The Semantic Web Part-I in light of 'The Semantic Web' by Tim Berners-Lee, James Hendler and Ora Lassila, Exploratory Am.


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You wish to discover the Ms. Cook you met at an exchange meeting a year ago ...
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The Semantic Web Part-I in light of \'The Semantic Web\' by Tim Berners-Lee, James Hendler and Ora Lassila, Scientific American, May 2001 Prepared for IT620 March 11 , 2003

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Talk Outline Introduction Building Blocks Agents Assisting in Evolution of Knowledge Pointers to More Information

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Introduction

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Semantic Web Agent I recognize what you mean …

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Example (vision) Lucy setting up a restorative arrangement for her mother by means of Semantic web operator Agent recovers mother\'s recommended treatment from specialist\'s operator Looked up a few rundown of suppliers Checked the ones in-plan for mother\'s protection inside a 20 mail sweep of her home with a rating of superb or great on a trusted rating administration It then started attempting to discover a match between accessible arrangement times (supplied by the specialists of individual suppliers through their Web destinations) and Lucy\'s bustling calendars. The accentuated catchphrases show terms whose semantics, or significance, were characterized for the specialist through the Semantic Web

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What is Semantic Web? The Semantic Web is not a different Web but rather an expansion of the present one, in which data is given very much characterized significance, better empowering PCs and individuals to work in participation Machines will start to better comprehend information that they simply show at present

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Current Web The vital property of web is its all inclusiveness The force of a hypertext connection is that "anything can connection to anything" Information introduced ranges from that for human utilization to that created chiefly for machines To date, the Web has grown most quickly as a medium of reports for individuals as opposed to for information and data that can be handled naturally. The Semantic Web plans to compensate for this.

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Approach adjusted by Semantic Web Researchers Make the dialect for the guidelines as expressive as expected to permit the Web to reason as broadly as craved despite the fact that it could prompt Catch 22s and unanswerable inquiries Similar to web-improvement No focal database and consequently one can never make certain to discover everything Works entirely well Expressive force has made boundless data accessible Can be looked genuinely well with web indexes, which creates surprisingly finish records

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Challenges for supporting Semantic Web Adding rationale to web give a dialect that communicates both information and standards for thinking about the information and permit rules from any current learning representation framework to be sent out onto the Web. Rationale must be intense to portray complex properties however not all that capable that operators can be deceived by being requested that consider a Catch 22 Fortunately basic principles, for example, \'head hex-jolt is a kind of machine jolt\' is adequate

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Building Blocks

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Two vital innovations for supporting semantic networks XML : Extensible Markup Language RDF : Resource Description Framework

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XML Can be use to make own labels to add explanations to a site page Semantic Agents can make utilization of the labels to discover the comment areas, yet need to comprehend what the labels are implied for

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Using RDF Expresses significance in set of triples Each triple (subject, verb, and article) composed utilizing XML labels As a part of RDF a record makes statement Particular things (People, website pages) have property (\'is an educator\', \'is a sister of\') with specific values (another site page, People) Subject and questions are distinguished by URIs Verbs likewise recognized by URIs, which permits anybody to characterized new verb some place on the be web

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RDFs The triples of RDFs structure a web of data about related things RDFs use URIs to encode this data, it guarantees that ideas are attached to one of a kind definitions that everybody can discover on the web Example data about individuals and locations "(field 5 in database A) (will be a field of sort) (postal district) " utilizing URIs as opposed to phrases for every term

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Need for Ontologies Two databases may utilize distinctive terms for the same data A system, (for example, semantic specialist) that necessities to consolidate data from two databases expected to realize that the two terms are semantically proportional

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What is an Ontology A philosophy is a report or document that formally characterizes the relations among terms. The most average sort of philosophy for the Web has A scientific categorization An arrangement of surmising guidelines

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Taxonomy characterizes classes of articles and relations among them e.g. location is a sort of area and city codes are just relevant to areas Can characterized countless among substances by allocating properties to classes and permitting subclasses to acquire such properties if city codes must be of sort city and urban areas for the most part have Web destinations, we can talk about the Web webpage connected with a city code regardless of the fact that no database interfaces a city code straightforwardly to a Web website

