quickly browse, search or visualiseviews on a widerange of
differently modelled data, Lack of suitable tools which allow
efficientaggregation and presentation of datato the UI frommultiple
datasets, Data consumers having partial knowledge of domainand
finding it difficult to understand the domain andthe data being
modelled.Points out the need for atoolset that helps developers
givebetter description ofthe domain being modelled.45 45. Recap:
Publish and Consume Information Integration; one of the holy grails
Problems with data sources. Different formats, etc, RDF can act as
a standard model. Publication to RDF. Challenges. Solutions. scovo
for multi-dimensional data string matching and its complexities
Consuming the data. Challenges. Solutions. Aggregating data based
on zip code Complexities of geo boundaries We have re-published the
data we generated into thelinked data cloud: EnAKTing
datasetswww.enakting.org/enakting/datasets46 46. Some of our
Outputs http://geoservice.psi.enakting.org: service to
discovergeographical resources, http://map.psi.enakting.org/:
integrate different PSI LinkedData sources by querying Backlinking
service, http://backlinks.psi.enakting.org: service to discover
back-links in PSI, http://void.rkbexplorer.com/: describes the
contents ofdata sets, enabling discovery and reuse of resources,
http://bagatelles.ecs.soton.ac.uk/psi/: platform forintegrating
several PSI catalogues from the Web
http://4sreasoner.ecs.soton.ac.uk/ Scalable Reasoning in4store; 4sr
is a branch of4store where backward chainedreasoning is implemented
http://apps.seme4.com/see-uk/ : Visualization tool forsome UK data
47 47. Our solutions/apps 48. Our solutions/apps 49. Findability of
Appropriate Data Sources Service Descriptions How do you tell the
world about your newlinked data sets? Provide good service
descriptions of your datasets Use vocabulary of Interlinked
Datasets 50. Vocabulary of Interlinked Datasets (VoID) allows
description of datasets and theirinterlinking, e.g. "there are 200k
links of typegr: predicates between dataset X and dataset Y;and
dataset Y mainly offers data about homesand X about mortgages . A
dataset: a set of RDF triples published,maintained or aggregated by
a single provider,and accessible on the Web, e.g.:DBpedia a
void:Dataset .allows the description of RDF links betweendatasets
(using void:Linkset). 51. Three Areas of voiD General Metadata
Access Metadata Structural Metadata 52. voiD (contd.)General
metadata: the datasets title,description, date of creation, the
creator,publisher, licence, subject(s), etc;:DBpedia a
void:Dataset;dcterms:title "DBPedia";dcterms:description "RDF data
extracted from Wikipedia";dcterms:contributor
:FU_Berlin;dcterms:modified
"2008-11-17"^^xsd:datedcterms:contributor:OpenLink_Software. 53.
Access metadata: describes how the RDF data(set) can be accessed
using sparql e.g.:DBpedia a void:Dataset;void:sparqlEndpoint. using
URI lookup,Sindice a void:Dataset ;void:uriLookupEndpoint . using
rdf dumps,:NYTimes a void:Dataset;void:dataDump. 54. Structural
metadata describes the structure and schema ofdatasets naming some
representative example entites for a dataset stating if datasets
entities share common URIs:DBpedia a void:Dataset;void:uriSpace
"http://dbpedia.org/resource/ .Stating the vocabularies used in a
dataset:LiveJournal a void:Dataset;void:vocabulary.Providing
statistics about datasets, e.g.expressing the number of RDF triples
or thenumber of entities of a dataset.:DBpedia a
void:Dataset;void:triples 1000000000 ; void:entities 3400000. 55.
Publishing voiD files as void.ttl in the root directory of the
site, with alocal hash URI for the dataset,
e.g.http://example.com/void.ttl#MyDataset. Using the root URI of
the site, such ashttp://example.com/,as the dataset URI, and
servingboth HTML and an RDF format via contentnegotiation from that
URI. Embedding the VoID description as HTML+RDFainto homepage of
dataset, with a local hash URIfor the dataset, yielding URI such
ashttp://example.com/#MyDataset. 56. Why is voiD useful -- voiD
Discovery By enabling the discovery and usage of linkeddatasets. A
sitemap such as http://www.yoursite.com/sitemap.xmlreferences
void.ttl, and sitemap.xml added robots.txt. A search engine crawls
the website indexingvoid.ttl plus a cache of the rdf triples
referenced inthis void file. through backlinks:void:inDataset.
