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The Semantic Web

The Semantic Web. Is it “The Web shortcut to A.I.”? Or more? Or less?. Evolution of World Wide Web. Rule Interchange. Personal Agents. Semantic Web era. Linked Data. WWW Database. Web 3.0. SPARQL. 2010-2020. ATOM. RDF/OWL. Cloud computing & SaaS. AJAX/JSON. Social networks. SOAP.

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The Semantic Web

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  1. The Semantic Web Is it “The Web shortcut to A.I.”? Or more? Or less?

  2. Evolution of World Wide Web Rule Interchange Personal Agents Semantic Web era Linked Data WWW Database Web 3.0 SPARQL 2010-2020 ATOM RDF/OWL Cloud computing & SaaS AJAX/JSON Social networks SOAP Blogs/Wikis WWW era Web 2.0 2000-2010 XML Portals OO/Java Intranet Richness of data connections HTTP/HTML Groupware Web 1.0 1990-2000 PC era Gopher SQL Databases SGML File servers Desktop Computing 1980-1990 FTP File systems Email Richness of social connections

  3. Motivation for Semantic Web

  4. Linked Data: The World Wide Web database

  5. The Semantic Web • A Vision Of Possibilities • “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” • -- Tim Berners-Lee, James Hendler and OraLassila, The Semantic Web, Scientific American, May 2001

  6. Semantic Web • In the Semantic Web we will need: • Machines talking to machines – semantics need to be unambiguously declared • Joined-up data – enabling complex tasks based on information from various sources • Wide scope – from, say, home to government to commerce • Trust – both in data and who is saying it • This is not going to be easily achieved

  7. Semantic Web vs Semantic Technologies • Semantic Technologies • Natural-language processing • Data mining/Machine learning • Artificial intelligence/Expert systems • Classification • Semantic search Semantic Web Semantic Web Formats (RDF, OWL, etc.) Query language (SPARQL) Rules language (RIF) Web pages marking language (RDFa) Triple/Quad stores

  8. Semantic web applications • Examples: • Personal information management (Chandler) • Social networking (FOAF) • Information syndication (RSS,PRISM) • Library/museum data (Dublin Core, Harmony) • Network security and configuration (SWAD-E)

  9. Interesting quotes • “Knowledge representation (…) is clearly a good idea, and some very nice demonstrations exist, but it has not yet changed the world.” Meaning: Of course the Semantic Web will do that. Will it?

  10. Today´s web • It is designed for human consumption • Information retrieval is mainly supported by keyword-based search engines • Some problems with information retrieval: • High recall, low precision • Low or no recall • Results are highly sensitive to vocabulary

  11. Machines still have a very minimal understanding of text and images. tell register But what about machines?

  12. machine-friendly data Li Ding is a person LiDingisasaon • Natural Language • XML – represent structures • Semantic Web - represent more semantics • represent structures • enable common vocabulary • associate symbols with logic interpretation for inference as seen by a machine as seen by a person <on>LiDing</on> <person>Li Ding</person> as seen by a person as seen by a machine

  13. The Semantic Web XMLCustomized tags, like: <dog>Nena</dog> + RDFRelations, in triples, like: (Nena) (is_dog_of) (Ahmed/Said) + OntologiesHierarchies of concepts, like mammal -> canine -> Cotton de Tulear -> Nena + Inference rulesLike: If (person) (owns) (dog), then (person) (cares_for) (dog) = Semantic Web!

  14. Semantic Web Layers Semantic Aspect Web Aspect HTTP "The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.“ – Berners-Lee, Hendler & Lassila, Scientific American, 2001 Image source: http://en.wikipedia.org/wiki/Image:W3c_semantic_web_stack.jpg

  15. XML (eXtensible Markup Language) • Standard for information and exchange • XML v. HTML • HTML: restricted set of tags, e.g. <TABLE>, <H1>, <B>, etc. • XML: you can create your own tags • Selena Sol (2000) highlights the four major benefits of using XML language: • XML separates data from presentation which means making changes to the display of data does not affect the XML data; • Searching for data in XML documents becomes easier as search engines can parse the description-bearing tags of the XML documents; • XML tag is human readable, even a person with no knowledge of XML language can still read an XML document; • Complex structures and relations of data can be encoded using XML.

  16. XML: An Example • XML is a semi structured language • <BookId= “B105”> • <Title> Topics in Optimal Transportation </Title> • <Author> • <Name> Cedric Villani </Name> • </Author> • <Publisher> • <Name>American Mathematical Society </Name> • <Place> NewYork</Place> • </Publisher> • </Book>

  17. RDF Motivation • The Resource Description Framework (RDF) is a language for representing resources in the World Wide Web. • RDF is intended for situations in which this information needs to be processed by applications, rather than being only displayed to people. • RDF is based on the idea of identifying things using Web identifiers (URIs).