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Inference Rules Example: "If a city code is connected with a state code, and a location uses that city code, then that location has the related state code." can be utilized to induce Cornell University location, being in Ithaca, must be in New York State, which is in the U.S ., and in this way ought to be organized to U.S. guidelines

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Use of Ontology Improve the exactness of Web hunts—the quest project can search for just those pages that allude to an exact idea rather than every one of the ones utilizing questionable catchphrases More propelled applications will utilize ontologies to relate the data on a page to the related learning structures and surmising rules

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An illustration http://www.cs.umd.edu/~hendler is page set apart up for use by semantic specialist A human can promptly discover the connection to a short true to life note and read there that Hendler got his Ph.D. from Brown University For a PC program (semantic specialist) to conclude that data, page is connected to a metaphysics of software engineering offices, which has data, for example, Professors work at colleges and they for the most part have doctorates Further markup on the page (not showed by the ordinary Web program) utilizes the cosmology\'s ideas to indicate that Hendler got his Ph.D. from the substance portrayed at the URI http://www. brown.edu — the Web page for Brown

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An intricate case Semantic Agents can derive data in light of markups present in numerous website pages Example You wish to discover the Ms. Cook you met at an exchange gathering a year ago You don\'t recollect that her first name You recall that she worked for one of your customers and that her child was an understudy at your institute of matriculation Search program filters through every one of the pages of individuals whose name is "Cook" (evading every one of the pages identifying with cooks, cooking, the Cook Islands et cetera) Find the ones that notice working for an organization that is on your rundown of customers and take after connections to Web pages of their youngsters to find if any are in school at the opportune spot

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Agents

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Agents The genuine force of the Semantic Web will be acknowledged when individuals make numerous projects that gather Web content from various sources, handle the data and trade the outcomes with different projects. The adequacy of such programming operators will increment exponentially as more machine-clear Web content and computerized administrations (counting different specialists) get to be accessible. The Semantic Web advances this cooperative energy: even operators that were not explicitly intended to cooperate can exchange information among themselves when the information accompany semantics.

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Agents showing proofs An imperative feature of specialists\' working will be the trading of "proofs" written in the Semantic Web\'s bringing together dialect (the dialect that communicates sensible deductions made utilizing guidelines and data, for example, those predetermined by ontologies). Case: Ms. Cook\'s contact data has been situated by an online administration, and to your incredible shock it puts her in Johannesburg. So you request a proof of this answer

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Agents use computerized marks Digital marks, which are scrambled squares of information must be utilized by PCs and operators to confirm that the joined data has been given by a particular trusted source Example: an announcement sent to your bookkeeping program that you owe cash to an online retailer is not a phony produced by the PC sagacious young person adjacent

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Agents use (semantic related) web-administrations Many mechanized Web-based administrations as of now exist without semantics, yet different projects, for example, specialists have no real way to find one that will play out a particular capacity. Administration disclosure, can happen just when there is a typical dialect to depict an administration in a way that lets different operators "understand" both the capacity offered and how to exploit it. Administrations and specialists can promote their capacity by, for instance, storing such portrayals in registries comparable to the Yellow Pages.

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Semantic Web versus Related Technologies Some low-level administration revelation plans are at present accessible: Microsoft\'s Universal Plug and Play, which concentrates on associating diverse sorts of gadgets Sun Microsystems\' Jini, which intends to interface administrations. These activities, be that as it may, assault the issue at a basic or syntactic level and depend intensely on institutionalization of a foreordained arrangement of usefulness portrayals. Institutionalization can just go in this way, since we can\'t foresee all conceivable future needs.

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Semantic Web versus Related Technologies The Semantic Web is more adaptable. The buyer and maker operators can achieve a common comprehension by trading ontologies, which give the vocabulary expected to dialog. Specialists can even "bootstrap" new thinking abilities when they find new ontologies. Semantics likewise makes it less demanding to take advan

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