Semantic Web – Machinery parts – china Pneumatic Accessories
Purpose
Humans are capable of using the Web to carry out tasks such as finding the Finnish word for “monkey”, reserving a library book, and searching for a low price for a DVD. However, a computer cannot accomplish the same tasks without human direction because web pages are designed to be read by people, not machines. The semantic web is a vision of information that is understandable by computers, so that they can perform more of the tedious work involved in finding, combining, and acting upon information on the web.
Tim Berners-Lee originally expressed the vision of the semantic web as follows:
I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web the content, links, and transactions between people and computers. A emantic Web, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ntelligent agents people have touted for ages will finally materialize.
Tim Berners-Lee, 1999
Semantic publishing will benefit greatly from the semantic web. In particular, the semantic web is expected to revolutionize scientific publishing, such as real-time publishing and sharing of experimental data on the Internet. This simple but radical idea is now being explored by W3C HCLS group’s Scientific Publishing Task Force.
Semantic Web application areas are experiencing intensified interest due to the rapid growth in the use of the Web, together with the innovation and renovation of information content technologies. The Semantic Web is regarded as an integrator across different content and information applications and systems, and provide mechanisms for the realisation of Enterprise Information Systems. The rapidity of the growth experienced provides the impetus for researchers to focus on the creation and dissemination of innovative Semantic Web technologies, where the envisaged emantic Web is long overdue. Often the terms emantics, etadata, ntologies and emantic Web are used inconsistently. In particular, these terms are used as everyday terminology by researchers and practitioners, spanning a vast landscape of different fields, technologies, concepts and application areas. Furthermore, there is confusion with regards to the current status of the enabling technologies envisioned to realise the Semantic Web. In a paper presented by Gerber, Barnard and Van der Merwe the Semantic Web landscape are charted and a brief summary of related terms and enabling technologies are presented. The architectural model proposed by Tim Berners-Lee is used as basis to present a status model that reflects current and emerging technologies .
Web 3.0
Tim Berners-Lee has described the semantic web as a component of ‘Web 3.0’.
People keep asking what Web 3.0 is. I think maybe when you’ve got an overlay of scalable vector graphics – everything rippling and folding and looking misty on Web 2.0 and access to a semantic Web integrated across a huge space of data, you’ll have access to an unbelievable data resource.”
Tim Berners-Lee, 2006
Relationship to the hypertext web
Limitations of HTML
Many files on a typical computer can be loosely divided into documents and data. Documents like mail messages, reports, and brochures are read by humans. Data, like calendars, addressbooks, playlists, and spreadsheets are presented using an application program which lets them be viewed, searched and combined in many ways.
Currently, the World Wide Web is based mainly on documents written in Hypertext Markup Language (HTML), a markup convention that is used for coding a body of text interspersed with multimedia objects such as images and interactive forms. Metadata tags, for example
<meta name=”keywords” content=”computing, computer studies, computer”>
<meta name=”description” content=”Cheap widgets for sale”>
<meta name=”author” content=”John Doe”>
provide a method by which computers can categorise the content of web pages.
With HTML and a tool to render it (perhaps web browser software, perhaps another user agent), one can create and present a page that lists items for sale. The HTML of this catalog page can make simple, document-level assertions such as “this document’s title is ‘Widget Superstore'”, but there is no capability within the HTML itself to assert unambiguously that, for example, item number X586172 is an Acme Gizmo with a retail price of 199, or that it is a consumer product. Rather, HTML can only say that the span of text “X586172” is something that should be positioned near “Acme Gizmo” and “199”, etc. There is no way to say “this is a catalog” or even to establish that “Acme Gizmo” is a kind of title or that “199” is a price. There is also no way to express that these pieces of information are bound together in describing a discrete item, distinct from other items perhaps listed on the page.
