PowerPoint PresentationSlide 2Slide 3Slide 4Slide 5Slide 6Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18Slide 19Slide 20Slide 21Slide 22Slide 23Slide 24Slide 25Slide 27Slide 28Slide 29Slide 30Slide 31Slide 32Slide 33Slide 34Slide 35Slide 36Slide 37Slide 38Slide 39Slide 40Slide 41Slide 42Slide 43Slide 44Slide 45Slide 46Slide 47Slide 48Slide 49Slide 50Slide 51Slide 52Slide 53Slide 54Slide 55Chapter 1 A Semantic Web Primer1Chapter 1The Semantic Web VisionAuthored byGrigoris AntoniouFrank van HarmelenEdited by Jeff PartykaChapter 1 A Semantic Web Primer2Lecture Outline1. Today’s Web2. Introduction to the Semantic Web3. Myths and Roadblocks4. Use CasesChapter 1 A Semantic Web Primer3Today’s WebMost of today’s Web content is suitable for human consumption –Even Web content that is generated automatically from databases is usually presented without the original structural information found in databases Web content is primarily presentationalTypical Web uses today–seeking and making use of information, searching for and getting in touch with other people, reviewing catalogs of online stores and ordering products by filling out formsChapter 1 A Semantic Web Primer4Keyword-Based Search Engines Current Web activities are not particularly well supported by software tools–Except for keyword-based search engines (e.g. Google, Bing, Yahoo)The Web would not have been the huge success it was, were it not for search enginesChapter 1 A Semantic Web Primer5Problems of Keyword-Based Search EnginesHigh recall, low precision.Low or no recallResults are highly sensitive to vocabulary Results are single Web pages , or single resourcesHuman involvement is necessary to interpret and combine resultsResults of Web searches are not readily accessible by other software toolsExamples of Keyword Searching Problems Searching for information on 'John Smith', and realizing that there are thousands of John Smiths!Difficulty finding implementation detail for an open source tool (e.g: how to properly load Stanford NER')Difficulty finding information about a software component (e.g: Solr's MoreLikeThis filter)Retrieving many StackOverflow pages regarding a particular coding issueMapping query results to queries in an automated fashionChapter 1 A Semantic Web Primer8On HTMLWeb content is currently formatted for human readers rather than programsHTML is the predominant language in which Web pages are written (directly or using tools)Vocabulary describes presentationChapter 1 A Semantic Web Primer9An HTML Example<h1>Agilitas Physiotherapy Centre</h1>Welcome to the home page of the Agilitas Physiotherapy Centre. Do you feel pain? Have you had an injury? Let our staff Lisa Davenport,Kelly Townsend (our lovely secretary) and Steve Matthews take careof your body and soul.<h2>Consultation hours</h2>Mon 11am - 7pm<br>Tue 11am - 7pm<br>Wed 3pm - 7pm<br>Thu 11am - 7pm<br>Fri 11am - 3pm<p>But note that we do not offer consultation during the weeks of the <a href=". . .">State Of Origin</a> games.Chapter 1 A Semantic Web Primer10Problems with HTMLHumans have no problem with thisMachines (software agents) do:–How distinguish therapists from the secretary, –How determine exact consultation hours –They would have to follow the link to the State Of Origin games to find when they take place.Chapter 1 A Semantic Web Primer11Handling Text Ambiguity There are two general strategies to extract semantics hereThe first way is to use machine learning methods from NLP and other fields to interpret the semanticsThe second way is to create a standardized way to annotate the semantics explicitlySemantic Web technologies use the second strategyBUT…both strategies are useful in different cases.Chapter 1 A Semantic Web Primer12A Better Representation<company><treatmentOffered>Physiotherapy</treatmentOffered><companyName>Agilitas Physiotherapy Centre</companyName><staff><therapist>Lisa Davenport</therapist><therapist>Steve Matthews</therapist><secretary>Kelly Townsend</secretary></staff></company>Chapter 1 A Semantic Web Primer13Explicit MetadataThis representation is far more easily processable by machinesMetadata: data about data –Metadata capture part of the meaning of dataSemantic Web does not rely on text-based manipulation, but rather on machine-processable metadataA Programmatic ExampleYears ago, I wanted to buy a ticket to go to a rock show. The show was sold out, but the website's message board had a ticket exchange board for selling tickets. I wrote a sniping program to copy the HTML of the page, do lots of page specific parsing to find the HTML that contained the message board, and examine the title of the newest message for keywordsA Programmatic Example (cont)I had encoded if-then logic that would outline conditions for when I had detected a new message indicating that a ticket was being soldThe semantics of the application were embedded in my codeMy program worked, and I responded within 15 seconds to a new ticket postHowever, the next year – there was a site redesign! So I had to rewrite much of my code.Syntactic matching: exact matching of keywordsExample: A search for “Java programming language” returns only results containing the string “Java programming language”Semantic matching: concept-based matchingA search for “Java programming language” would return not only results containing the string “Java Programming Language”, but also results about J2EE, JSP, Servlets, OpenJDK, etc.Syntactic vs. SemanticChapter 1 A Semantic Web Primer17The Semantic Web ApproachRepresent Web content in a form that is more easily machine-processable.Use intelligent techniques to take advantage of these representations. The Semantic Web will gradually evolve out of the existing Web, it is not a competition to the current WWWChapter 1 A Semantic Web Primer18Main Ideas of Semantic WebIndividuals (instance data)RelationshipsConceptsOntologiesTools and FrameworksChapter 1 A Semantic Web Primer19IndividualsAny “thing” - could be a person, could be a real-world object (a plane), could be an abstract object (a week, a year)Statements can be made about individualsFor example, for an individual “John”:-John likes pizza-John studies at UTD-John drives a truckChapter 1 A Semantic Web Primer20RelationshipsRelationships tie together two