  18. The Semantic Web is simple Don't say "colour" say <http://example.com/2002/std6#col> • Each URI denotes a concept • URIs are connected by triples • Machines read data as directed RDF graph RDF (Resource Description Framework) Relational database Source: Tim Berners-Lee, Putting the Web back into Semantic Web, ISWC2005 Keynote

  19. RDF Basic Concepts • the thing the statement describes (the web page`s URL) • a specific property of the thing (e.g. creator) • the concrete message the statement wants to give, in other words the value of the property (Ahmed Hassan) Example „Imagine trying to state that someone named Ahmed Hassan created a particular Web page.“ http://www.example.org/index.html has a creator whose value is Ahmed Hassan

  20. Subject Object Predicate RDF Basic Concepts RDF terminology • the part that identifies the thing the statemant is about is called subject • the part that identifies the property is called predicate • the part that identifies the value of the property is called object

  21. Subject Object Predicate RDF Basic Concepts RDF terminology • the part that identifies the thing the statemant is about is called subject • the part that identifies the property is called predicate • the part that identifies the value of the property is called object http://www.example.org/index.html has a creator whose value is Ahmed Hassan • the subject is the URL „http://www.example.org/index.html“ • the predicate is the word „creator“ • the object is the name „Ahmed Hassan“

  22. http://www.example.org/index.html http://purl.org/dc/elements/1.1/creator http://www.example.org/staffid/5232 RDF Model As mentioned: • RDF makes statements about resources • Each statement consists of a subject, a predicate and an object http://www.example.org/index.html has a creator whose value is Ahmed Hassan subject predicate object

  23. RDF Basic Concepts To make these statements machine-proccessable two things are needed: • a system of machine-processable identifiers (for subjects, predicates and objects) without any possibilty of confusion between similar looking identifiers • a machine-processable language for representing these statements and exchanging them between machines Uniform Resource Identifiers (URI) allow to identify and uniquely name things - even if they have no network-accessible location. RDF defines a XML markup language, named RDF/XML, which allows to represent RDF statements.

  24. http://www.example.org/index.html http://www.example.org/terms/creation-date August 16, 1999 RDF Syntax <?xml version="1.0"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:exterms="http://www.example.org/terms/"> <rdf:Description rdf:about="http://www.example.org/index.html"> <exterms:creation-date>August 16, 1999</exterms:creation-date> </rdf:Description> </rdf:RDF>

  25. RDF Developments • We have seen that: • RDF looks complex • There are still some uncertain areas • Let’s now look at: • A simple RDF application • Browser support • Project work • Related work which may: • require the Semantic Web • be used to build the Semantic Web

  26. A Lightweight RDF Application • RSS (RDF Site Summary): • Example of a lightweight RDF application • A format for news syndication • Worth looking at for: • News syndication • Gaining experience of an RDF application • See: <http://blogspace.com/rss/> <http://www.oreillynet.com/rss/> <http://www.webreference.com/authoring/languages/xml/rss/intro/>

  27. Browser Support • The Mozilla open source browser is using RDF to integrate and aggregate Internet resources. http://www.mozilla.org/rdf/doc/

  28. RDF Conclusion • Expressivity of RDF is limited • Local scope of properties • Disjointness of classes • Boolean combination of classes • Cardiniality restrictions • Special characteristics of properties • Need for standardized ontology languagethat builds upon existing concepts of RDF • => OWL Web Ontology Language

  29. What Is An Ontology • An ontology is an explicit description of a domain: • concepts • properties and attributes of concepts • constraints on properties and attributes • Individuals (often, but not always) • An ontology defines • a common vocabulary • a shared understanding

  30. Ontology Examples • Taxonomies on the Web • Yahoo! categories • Catalogs for on-line shopping • Amazon.com product catalog • Domain-specific standard terminology • Unified Medical Language System (UMLS) • UNSPSC - terminology for products and services

  31. determinescope determinescope considerreuse considerreuse considerreuse considerreuse enumerate terms enumerate terms enumerate terms defineclasses defineclasses defineclasses defineclasses defineclasses defineproperties defineproperties defineproperties defineproperties defineconstraints defineconstraints defineconstraints createinstances createinstances createinstances createinstances Ontology-Development Process In reality - an iterative process:

  32. Determine Domain and Scope determinescope considerreuse enumerate terms defineclasses defineproperties defineconstraints createinstances • What is the domain that the ontology will cover? • For what we are going to use the ontology? • For what types of questions the information in the ontology should provide answers (competency questions)? Answers to these questions may change during the lifecycle

  33. Consider Reuse considerreuse determinescope enumerate terms defineclasses defineproperties defineconstraints createinstances • Why reuse other ontologies? • to save the effort • to interact with the tools that use other ontologies • to use ontologies that have been validated through use in applications

  34. What to Reuse? • Ontology libraries • DAML ontology library (www.daml.org/ontologies) • Ontolingua ontology library (www.ksl.stanford.edu/software/ontolingua/) • Protégé ontology library (protege.stanford.edu/plugins.html) • Upper ontologies • IEEE Standard Upper Ontology (suo.ieee.org) • Cyc (www.cyc.com)

  35. What to Reuse? (II) • General ontologies • DMOZ (www.dmoz.org) • WordNet (www.cogsci.princeton.edu/~wn/) • Domain-specific ontologies • UMLS Semantic Net • GO (Gene Ontology) (www.geneontology.org)

  36. Enumerate Important Terms enumerate terms considerreuse determinescope defineclasses defineproperties defineconstraints createinstances • What are the terms we need to talk about? • What are the properties of these terms? • What do we want to say about the terms?