Semantic HTML refers to the traditional HTML practice of markup following intention, rather than specifying layout details directly. For example, the use of <em> denoting “emphasis” rather than <i>, which specifies italics. Layout details are left up to the browser, in combination with Cascading Style Sheets. But this practice falls short of specifying the semantics of objects such as items for sale or prices.
Microformats represent unofficial attempts to extend HTML syntax to create machine-readable semantic markup about objects such as retail stores and items for sale.
Semantic Web solutions
The Semantic Web takes the solution further. It involves publishing in languages specifically designed for data: Resource Description Framework (RDF), Web Ontology Language (OWL), and Extensible Markup Language (XML). HTML describes documents and the links between them. RDF, OWL, and XML, by contrast, can describe arbitrary things such as people, meetings, or airplane parts. Tim Berners-Lee calls the resulting network of Linked Data the Giant Global Graph, in contrast to the HTML-based World Wide Web.
These technologies are combined in order to provide descriptions that supplement or replace the content of Web documents. Thus, content may manifest itself as descriptive data stored in Web-accessible databases , or as markup within documents (particularly, in Extensible HTML (XHTML) interspersed with XML, or, more often, purely in XML, with layout or rendering cues stored separately). The machine-readable descriptions enable content managers to add meaning to the content, i.e., to describe the structure of the knowledge we have about that content. In this way, a machine can process knowledge itself, instead of text, using processes similar to human deductive reasoning and inference, thereby obtaining more meaningful results and helping computers to perform automated information gathering and research.
An example of a tag that would be used in a non-semantic web page:
<item>cat</item>
Encoding similar information in a semantic web page might look like this:
<item rdf:about=”http://dbpedia.org/resource/Cat”>Cat</item>
Relationship to object oriented programming
A number of authors highlight the similarities which the Semantic Web shares with object-oriented programming (OOP). Both the semantic web and object-oriented programming have classes with attributes and the concept of instances or objects. Linked Data uses Dereferenceable Uniform Resource Identifiers in a manner similar to the common programming concept of pointers or “object identifiers” in OOP. Dereferenceable URIs can thus be used to access “data by reference”. The Unified Modeling Language is designed to communicate about object-oriented systems, and can thus be used for both object-oriented programming and semantic web development.
When the web was first being created in the late 1980s and early 1990s, it was done using object-oriented programming languages[citation needed] such as Objective-C, Smalltalk and CORBA. In the mid-1990s this development practice was furthered with the announcement of the Enterprise Objects Framework, Portable Distributed Objects and WebObjects all by NeXT, in addition to the Component Object Model released by Microsoft. XML was then released in 1998, and RDF a year after in 1999.
Similarity to object oriented programming also came from two other routes: the first was the development of the very knowledge-centric “Hyperdocument” systems by Douglas Engelbart, and the second comes from the usage and development of the Hypertext Transfer Protocol.[clarification needed]
Skeptical reactions
Practical feasibility
Critics (e.g. Which Semantic Web?) question the basic feasibility of a complete or even partial fulfillment of the semantic web. Cory Doctorow’s critique (“metacrap”) is from the perspective of human behavior and personal preferences. For example, people lie: they may include spurious metadata into Web pages in an attempt to mislead Semantic Web engines that naively assume the metadata’s veracity. This phenomenon was well-known with metatags that fooled the AltaVista ranking algorithm into elevating the ranking of certain Web pages: the Google indexing engine specifically looks for such attempts at manipulation. Peter Grdenfors and Timo Honkela point out that logic-based semantic web technologies cover only a fraction of the relevant phenomena related to semantics .
Where semantic web technologies have found a greater degree of practical adoption, it has tended to be among core specialized communities and organizations for intra-company projects. The practical constraints toward adoption have appeared less challenging where domain and scope is more limited than that of the general public and the World-Wide Web.
