Semantic Web Fundamentals

Transcription

1 Semantic Web Fundamentals Web Technologies ( ) 3SSt VU WS 2018/19 with acknowledgements to P. Höfler, V. Pammer, W. Kienreich ISDS, TU Graz January 7 th 2019

2 Overview What is Semantic Web? Technology stack Linked Data (Cloud) Example applications 2

3 3 Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Tim Berners-Lee, Scientific American, 2001 Also described as Web 3.0 (where Web 2.0 is the social Web) Semantic Web is a web of data Addition to the classic Web of documents Goals Give Web information an exact meaning (semantics) Provide common formats for data integration/combination enabled by being about the same thing (semantics) Empower computers to understand, process and integrate Web information

4 4 Semantic Web Stack

5 5 Resource Identification Unicode: encoding standard for text, includes many character sets (covering different languages) Used for encoding resources in the Semantic Web defines UTF-8 (preferred), UTF-16, UTF-32 character encodings Current version: ,439 characters from 146 modern and historic scripts

6 6 Resource Identification IRI/URI (Internationalized Resource Identifier/Uniform Resource Identifier): unique identification of resources URL: Uniform Resource Locator Reference to a resource in the Web Also defines the means for accessing the resource (e.g. URN: Uniform Resource Name Identifies a resource within a specific namespace (e.g. ISBN International Standard Book Number)

7 Resource Identification URI: Uniform Resource Identifier Standard for identifying resources in the Semantic Web Generalisation of URLs Can be a virtual pointer (e.g. not associated to content/document) Consist of Scheme name: e.g. http: Authority: e.g. //myhost.com:8080 Path: e.g. /dir/subdir/file.db Query: e.g.?date= &place=graz Fragment: e.g. #foto IRI: Internationalized Resource Identifier Extends URIs from ASCII to Universal Character Set Best practice: HTTP URIs 7

8 8 XML XML: meta-markup language Used to define a syntax for creating documents containing structured data Nested opening and closing tags define a hierarchy of elements Attributes define element properties Content belonging to an element stored between tags XML Namespaces: provide a possibility to use uniquely named elements and attributes from different sources (vocabularies) Declared using reserved attribute xmlns:prefix= namespaceuri xmlns:xhtml="

9 9 Resource Description Framework (RDF) RDF is a framework for creating statements about resources in the form of triples Triples are subject-predicate-object expressions Graph representation of resource information RDF Schema (RDFS) provides basic vocabulary for RDF Allows to define application-specific classes and properties Resources defined as instances of classes and subclasses (like in OOP)

10 10 Resource Description Framework (RDF) Language for representing information about Web resources Consists of triples: <subject> <predicate> <object> Different serialisation formats (initially XML) Designed to be understood by computers Not indented for consumption by humans W3C recommendation since 2004

11 11 RDF - Examples In English Moby-Dick was written by Herman Melville. In RDF < < < In English Tim Berners-Lee likes Moby-Dick. In RDF < < <

12 12 RDF - Graph Tim Berners-Lee is a likes published on Person Moby Dick is a author is a Herman Melville Book

13 13 RDF Serialization Formats RDF/XML <?xml version="1.0" encoding="utf-8"?>! <rdf:rdf xmlns:rdf=" xmlns:dbo=" <rdf:description rdf:about=" <dbo:author rdf:resource=" /> </rdf:description> </rdf:rdf>

14 14 RDF Serialization Formats dbo: < < dbo:author < JSON-LD { } "@id": " " { "@id": " }

15 15 RDF Schema (RDFS) and Web Ontology Language (OWL) Data-modelling vocabulary with pre-defined semantics for RDF data an RDFS document is a valid RDF document Contains elements for defining ontologies Classes, properties, data types, sub-classing etc. Classes are sets of instances (rdfs:class), e.g. rdfs:resource (class of all URIs, all other classes are subclasses) rdf:property (class of all properties) rdfs:literal (class of all literals, e.g. strings or integers) rdfs:datatype (class of all datatypes) rdfs:container (super-class of rdf:alt, rdf:bag, rdf:seq)

16 16 RDF Schema (RDFS) and Web Ontology Language (OWL) OWL extends RDFS with more advanced constructs Extended vocabulary Stating additional constraints cardinality, value restrictions, characteristics of properties (e.g. transitivity). Brings reasoning power to the semantic web Entity names are IRIs RDF defines how to write statements, OWL defines what is valid to write

17 17 Ontology Definition: naming and definition of the types, properties, and relationships of entities in a particular domain a formalization of a conceptualization Encoded using ontology languages (RDF, RDFS, OWL etc.) An ontology has an IRI And may have a version number import other ontologies Be described using metadata (e.g. with RDFS)

18 18 Ontology Consist of Instances (basic objects) Classes (define different types of instances) Attributes (describe classes and instances) Relations (put classes and instances into relation) Restrictions (to something being accepted as input) Rules (if-then statements describing logical inference) Axioms (assertions/statements, including rules) Declarations: non-logical axioms to ensure IRIs are used for proper entity types (e.g. Declaration(NamedIndividual(:Frenkie)), ClassAssertion(:Manager :Frankie)) Assertional Axioms: assert facts or annotations about entities