  37. Define Classes and the Class Hierarchy defineclasses considerreuse enumerate terms determinescope defineproperties defineconstraints createinstances • A class is a concept in the domain • a class of courses • a class of students • a class of graduate students • A class is a collection of elements with similar properties • Instances of classes • a student of AI course who will come today

  38. Class Inheritance • Classes usually constitute a taxonomic hierarchy (a subclass-superclass hierarchy) • A class hierarchy is usually an IS-A hierarchy: an instance of a subclass is an instance of a superclass • If you think of a class as a set of elements, a subclass is a subset

  39. Apple is a subclass of Fruit Every apple is a fruit Student is a subclass of Person Every Student is a Person G student is a subclass of Student Every G Student is a Student Class Inheritance - Example

  40. Modes of Development • top-down – define the most general concepts first and then specialize them • bottom-up – define the most specific concepts and then organize them in more general classes • combination – define the more salient concepts first and then generalize and specialize them

  41. Define Properties of Classes – Slots defineproperties determinescope considerreuse enumerate terms defineclasses defineconstraints createinstances • Slots in a class definition describe attributes of instances of the class and relations to other instances Each Student will have Name, GPA, Address, etc.

  42. Slots for the Class (in Protégé-2000)

  43. Property Constraints defineconstraints determinescope considerreuse enumerate terms createinstances defineclasses defineproperties • Property constraints (facets) describe or limit the set of possible values for a slot The name of a Student is a string The Birth date is an instance of Date A student has exactly one Address

  44. Common Facets • Slot cardinality – the number of values a slot has • Slot value type – the type of values a slot has • Minimum and maximum value – a range of values for a numeric slot • Default value – the value a slot has unless explicitly specified otherwise

  45. Common Facets: Value Type • String: a string of characters (“Château Lafite”) • Number: an integer or a float (15, 4.5) • Boolean: a true/false flag • Enumerated type: a list of allowed values (high, medium, low) • Complex type: an instance of another class • Specify the class to which the instances belong The Wine class is the value type for the slot “produces” at the Winery class

  46. Create Instances createinstances determinescope considerreuse enumerate terms defineclasses defineconstraints defineproperties • Create an instance of a class • The class becomes a direct type of the instance • Any superclass of the direct type is a type of the instance • Assign slot values for the instance frame • Slot values should conform to the facet constraints • Knowledge-acquisition tools often check that

  47. Creating an Instance: Example

  48. Ontologies and the SW Languages • Most Semantic Web languages are designed explicitly for representing ontologies • RDF Schema • DAML+OIL • SHOE • XOL • XML Schema

  49. Ontology Languages: RDFS and OWL • RDFS • Set theory – rdfs:Class • Relation – rdf:Property, rdfs:domain, rdfs:range • Hierarchy – rdfs:subClassOf, rdfs:subPropertyOf • Built-in Datatype – xsd:string, xsd:dataTime • OWL • Description Logic • Class, Thing, Nothing • DatatypeProperty, ObjectProperty, AnnotationProperty,… • Class axioms • oneOf, disjointWith, unionOf, complementOf, intersectionOf … • Restriction, onProperty, cardinality, hasValue… • Property axioms • inverseOf , TransitiveProperty , SymmetricProperty • FunctionalProperty, InverseFunctionalProperty • Equality– equivalentClass , sameAs , differentFrom… • Ontology annotation – Ontology, imports, versionInfo

  50. More languages and more ontologies • Languages (require special inference engine) • [Trust/Uncertainty] BayesOWL • [Proof] PML (Proof Markup Language) • [Query/Data Access] SPARQL Query Language for RDF • [Rule] SWRL( Semantic Web Rule Language) • [Policy] REI: A Policy Specification Language • [Service] OWL-S by DAML (1.2 preview available) • [Service] SAWSDL (Semantic Annotations for WSDL) • [Thesauri] SKOS (Simple Knowledge Organization System) • Ontologies (only need RDFS and/or OWL inference) • Upper ontologies - OpenCyc, WordNet, OntoSem, SUO • Specialized common ontologies - FOAF, Dublin Core, RSS • Domain ontologies – bibtex, biology, and many… Li Ding, Pranam Kolari, Zhongli Ding, and Sasikanth Avancha, “Using Ontologies in the Semantic Web: A Survey”, in Ontologies in the Context of Information Systems (book chapter), 2005. http://ebiquity.umbc.edu/paper/html/id/257/

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