The potential of an idea in fast progress
The original 2001 Scientific American article by Berners-Lee described an expected evolution of the existing Web to a Semantic Web. A complete evolution as described by Berners-Lee has yet to occur. In 2006, Berners-Lee and colleagues stated that: “This simple idea, however, remains largely unrealized.” While the idea is still in the making, it seems to evolve quickly and inspire many. Between 2007-2010 several scholars have already explored first applications and the social potential of the semantic web in the business and health sectors, and for social networking and even for the broader evolution of democracy, specifically, how a society forms its common will in a democratic manner through a semantic web
Censorship and privacy
Enthusiasm about the semantic web could be tempered by concerns regarding censorship and privacy. For instance, text-analyzing techniques can now be easily bypassed by using other words, metaphors for instance, or by using images in place of words. An advanced implementation of the semantic web would make it much easier for governments to control the viewing and creation of online information, as this information would be much easier for an automated content-blocking machine to understand. In addition, the issue has also been raised that, with the use of FOAF files and geo location meta-data, there would be very little anonymity associated with the authorship of articles on things such as a personal blog.
Doubling output formats
Another criticism of the semantic web is that it would be much more time-consuming to create and publish content because there would need to be two formats for one piece of data: one for human viewing and one for machines. However, many web applications in development are addressing this issue by creating a machine-readable format upon the publishing of data or the request of a machine for such data. The development of microformats has been one reaction to this kind of criticism.
Specifications such as eRDF and RDFa allow arbitrary RDF data to be embedded in HTML pages. The GRDDL (Gleaning Resource Descriptions from Dialects of Language) mechanism allows existing material (including microformats) to be automatically interpreted as RDF, so publishers only need to use a single format, such as HTML.
Need
The idea of a ‘semantic web’ necessarily coming from some marking code other than simple HTML is built on the assumption that it is not possible for a machine to appropriately interpret code based on nothing but the order relationships of letters and words. If this is not true, then it may be possible to build a ‘semantic web’ on HTML alone, making a specially built ‘semantic web’ coding system unnecessary.
There are latent dynamic network models that can, under certain conditions, be ‘trained’ to appropriately ‘learn’ meaning based on order data, in the process ‘learning’ relationships with order (a kind of rudimentary working grammar). See for example latent semantic analysis
Components
The Semantic Web Stack.
The semantic web comprises the standards and tools of XML, XML Schema, RDF, RDF Schema and OWL that are organized in the Semantic Web Stack. The OWL Web Ontology Language Overview describes the function and relationship of each of these components of the semantic web:
XML provides an elemental syntax for content structure within documents, yet associates no semantics with the meaning of the content contained within.
XML Schema is a language for providing and restricting the structure and content of elements contained within XML documents.
RDF is a simple language for expressing data models, which refer to objects (“resources”) and their relationships. An RDF-based model can be represented in XML syntax.
RDF Schema is a vocabulary for describing properties and classes of RDF-based resources, with semantics for generalized-hierarchies of such properties and classes.
OWL adds more vocabulary for describing properties and classes: among others, relations between classes (e.g. disjointness), cardinality (e.g. “exactly one”), equality, richer typing of properties, characteristics of properties (e.g. symmetry), and enumerated classes.
SPARQL is a protocol and query language for semantic web data sources.
Current ongoing standardizations include:
Rule Interchange Format (RIF) as the Rule Layer of the Semantic Web Stack
Not yet fully realized layers include:
Unifying Logic and Proof layers are undergoing active research.
The intent is to enhance the usability and usefulness of the Web and its interconnected resources through:
Servers which expose existing data systems using the RDF and SPARQL standards. Many converters to RDF exist from different applications. Relational databases are an important source. The semantic web server attaches to the existing system without affecting its operation.
Documents “marked up” with semantic information (an extension of the HTML <meta> tags used in today’s Web pages to supply information for Web search engines using web crawlers). This could be machine-understandable information about the human-understandable content of the document (such as the creator, title, description, etc., of the document) or it could be purely metadata representing a set of facts (such as resources and services elsewhere in the site). (Note that anything that can be identified with a Uniform Resource Identifier (URI) can be described, so the semantic web can reason about animals, people, places, ideas, etc.) Semantic markup is often generated automatically, rather than manually.
