The Semantic Web: Web of (integrated) Data

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1 The Semantic Web: Web of (integrated) Data Frank van Harmelen Vrije Universiteit Amsterdam Take home message Semantic Web = Web of Data (no longer only web of text, web of pictures) Set of open, stable W3C standards Rapidly emerging tools & vendors Use cases: data integration web services knowledge management search (intranets) 1

2 Outline The vision What is required Machine representation XML, RDF, OWL Where are we now? Examples Things we would like to do on the Web 2

3 Intelligent things we can t do today Search engines concepts, not keywords semantic narrowing/widening of queries Shopbots semantic interchange, not screenscraping E-commerce Negotiation, catalogue mapping, personalisation Web Services Need semantic characterisations to find them, to combine them Navigation by semantic proximity, not hardwired links... Why can t Google do this harmelen 3

4 Other use-case are personalisation semantic linking data integration web services... Sounds good, so.. how is this tackled? 4

5 Outline The vision What is required Machine representation XML, RDF, OWL Where are we now? Examples machine accessible meaning (What it s like to be a machine) name symptoms drug disease administration Meta-data! 5

6 What is meta-data? name symptoms drug disease administration it's just data it's data describing other data its' meant for machine consumption meta-data + ontologies <treatment> reduces <name> <symptoms> IS-A <drug> <disease> <drug administration> 6

7 What s inside an ontology? terms + specialisation hierarchy classes + class-hierarchy instances slots/values inheritance (multiple? defaults?) restrictions on slots (type, cardinality) properties of slots (symm., trans., ) relations between classes (disjoint, covers) reasoning tasks: classification, subsumption Increasing semantic weight In short (for the duration of this tutorial) Ontologies are not definitive descriptions of what exists in the world (= philosphy) Ontologies are shared models of the world constructed to facilitate communication Yes, ontologies exist (because we build them) 7

8 Real life examples handcrafted (often by communities) music: CDnow (2410/5), MusicMoz (1073/7) biomedical: SNOMED (200k), GO (15k), Emtree(45k+190k) ranging from lightweight (Yahoo, UNSPC) to heavyweight (Cyc) ranging from small (METAR) to large (UNSPC) allright, but how to represent all this in a computer? 8

9 Outline The vision What is required machine representation XML, RDF, OWL Where are we now? Examples Semantic Web architecture 9

10 What was XML again? <country name= Netherlands > <capital name= Amsterdam > <areacode>020</areacode> </capital> </country> country name capital Netherlands name areacode Amsterdam 020 So why not just use XML? No agreement on: structure is country a: object? class? attribute? relation? something else? what does nesting mean? vocabulary is country the same as nation? <country name= Netherlands > <capital name= Amsterdam > <areacode>020</areacode> </capital> </country> <nation> <name>netherlands</name> <capital>amsterdam</capital> <capital_areacode> 020 </capital_areacode> </nation> Are the above XML documents the same? Do they convey the same information? Is the answer machine-derivable? 10

11 So: XML machine accessible meaning < name ναμε > <εδυχατιον> < education> <ωορκ> < work> < CV Χς > <πριϖατε> < private> The semantic pyramid again 11

12 W3C Stack XML: Surface syntax, no semantics XML Schema: Describes structure of XML documents RDF: Datamodel for relations between things RDF Schema: RDF Vocabulary Definition Language OWL: A more expressive Vocabulary Definition Language RDF & RDF Schema RDF = relations between things all objects are URL s (both things and relations) RDF Schema = hierarchical organisation of an RDF vocabulary all things are URL s (classes of things, subclass relations) For more details: see slides later today 12

13 The semantic pyramid again OWL: things RDF Schema can t do equality enumeration number restrictions Single-valued/multi-valued Optional/required values inverse, symmetric, transitive boolean algebra Union, complement Again: For more details: see slides later today 13

14 Sounds good in theory. How far are you with this in practice? Where are we now: tools Languages are stable (W3C) Tooling is rapidly emerging HP, IBM, Oracle, Adobe, Parsers, Editors, visualisers, large scale storage and querying Portal generation Aduna Intellidimension 14