19 19 Triple Stores Databases for storing RDF statements Optimized for storage of triples and the resulting graph structures Related to graph DBs (which are more general) May be implemented on top of existing DB engines Retrieval using semantic queries Query execution efficiency is the hard part Examples: AllegroGraph, OpenLink Virtuoso, Jena

20 20 SPARQL Protocol and RDF Query Language (SPARQL) SPARQL is the RDF query language for a triple store Interface is called SPARQL end point 4 query types ASK: returns a true or false value SELECT: matching resources returned in a table format CONSTRUCT: returns results as valid RDF DESCRIBE: returns RDF statements describing the matching resources it is up to the endpoint to decide which descriptions are included

21 SPARQL Protocol and RDF Query Language (SPARQL) SPARQL is the RDF query language for a triple store Interface is called SPARQL end point Query example Result SELECT?book?releaseDate WHERE { < con:likes?book.?book dbpedia:releasedate?releasedate. } (< ) 21

22 22 Linked Data The Semantic Web isn't just about putting data on the web. It is about making links, so that a person or machine can explore the web of data. With linked data, when you have some of it, you can find other, related, data. Tim Berners-Lee

23 23 Linked Data Publishing and interlinking structured data on the Web Using Semantic Web standards Discoverable using SPARQL 4 principles (by Tim Berners-Lee) Use URIs as names for things Use HTTP URIs so that people can look up those names When someone looks up an URI, provide useful information, using standards (RDF, SPARQL) Include links to other URIs, so that they can discover more things.

24 24 Linked Open Data (LOD) W3C SWEO (Semantic Web Education and Outreach) Community Project The goal of the W3C SWEO Linking Open Data community project is to extend the Web with a data commons by publishing various open data sets as RDF on the Web and by setting RDF links between data items from different data sources Examples DBpedia: data from Wikipedia, structured using semantic Web technologies FOAF: dataset describing persons, their properties and relations GeoNames: descriptions of geographical features worldwide

25 LOD Cloud Explore the LOD cloud (SVG) WT VU Semantic Web Fundamentals January 7th

26 Ontology Alignment Problem: LOD cloud is far from perfect different domains/view-points diversity of vocabularies and conceptualizations no links between them Ontology alignment: establish links between (equal) concepts from different ontologies to enable interoperability Multitude of techniques: statistical, linguistic, structural, rule-based etc. External Knowledge (WordNet ) Ontology A Ontology Alignment Algorithms Statistical Methods match match Linguistic Methods Knowledge Base Ontology B Aligned Ontologies

27 27 UI Layer - Example UI Layer enables humans to use semantic Web applications Example application: Query Wizard + Visualization Wizard for LOD Query Wizard: Search for datasets like you search for documents Visualization Wizard: automatically generate interactive visualizations Rely on semantic information

28 28 Query Wizard Google-like search in Linked Data Keyword search + attribute selection (columns) Search result: a tabular result set

29 29 Visualization Wizard 10+ different visualisations time, geo-spatial, categorical, statistics Automatic visualisation of tabular data sets depending on data characteristics and visualization capabilities Filtering and aggregation Interactive analysis with Multiple Coordinated Views Utilize Semantic Web technologies to achieve the goals

30 30 Visualization Wizard - Ontologies RDF Data Cube Vocabulary (W3C standard) Represents statistical data as collections of observations Dimensions: identify observations Measures: are related to concrete values Visual Analytics Vocabulary Describes visualizations semantically as an OWL ontology Chart name Definition of visual channels of the visualization: available axes, colors, different icons, item size And their data presentation capabilities: data type, cardinality, persistence

31 31 Visualization Wizard - Mapping The two ontologies define the relation between data and visualizations Compute all valid mappings of a data set onto visualizations These visualizations (and only these) can be created automatically with a single click

32 32 Visualization Wizard UI Meaningful charts can be created automatically Bar Chart (selected) Pie Chart Parallel Coordinates Geo View Other charts are disabled Cannot be created for that data set

33 Visualization Wizard Selection/Filtering Multidimensional data Data elements - lines Colours - property (violet - EU countries) Exploration Filter along multiple axes (e.g. years, CO2 Tons/P) Read values on other axes Spot patterns, dependencies

34 Visualization Wizard Coordinated Multiple Views Visualising data sets in multiple visualisations Each visualization specialized for a different data aspect numerical values, categories, time and geo information Interactive analysis using coordinated brushing Selection in one visualization (scatterplot) reflected in all others (geo)

35 Visualization Wizard - Aggregation Aggregate CO2 emissions and life expectancy Average for countries over the years Correlation: CO2 emissions and life expectancy Outliers: Russia, Saudi Arabia

36 Visualization Wizard Aggregation aggregate value (sum) for countries over the years Data transformations - such as aggregation - create new data sets!

37 Visualization Wizard Coordinated Multiple Views Problem: how to link different data sets to enable interactive analysis in multiple visualizations? Linking enabled by semantic information (through being about the same thing ) for data sets created by data transformations and for any data set loaded from the web Examples of insights obtained through semantic coordinated brushing Countries with lowest funding lie in Eastern Europe Their funding is increasing over the years but so is for the other counters too