Linked data and its role in the semantic web. Dave Reynolds, Epimorphics

Transcription

1 Linked data and its role in the semantic web Dave Reynolds, Epimorphics

2 Roadmap What is linked data? Modelling Strengths and weaknesses Examples Access other topics image: Leo flickr.com

3 Linked data intro

4 Linked data... publishing data on the web to enable integration, linking and reuse across silos

5 Can t we just publish data as files? pdf easy to read and publish Excel csv allows further processing and analysis processing without need for proprietary tools But... structure of data not explained no connection between different data sets, silos static and fixed can t retrieve just slices relevant to problem

6 Linked data Apply the principles of the web to publication of data The web: is a global network of pages each identified by a URL fetching a URL gives a document pages connected by links open, anyone can say anything about anything else

7 Linked data Apply the principles to the web to publication of data The linked data web: is a global network of things each identified by a URI fetching a URI gives a set of statements things connected by typed links open, anyone can say anything about anything else Linked data is data you can click on

8 Example schools information

9 Example schools information Cardiff High School label district phase Secondary Cardiff

10 Example schools information Cardiff High School label district phase school:phaseofeducation_secondary label Cardiff

11 Example schools information Cardiff High School label district phase school:phaseofeducation_secondary label Cardiff contains ward extent contains parish GML:

12 Example schools information Cardiff High School label district phase school:phaseofeducation_secondary same as label Cardiff contains ward extent contains parish GML:

13

14 Linked data principles Use URIs as names for things Use HTTP URIs so that people can look up those names When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL) Include links to other URIs, so that they can discover more things Pattern of application of semantic web stack

15 Linked open data cloud: 2007 Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.

16 Linked open data cloud: 2009 Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.

17 Linked open data cloud: 2010 Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.

18 Data.gov.uk linked datasets and APIs

19 Data.gov.uk visualizations on top of linked data

20

21 Ordnance survey

22 Environment agency - data, API, visualizations

23 BBC integration and site design

24 E-commerce and rich snippets Overstock.com Peek-cloppenburg.de

25 Internal use

26 Open? Linked open data = linked data + open data

27 Modelling

28 Modelling Thing, entity, concept... resource resource being described abstract concept real world thing data item, particular measurement document identify by URI provide information making statements about those resources identifier NOT a container c.f. UML open schema critical to open extensibility and integration similar to Entity-Attribute-Value modelling

29 Modelling RDF Resource Description Framework Statement, triple, logical assertion Subject Predicate Object

30 Modelling RDF Statement, triple, logical assertion Subject Predicate Object some school has a name/label some literal

31 Modelling RDF Statement, triple, logical assertion Subject Predicate Object /id/school/ has a name/label Cardiff High School

32 Modelling RDF Statement, triple, logical assertion Subject Predicate Object /id/school/ f-schema#label Cardiff High School

33 Modelling RDF Statement, triple, logical assertion Subject Predicate Object school: rdfs:label Cardiff High School where school: = rdfs: =

34 Modelling RDF Statement, triple, logical assertion Subject Predicate Object school: rdfs:label Cardiff High School school: ont:districtadministrative la:00pt la:00pt rdfs:label Cardiff

35 Modelling RDF Statement, triple, logical assertion Subject Predicate Object school: rdfs:label Cardiff High School school: ont:districtadministrative la:00pt la:00pt rdfs:label Cardiff school: Cardiff High School rdfs:label ont:districtadministrative la:00pt rdfs:label Cardiff

36 Modelling RDF Statement, triple, logical assertion Subject Predicate Object school: rdfs:label Cardiff High School school: ont:districtadministrative la:00pt la:00pt rdfs:label Cardiff la:00pt rdfs:label

37 RDF Syntaxes RDF/XML normative Turtle more human readable/writable being standardized RDFa embed in (X)HTML [others omitted]

38 Modelling RDF RDF/XML syntax Subject Predicate Object school: rdfs:label Cardiff High School school: ont:districtadministrative la:00pt la:00pt rdfs:label Cardiff <rdf:rdf xmlns:rdf=" xmlns:ont=" xmlns:la=" xmlns:school=" xmlns:rdfs=" <rdf:description rdf:about=" <rdfs:label>cardiff High School</rdfs:label> <ont:districtadministrative> <rdf:description rdf:about=" <rdfs:label>cardiff</rdfs:label> </rdf:description> </ont:districtadministrative> </rdf:description> </rdf:rdf>

39 Modelling RDF Turtle syntax Subject Predicate Object school: rdfs:label Cardiff High School school: ont:districtadministrative la:00pt la:00pt rdfs:label rdfs: school: ont: la: < school: rdfs:label "Cardiff High School"; ont:districtadministrative la:00pt. la:00pt rdfs:label "Cardiff".

