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The Semantic Web: Enabling Meaningful Interaction Between Machines and Humans

The Semantic Web is an extension of the current web, allowing machines and people to collaborate more effectively through well-defined meanings of information. It aims to automate, integrate, and reuse data across applications via intelligent agents and services. With a focus on machine-readable data and flexible frameworks like RDF, the Semantic Web enhances searching, annotation, and web service automation. By using ontologies, it helps organize knowledge and establish relationships between data, making the web more intuitive and interconnected.

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The Semantic Web: Enabling Meaningful Interaction Between Machines and Humans

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  1. The W3C’s Semantic Web Kyle Mosack

  2. The Semantic Web • "The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation." It is a source to retrieve information from the web (using the web spiders from RDF files) and access the data through Semantic Web Agents or Semantic Web Services.” Source: "The Semantic Web" by Tim Berners-Lee, James Hendler, and Ora Lassila, Scientific American, 2001

  3. What It Could Be • Machine Readable Data View • Having data on web be defined an linked in a way that can be read by machine for automation, integration, and re-use across different applications • Intelligent Agent View • Agents retrieve and manipulate pertinent information • Distributed Data View • Provide sufficient flexibility to be able to represent all databases and logic rules to link them together • Automated Infrastructure View • Current web lacks an easy automation framework

  4. What it Could Be • Servant of Humanity View • Enable Web applications to automatically collect , integrate, and process information and interoperate with other applications • Better Annotation View • Annotations expressed in a machine processable form and linked together • Improved Searching View • Access Web content by concepts instead of keywords • Web Services View • Expand services from existing web by automating services with Web agents

  5. Semantic Web Layer Cake

  6. Semantic Web Layer Cake

  7. Readable Description Framework • Designed for specific data about specific subjects • Can represent Data and Metadata • Moves proprietary data to a form computers can analyze • Recommendation from W3C for Semantic Web

  8. RDF • To be successful RDF must be able to • Describe most kinds of data that will be available • Describe structural design of data sets • Describe relationships between bits of data

  9. RDF • Creates a data model with triplets • Subject, predicate (property), object (property value) • These are statements about resources • Identified by URI • Easily convertible • Not limited by predefined database values • Flexible

  10. RDF Graph with Anonymous Nodes

  11. RDF Applications • Mozilla • Uses XUL (extensible user interface language) • Uses RDF as a source for listings and other control information that defines which XUL files to use for specific XUL interfaces • RSS (Really Simple Syndication) • Helps spread summaries of personal blogs cheaply and easily • Uses RDF’s XML exchange format • One of the few RDF applications that is distributed over the Web rather than being used locally, unlike Mozilla application • Can be seen as an early, maybe primitive, Semantic Web Application

  12. RDF Applications • RDF for annotations: Annotea • Experimental scheme by W3C for annotating Web pages • Bibliographic meta data: Dublin Core • Provides practical standard terms applicable to nearly any published work • Webscripter: Fusing Information • DAML (DARPA Agent Markup Language) • Ability to make up web pages so that information about the page can be extracted in a uniform way and combined with other web pages

  13. RDF and the Semantic Web • Abilities beyond conventional database • Combine data with different data sets that don’t follow the same data models • Add data that doesn’t fit the table structures • Exchange data with any other application that can handle RDF • Use an RDF processor that can do logical reasoning to discover unstated relationships • Use someone else’s ontology to learn more about own data • Add statements about publications and references that have been defined somewhere else on the Web • Do all these things using well defined standards, so wide range of applications can process the data

  14. RDF – Potential Problems • Many stem from nature of the Web • Incomplete information • Contradictory or unreliable information • Full first order logic requires an ability to generate general statements about the whole table • No way to negate statements • May not be powerful enough

  15. Ontology • Study of existence or being • The kinds of things that can be talked about in a system or context • Provides the means to classify these properties • Name and label them • Kinds of organization include • Lists, hierarchies, and trees

  16. Ontology • The ontology of a complicated semantic system can capture enough knowledge so a computer can perform everyday knowledge • To define a set of classes that together cover a domain of interest • Framework provides syntax, vocabulary, and some pre-defined terms • Framework is an ontology for constructing ontology

  17. Ontology Considerations • Merging Ontology • Terms and classes can by understood by more than one ontology • Can be accomplished by using same system (like OWL) • Even if able to merge, inconsistencies could jeopardize reliability • Imports of second ontology should be kept simple and be done so in small sections