Common metadata vocabularies (ontologies) and maps between vocabularies that allow document creators to know how to mark up their documents so that agents can use the information in the supplied metadata (so that Author in the sense of ‘the Author of the page’ won’t be confused with Author in the sense of a book that is the subject of a book review).
Automated agents to perform tasks for users of the semantic web using this data
Web-based services (often with agents of their own) to supply information specifically to agents (for example, a Trust service that an agent could ask if some online store has a history of poor service or spamming)
Challenges
Some of the challenges for the Semantic Web include vastness, vagueness, uncertainty, inconsistency and deceit. Automated reasoning systems will have to deal with all of these issues in order to deliver on the promise of the Semantic Web.
Vastness: The World Wide Web contains at least 48 billion pages as of this writing (August 2, 2009). The SNOMED CT medical terminology ontology contains 370,000 class names, and existing technology has not yet been able to eliminate all semantically duplicated terms. Any automated reasoning system will have to deal with truly huge inputs.
Vagueness: These are imprecise concepts like “young” or “tall”. This arises from the vagueness of user queries, of concepts represented by content providers, of matching query terms to provider terms and of trying to combine different knowledge bases with overlapping but subtly different concepts. Fuzzy logic is the most common technique for dealing with vagueness.
Uncertainty: These are precise concepts with uncertain values. For example, a patient might present a set of symptoms which correspond to a number of different distinct diagnoses each with a different probability. Probabilistic reasoning techniques are generally employed to address uncertainty.
Inconsistency: These are logical contradictions which will inevitably arise during the development of large ontologies, and when ontologies from separate sources are combined. Deductive reasoning fails catastrophically when faced with inconsistency, because “anything follows from a contradiction”. Defeasible reasoning and paraconsistent reasoning are two techniques which can be employed to deal with inconsistency.
Deceit: This is when the producer of the information is intentionally misleading the consumer of the information. Cryptography techniques are currently utilized to alleviate this threat.
This list of challenges is illustrative rather than exhaustive, and it focuses on the challenges to the “unifying logic” and “proof” layers of the Semantic Web. The World Wide Web Consortium (W3C) Incubator Group for Uncertainty Reasoning for the World Wide Web (URW3-XG) final report lumps these problems together under the single heading of “uncertainty”. Many of the techniques mentioned here will require extensions to the Web Ontology Language (OWL) for example to annotate conditional probabilities. This is an area of active research.
Projects
This section provides some example projects and tools, but is very incomplete. The choice of projects is somewhat arbitrary but may serve illustrative purposes. It is also remarkable that in this early stage of the development of semantic web technology, it is already possible to compile a list of hundreds of components that in one way or another can be used in building or extending semantic webs.
DBpedia
DBpedia is an effort to publish structured data extracted from Wikipedia: the data is published in RDF and made available on the Web for use under the GNU Free Documentation License, thus allowing Semantic Web agents to provide inferencing and advanced querying over the Wikipedia-derived dataset and facilitating interlinking, re-use and extension in other data-sources.
FOAF
A popular application of the semantic web is Friend of a Friend (or FoaF), which uses RDF to describe the relationships people have to other people and the “things” around them. FOAF permits intelligent agents to make sense of the thousands of connections people have with each other, their jobs and the items important to their lives; connections that may or may not be enumerated in searches using traditional web search engines. Because the connections are so vast in number, human interpretation of the information may not be the best way of analyzing them.
FOAF is an example of how the Semantic Web attempts to make use of the relationships within a social context.
GoodRelations for e-commerce
A huge potential for Semantic Web technologies lies in adding data structure and typed links to the vast amount of offer data, product model features, and tendering / request for quotation data.
