Semantic Web Fundamentals

Transcription

1 Semantic Web Fundamentals Web Technologies ( ) 3SSt VU WS 2017/18 Vedran Sabol with acknowledgements to P. Höfler, V. Pammer, W. Kienreich ISDS, TU Graz December 11 th 2017

2 Overview What is Semantic Web? Technology stack Linked Data (Cloud) Example application Vedran Sabol 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 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 Semantic Web Stack Unicode: encoding standard for text, includes many character set (for different languages) Used for encoding resources in the Semantic Web 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)

6 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 Vedran Sabol 6

7 7 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="

8 8 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)

9 9 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

10 10 RDF - Examples In English Moby-Dick was written by Herman Melville. In RDF < < < In English Tim Berners-Lee likes Moby-Dick. In RDF < < <

11 11 RDF - Graph Tim Berners-Lee is a likes published on Person Moby Dick is a author is a Herman Melville Book

12 12 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>

13 13 RDF Serialization Formats dbo: < < dbo:author < JSON-LD { } "@id": " " { "@id": " }

14 14 RDF Schema (RDFS) and Web Ontology Language (OWL) Data-modelling vocabulary for RDF data Contains elements for defining ontologies Classes, properties, data types, sub-classing etc. OWL extends RDFS with more advanced constructs stating additional constraints cardinality, value restrictions, characteristics of properties (e.g. transitivity). brings reasoning power to the semantic web RDF defines how to write statements, OWL defines what is valid to write

15 15 Ontology Definition: naming and definition of the types, properties, and relationships of entities in a particular domain Consist of Instances (basic objects) Classes Attributes (describe classes and instances) Relations (relate classes and instances) Function terms Complex structures formed from certain relations that can be used in place of an individual term in a statement Restrictions (to something being accepted as input) Rules (if-then statements describing logical inference) Axioms

16 SPARQL Protocol and RDF Query Language (SPARQL) Triple Stores Databases for storing RDF statements AllegroGraph, OpenLink Virtuoso, Jena Retrieval using semantic queries 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. (< ) Vedran Sabol 16

17 17 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

18 18 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.

19 19 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 FOAF: dataset describing persons, their properties and relations GeoNames: descriptions of geographical features worldwide

20 20 LOD

21 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

22 22 Serving Linked Data Intended Use? HTML files with embedded data (ideal for humans) RDF files / dump (ideal for static data / downloads) SPARQL Endpoints (ideal for exploring dynamic data sets) Data Storage Wrapped Relational DBs Triple Stores (preferred)

23 23 UI Layer 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

24 24 Query Wizard Google-like search in Linked Data Keyword search + attribute selection (columns) Search result: a tabular result set

25 25 Visualization Wizard 10 different visualisations time, geo-spatial, categorical, statistics Automatic visualisation of tabular data depending on data characteristics and visualization capabilities Filtering and aggregation Interactive analysis over multiple dimensions Multiple coordinated views technology

26 Visualization Wizard 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 Vedran Sabol

27 Visualization Wizard Aggregate CO2 emissions and life expectancy Average values for countries Correlations: emissions and life expectancy Outliers: Russia, Saudi Arabia Vedran Sabol

28 28 Visualization Wizard 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 Visual Channels: data type, cardinality, persistence Ontologies define the relation between data and visualizations Compute all valid mappings Only these can be instantiated (= visualisation created for a data set)

29 29 Visualization Wizard Problem: many valid mappings - which is optimal for the user/task/data? VizRec: personalised visualisation recommender Recommend the best mapping Based on previous usage patterns