  18. Merging Ontology

  19. Ontology Considerations • Problems with importing a large Ontology • Vocabulary may change over time once ontology is developed • Already committed to the vocabulary that is distributed • Can be limited by designer but no solutions have been made available

  20. Ontology Languages • Frameworks with Web-like uses • RDFS • Resource Description Framework Schema Specification • Base RDF language for describing ontology • Built on top of RDF • RDF makes statements about resources, making assertions about a subject • Every RDFS statement is a legal RDF Statement • RDF classes and properties • Together with standard classes, possible to give basic characteristics of classes for an ontology • Not enough power to express many constraints or logical properties

  21. Ontology Languages • OWL (Web Ontology Language) • Final Recommendation by W3C • Built to standardize a more capable Ontology Framework than RDFS • Need to restrict Cardinality • Express optionally • Combine classes

  22. Ontology Summary • To be useful for the semantic Web, an ontology language must be: • Able to reference concepts defined elsewhere on Web • Sharable over the Web • Be able to work with one or more languages • Able to merge several different ontologies • Widely accepted as a standard • Expressive enough for serious use • Support logical functions that are needed to conduct business of the semantic Web • Last two points are questioned abilities of OWL

  23. Logic • Uses in the Semantic Web • Applying and evaluating rules • Inferring facts that haven’t been explicitly stated • Explaining why a particular conclusion has been reached • Detecting contradictory statements and claims • Specifying ontology and vocabulary of all kinds • Representing knowledge • Describing the kinds of things that may be said about a subject • How those statements are to be understood • The statement and execution of queries to obtain information from stores of data • Combining information from distributed sources in a coherent way

  24. Logic • There is considerable risk that an opensystem will absorb contradictory or incorrect information • Most reasoning systems can not capture explanations • Ones that are able to can not do so in a uniform, easy to read way • More current versions of RDF define ways to understand a collection of RDF statements that can deal with the possibility of contradictory information • Requires more computing power

  25. Logic • Logic and Ontology • Ontology defines concepts and terms • Logic provides ways to make statements that define the use of concepts and terms • To reason about collections of statements that use the concepts and terms

  26. Logic • Logic and Representing Knowledge • Logic is a formal discipline dealing mostly with formal language that can express a subset of everything that can be articulated using natural language • Formal description of data and information naturally involves the use of logic

  27. Logic • Queries • Logical descriptions of information to be retrieved from a database • Queries will need to operate across distributed sources of data to be effective in the Semantic Web • Needs to reconcile the differences in ontology and deal with problems of contradictory data

  28. Logic • Problems of Semantic Web logic looks to deal with • When trying to decide what data should be imported from remote database • Size of the knowledge base might be too large and overwhelm resources • Importing data without duplicating knowledge • How much is interconnected? • Don’t want to import automatically because of a formation of any new data • Importing unreliable information • Contaminate good data

  29. Trust • With development in utility of Web, trust needs to be established between system and user • Trust • Identity: Who are you? • Why should I trust you? • Who else trusts you? • How much should I trust you? • How do I know that you said what you claimed you have said?

  30. Trust • Belief • How much confidence should I place in what you say? • What should I believe when different facts don’t agree • How much should my prior beliefs influence my confidence in what you say? • How can I establish the correct degree of belief for a given set of information?

  31. Tools of Trust • Keys – Private and Public • Digests • Special summary of a document or message that can not be reversed to the original, no key needed • Slight differences in messages amount to large differences in digest • Similarity of digests can not be used to predict similarity in messages

  32. Tools of Trust • Public Key Infrastructure (PKI) • Widespread system • Certificate Authority (CA) issues digital certificate • CA trustworthy source • CA signs certificate for other CAs • Creates chain of certifications, eventually amounting to a size that alleviates issues of trust • Potential problems • If CA private key compromised, entire chain untrustworthy • Large cross-certified chains can become unmanageable • CAs may have different standards • Human error and fraud • Digital Signatures • Authenticated by CA or CA chain

  33. Video The Semantic Web

  34. Work Cited Passin, T. B. Explorer's guide to the semantic web. Manning Publications, 2004. Print. http://www.w3schools.com/web/web_semantic.asp http://www.semanticfocus.com/media/insets/semantic-web-layer cake-2.pnghttp://www.xml.com/2003/02/05/graphics/graph1.gif http://www.codeproject.com/KB/books/GuideSemanticWeb/img002.jpg http://searchsoa.techtarget.com/definition/Semantic-Web http://semanticweb.org/wiki/Semantic_Web

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