The GoodRelations ontology is a popular vocabulary for expressing product information, prices, payment options, etc. It also allows expressing demand in a straightforward fashion.
GoodRelations has been adopted by BestBuy, Yahoo, OpenLink Software, O’Reilly Media, the Book Mashup, and many others.
SIOC
The SIOC Project – Semantically-Interlinked Online Communities provides a vocabulary of terms and relationships that model web data spaces. Examples of such data spaces include, among others: discussion forums, weblogs, blogrolls / feed subscriptions, mailing lists, shared bookmarks, image galleries.
SIMILE
Semantic Interoperability of Metadata and Information in unLike Environments
SIMILE is a joint project, conducted by the MIT Libraries and MIT CSAIL, which seeks to enhance interoperability among digital assets, schemata/vocabularies/ontologies, meta data, and services.
NextBio
A database consolidating high-throughput life sciences experimental data tagged and connected via biomedical ontologies. Nextbio is accessible via a search engine interface. Researchers can contribute their findings for incorporation to the database. The database currently supports gene or protein expression data and is steadily expanding to support other biological data types.
Linking Open Data
Datasets in the Linking Open Data project, as of Sept 2008
Class linkages within the Linking Open Data datasets
The Linking Open Data project is a W3C-led effort to create openly accessible, and interlinked, RDF Data on the Web. The data in question takes the form of RDF Data Sets drawn from a broad collection of data sources. There is a focus on the Linked Data style of publishing RDF on the Web.
OpenPSI
OpenPSI the (OpenPSI project) is a community effort to create UK government linked data service that supports research. It is a collaboration between the University of Southampton and the UK government, lead by OPSI at the National Archive and is supported by JISC funding.
Erfgoedplus.be
Erfgoedplus.be (‘heritage-plus’) is a project aimed at disclosing all types of heritage from the provinces of Limburg and Vlaams-Brabant and the city of Leuven to the public by applying semantic web technology. Erfgoedplus.be uses RDF/XML, OWL and SKOS to describe relationships to heritage types, concepts, objects, people, place and time. Data are normalized and enriched by means of thesauri (AAT) and an ontology (CIDOC CRM), available for input, conversion and navigation.
Erfgoedplus.be is a regional aggregator for EuropeanaLocal (Europeana) and an example of how semantic web technology is applied within the heterogeneous context of heritage.
See also
Book:Semantic Web
Books are collections of articles which can be downloaded or ordered in print.
Agris: International Information System for the Agricultural Sciences and Technology
Business semantics management
Corporate Semantic Web
Entity-attribute-value model
Linked Data
List of emerging technologies
Ontology learning
Semantic advertising
Semantic Sensor Web
Semantic Web Services
Social Semantic Web
Swoogle
Website Parse Template
Semantic MediaWiki
Smart-M3
References
^ Berners-Lee, Tim; James Hendler and Ora Lassila (May 17, 2001). “The Semantic Web”. Scientific American Magazine. http://www.sciam.com/article.cfm?id=the-semantic-web&print=true. Retrieved March 26, 2008.
^ a b “W3C Semantic Web Frequently Asked Questions”. W3C. http://www.w3.org/2001/sw/SW-FAQ. Retrieved March 13, 2008.
^ Herman, Ivan (March 7, 2008). “Semantic Web Activity Statement”. W3C. http://www.w3.org/2001/sw/Activity.html. Retrieved March 13, 2008.
^ “Design Issues”. W3C. http://www.w3.org/DesignIssues/. Retrieved March 13, 2008.
^ Herman, Ivan (March 12, 2008). “W3C Semantic Web Activity”. W3C. http://www.w3.org/2001/sw/. Retrieved March 13, 2008.
^ Berners-Lee, Tim; Fischetti, Mark (1999). Weaving the Web. HarperSanFrancisco. chapter 12. ISBN 9780062515872.