Through a well-known URI: void.ttl can be placedin
/.well-known/void on any Web server ,
e.g.http://www.example.com/.well-known/void . 57. @prefix void: .
@prefix scovo: . a void:Dataset;foaf:homepage;rdfs:label
"crime.psi.enakting.org Linked Data Repository";dcterms:date
"2010-09-13T11:30:29"^^xsd:date;dcterms:title
"crime.psi.enakting.org Linked Data Repository";foaf:nick
"crime";dcterms:description "United Kingdoms crime statistics per
region for the year 2008/09, provided by theUnited Kingdom Home
Office. Dataset
provenance:http://www.homeoffice.gov.uk/rds/pdfs09/hosb1109chap7.xls";dcterms:publisher;void:statItem
[scovo:dimensionvoid:numberOfTriples; rdf:value 4988; rdfs:label
"4,988 triples; ];void:subset [a void:Linkset; rdfs:label
"crime.psi.enakting.org CRS ->
http://data.ordnancesurvey.co.uk/";void:subjectsTarget;void:objectsTarget;void:linkPredicatecoref:duplicate;void:statItem
[rdfs:label "133 URI equivalences"; rdf:value 133;
scovo:dimensionvoid:numberOfTriples; ] ]. 58. Provenance and Trust
in Linked Data Whom do you trust on the Web? 59. Provenance and
Trust Mash-ups, aggregation, integration, data re-use. How do you
elicit Reliability and Accuracy? Generate trust by revealing as
much information ofyou as possible. Enables consumers to decide the
quality andtrustworthiness of your data. Useful for Data
Discovery/Mining + QueryPlanning. 60. Different kinds of Provenance
When was x derived (when-provenance). How was x derived
(how-provenance). What data was used to derive x (what-
provenance). Who carried out the transformation(s) from whence x
came (who-provenance). 61. Provenance Models for Linked Datasets
Provenance Vocabulary Ontology 62. Provenance Models for Linked
Datasets (contd) Open Provenance Model 63. Provenance Models for
Linked Datasets (contd) Provenance for Datasets (voidp)
http://www.enakting.org/provenance/voidp/ 64. voiD Provenance
Extension voidp Designed to be simple and lightweight. Mainly for
(RDF) data publishers. Includes necessary information of the
process, its inputs, and outputs. Basis is simple: An agent runs a
process on a data (or dataset) to get another data (or dataset).
Agent Process Data Data . @prefix voidp: . 65. voidp Classes and
Predicates voidp:ProvenanceEvent:items under provenancecontrol.
voidp:actor: actor, person, group, software or physicalartifact,
involved in this provenance event. voidp:certification:used to
contain dataset signatureelements voidp:contact: contact details of
whom to contact shouldpeople have queries about this dataset.
voidp:item:the provenance characteristics of a data itemunder
provenance control. voidp:processType: the type of transformation
or conversionprocedure carried out on the items source
voidp:resultingDataset: dataset that is the result of
thisprovenance event. voidp:sourceDataset: source dataset for the
data item underprovenance control. 66. voidp: A Concrete
Example@prefix voidp: . a void:Datasetvoidp:activity [ a
voidp:Provenance;voidp:item [foaf:name;rdf:typescovo:Dataset;
rdfs:label "RECORDED CRIME STATISTICS 1898 - 2001/02"@en
;prv:createdBy [rdf:typeprv:Actorprv:performedBy
;];voidp:originatingSource ;voidp:hashValue
"12335353535"^^xsd:string ;voidp:processType
;to:hasBeginning"2010-10-24T21:32:52"^^xsd:dateTime
;to:hasEnd"2010-10-25T09:32:00"^^xsd:dateTime ;]. 67. voidp in the
WildThe Datalift projecthttp://data.lirmm.fr/ontologies/vdpp
data.southampton.ac.ukhttp://graphite.ecs.soton.ac.uk/browser/?uri=http
%3A%2F%2Fid.southampton.ac.uk%2Fdataset%2F
jargon%2Flatest.rdfhttp://graphite.ecs.soton.ac.uk/browser/?uri=http
%3A%2F%2Fdata.southampton.ac.uk%2Fdumps%
2Fjargon%2F2011-11-10%2Fjargon.rdf 68. Conclusion The Web of Data
is real The Web of Data is here Its time to get on board 69.
http://www.enakting.org/enakting/[email protected] Slides
at:http://www.slideshare.net/TopeOmitola/omitola-birmingham-cityuniv
Questions? 70