15 Three example use-cases Closed-world data integration: DOPE Elsevier Open-world data integration: streaming Philips Semantic Web services Conclusions Closed-world data integration: DOPE Elsevier This section joint with Aduna and Anita de 15

16 Background Vertical Information Provision Buy a topic instead of a Journal! Web provides new opportunities Business driver: drug development Rich, information-hungry market Good thesaurus (EMTREE) The Data Document repositories: ScienceDirect: approx fulltext articles MEDLINE: approx abstracts Extracted Metadata The Collexis Metadata Server: conceptextraction ("semantic fingerprinting") Thesauri and Ontologies EMTREE: preferred terms synonyms 16

17 Architecture: Query interface RDF Schema EMTREE RDF RDF Datasource 1. Datasource n 17

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21 Web-based data integration scenario: heterogeneous open This section material from Zharko VU & Philips 21

22 Motivating scenario LaunchCast itunes Rhapsody Semantic Web Buy.com User devices consumer.philips.com Napster emusic MusicNow Wal*Mart Musicmatch Providers MusicNet Example Hits from the 60s Mediator Music Ontology Evergreens Evergreens and Golden hits are related: Golden hits is mostly subclass of Evergreens 22

23 Domain characteristics Many music providers Wide variety of music offered Constantly increasing in size and evolving Cumbersome to browse and retrieve music There is no agreement Different terms are used The same terms contain different sets of artists data-sources CDNow (Amazon.com) Size: 2410 classes Depth: 5 levels ArtistGigs Size: 382 classes Depth: 4 levels Artist Direct Network Size: 465 classes Depth: 2 levels CD baby Size: 222 classes Depth: 2 levels All Music Guide Size: 403 classes Depth: 3 levels MusicMoz Size: 1073 classes Depth: 7 levels Yahoo Size: 96 classes Depth: 2 levels 23

24 Why approximate matching Genre is not precisely defined Pop and Rock have no common definition on the big portals AllMusic.com, Amazon.com and MP3.com Exact reasoning will not be useful A X 99 % 1 % Results A - AllMusicGuide B - ArtistDirectNetwork B subclass of A A subclass of B equivalences

25 Semantic Web Services This section material from Marta VU What are web-services a software system designed to support interoperable machine-to-machine interaction over a network. has an interface described in a machine processable format (specifically WSDL). Other systems interact with a web service in a manner specified by its descriptions using SOAP messages 25

26 Web Service Tasks Web Service Discovery & Selection Find an airline that can fly me to Marina del Rey Web Service Invocation Book flight tickets from NWA to arrive 12 th Oct. Web Service Composition & Interoperation Arrange taxis, flights and hotel for travel from Southampton to Portland, OR, via Marina del Rey, CA. Web Service Execution Monitoring Has the taxi to Gatwick Airport been reserved yet? Limitations of WS Technology Manual Discovery Manual Invocation Manual (ad hoc) Mediation Manual (ad hoc) Composition 26

27 Use of Semantics: Example <do:hotelbooking rdf:id= WS1"> <owls:hasinput rdf:resource= do:hotel /> </do:hotelbooking > <do:hostelbooking rdf:id= WS2"> <owls:hasinput rdf:res= do:hostel /> </do:hostelbooking > R:(BookingService,Hotel)=> * exact match with WS1 * plug-in match for WS2 Degrees of WS Matching Match Advertisement with Request: Exact: Plug-In: Subsume: Intersection: Disjoint: Adv equals Req Adv is more general than Req Adv is less general than Req Adv and Req overlap (a bit) Adv and Req don t overlap Matchmaking algorithms (primarily) employ subsumption reasoning over the knowledge provided by the domain ontologies. 27

28 Take home message again: Take home message Semantic Web = Web of Data (no longer web of text, web of pictures) Set of open, stable W3C standards Rapidly emerging tools & vendors Use cases: data integration web services knowledge management search (intranets) 28

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