40 Modelling Vocabularies so far no actual models, let alone semantics want to define types of thing : Class what you can say about them : Property encode definitions in more RDF and publish at the corresponding URIs link from data to data model reuse published vocabularies to enable integration freely combine different vocabularies or new ones

41 Modelling vocabularies Logical modelling modelling the domain, not a particular data structure what exists what is asserted? what can you deduce from that? not about constraints as such monotonic, open world controlled vocabulary thesaurus Ontology taxonomy ontology

42 Modelling vocabularies unfamiliar terminology but related to information architecture and conceptual modelling domain-driven design... and yes knowledge representation

43 Modelling RDFS RDF vocabulary description language classes, types and type hierarchy ont:school rdfs:label rdf:type School rdfs:class

44 Modelling RDFS RDF vocabulary description language classes, types and type hierarchy ont:school rdfs:label rdf:type School rdfs:class rdfs:subclassof rdf:type ont:welshestablishment

45 Modelling RDFS RDF vocabulary description language classes, types and type hierarchy rdfs:label School ont:welshestablishment rdf:type ont:school rdfs:class rdf:type rdfs:subclassof rdf:type school: ont:welshestablishment

46 Modelling RDFS RDF vocabulary description language classes, types and type hierarchy rdfs:label School ont:welshestablishment rdf:type ont:school rdfs:class rdf:type rdfs:subclassof rdf:type school: ont:welshestablishment ont:welshestablishment school: rdf:type ont:school rdfs:label School

47 Modelling RDFS RDF vocabulary description language properties, property hierarchy ont:staffat rdf:type rdf:property person:joebloggs ont:headof rdfs:subpropertyof school: ont:headof ont:staffat person:joebloggs school: ont:headof

48 Modelling RDFS RDF vocabulary description language class/property relations domain range Already have power to do some vocabulary mapping declare classes or properties from different vocabularies to be equivalent: A rdfs:subclassof B B rdfs:subclassof A

49 Modelling - OWL richer modelling and semantics axioms on properties transitive, symmetric, inverseof,... functional, inverse functional equivalent property axioms on classes intersection, union, disjoint, equivalent restrictions on classes some value from, all values from, cardinality, has value, one of, keys axioms on individuals same as, different from, all different imports

50 Modelling OWL supports much richer modelling consistency checking of model consistency checking of data some surprises if used to schema languages open world, no unique name assumption can extend to closed world checking inference classification inferred relationships

51 Modelling Spectrum of goals and styles Lightweight vocabularies simple modelling just enough agreement to get useful work done removing boundaries to enable information to be found and connected global consistency not possible a little semantics goes a long way Rich ontological models rich domain models need expressivity consistency is critical make complex inferences you can rely on, across data you trust knowledge is power

52 Modelling Ontology reuse invest in complete ontology for a domain rich but general model, may be modular inside strong ontological commitment e.g. medical ontologies reuse small, common, vocabularies FOAF, SIOC, Dublin Core, Org... pick and choose classes and properties you need fill in a few missing links for your domain generic reusable vocabularies Data cube vocabulary

53 Accessing all this data link following HTTP GET, follow links, aggregate relevant statements query SPARQL

54 SPARQL core idea is pattern matching graph patterns with variables any subgraph which matches yields row of bindings ont:districtadministrative rdfs:label?school [ ] Cardiff syntax based on Turtle syntax for RDF web API endpoints lots of power filters optionals named graphs sub-queries property chains aggregation federated query update construct

55 Accessing all this data link following HTTP GET, follow links, aggregate relevant statements query SPARQL linked data API RESTful API onto linked data resources simple query, usable without RDF stack, web dev friendly easy to layer visualizations and UIs on top third parties search engines and aggregators e.g. Sindice, sameas.org

56 Semantic web layer cake

57 Strengths and weaknesses image: flickr.com

58 Strengths data integration use of global identifiers (URIs) composable statements v. containers, schemaless linking, vocabulary mapping extensible, incremental, decentralized, resilient no global ontology/schema to develop or maintain freely add terms from other vocabularies open world assumption modelling and data entwined link data to models, data in context use same technology to share, manage extend models supports inference and classification rich access routes web linking, download, query, web APIs

59 Weaknesses complexity of the stack alphabet soup RDF, RDFS, OWL, SPARQL, RIF.. unfamiliar ontology, logical entailment lots of arcane details RDF/XML syntax use the parts you need tooling e.g. Linked Data API core ideas not that complex performance of schema-less stores optimization challenges technology improving steadily hybrid solutions limited validation and constraints cost of modelling,ontology development no inbuilt notions of time, uncertainty closed world checkers ontology reuse generic ontologies (data cube) tools model on top

60 Wrapping up image: erika flickr.com

61 Things we missed out RDF nuances blank nodes, containers and collections named graphs linked data nuances URI for thing v. web page, content negotiation, httprange-14 URI architecture OWL nuances OWL species, serializations, lots of details Other technologies in the stack SPARQL update, rules (RIF), GRDDL, Powder, Geo SPARQL, RDB mapping, triple/quad stores Embedding structured data in markup RDFa, micro formats, micro data, schema.org and all that

62 Hot topics Government linked data identifiers to seed linked data data publication transparency, improving services, economic growth structured data and search engines rich snippets, structured results, SEO search => question answering user interfaces visualization, exploration, exploiting linking data as a service

63 fin.