^ Gerber, AJ, Barnard, A & Van der Merwe, Alta (2006), A Semantic Web Status Model, Integrated Design & Process Technology, Special Issue: IDPT 2006
^ Gerber, Aurona; Van der Merwe, Alta; Barnard, Andries; (2008), A Functional Semantic Web architecture, European Semantic Web Conference 2008, ESWC08, Tenerife, June 2008.
^ Victoria Shannon (June 26, 2006). “A ‘more revolutionary’ Web”. International Herald Tribune. http://www.iht.com/articles/2006/05/23/business/web.php. Retrieved May 24, 2006.
^ Artem Chebotko and Shiyong Lu, “Querying the Semantic Web: An Efficient Approach Using Relational Databases”, LAP Lambert Academic Publishing, ISBN 978-3-8383-0264-5, 2009.
^ Knublauch, Holger; Oberle, Daniel; Tetlow, Phil; Evan (March 9, 2006). “A Semantic Web Primer for Object-Oriented Software Developers”. W3C. http://www.w3.org/2001/sw/BestPractices/SE/ODSD/. Retrieved July 30, 2008.
^ Connolly, Daniel (August 13, 2002). “An Evaluation of the World Wide Web with respect to Engelbart’s Requirements”. W3C. http://www.w3.org/Architecture/NOTE-ioh-arch. Retrieved July 30, 2008.
^ Engelbart, Douglas (1990). “Knowledge-Domain Interoperability and an Open Hyperdocument System”. Bootstrap Institute. http://www.bootstrap.org/augdocs/augment-132082.htm. Retrieved July 30, 2008.
^ Connolly, Dan. “From the editor… WebApps”. W3C. http://www.w3.org/People/Connolly/9703-web-apps-essay.html. Retrieved July 30, 2008.
^ Grdenfors, Peter (2004), “How to make the Semantic Web more semantic”, Formal Ontology in Information Systems: proceedings of the third international conference (FOIS-2004) (IOS Press): p. 1734
^ Timo Honkela, Ville Knnen, Tiina Lindh-Knuutila and Mari-Sanna Paukkeri (2008), “Simulating processes of concept formation and communication”, Journal of Economic Methodology, http://www.informaworld.com/smpp/content~content=a903999101
^ a b Ivan Herman (2007). “State of the Semantic Web”. Semantic Days 2007. http://www.w3.org/2007/Talks/0424-Stavanger-IH/Slides.pdf. Retrieved July 26, 2007.
^ Berners-Lee, Tim (May 1, 2001). “The Semantic Web”. Scientific American. http://www.sciam.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21. Retrieved March 13, 2008.
^ Nigel Shadbolt, Wendy Hall, Tim Berners-Lee (2006). “The Semantic Web Revisited”. IEEE Intelligent Systems. http://eprints.ecs.soton.ac.uk/12614/1/Semantic_Web_Revisted.pdf. Retrieved April 13, 2007.
^ Lee Feigenbaum (May 1, 2007). “The Semantic Web in Action”. Scientific American. http://www.thefigtrees.net/lee/sw/sciam/semantic-web-in-action. Retrieved February 24, 2010.
^ Martin Hilbert (April, 2009). “The Maturing Concept of E-Democracy: From E-Voting and Online Consultations to Democratic Value Out of Jumbled Online Chatter”. Journal of Information Technology and Politics. http://www.informaworld.com/smpp/content~db=all~content=a911066517. Retrieved February 24, 2010.
^ Lukasiewicz, Thomas; Umberto Straccia. “Managing uncertainty and vagueness in description logics for the Semantic Web”. http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B758F-4SPSPKW-1&_user=147018&_coverDate=11%2F30%2F2008&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_acct=C000012179&_version=1&_urlVersion=0&_userid=147018&md5=8123c273189b1148cadb12f95b87a5ef.
^ See, for instance: Bergman, Michael K.. “Sweet Tools”. AI3; Adaptive Information, Adaptive Innovation, Adaptive Infrastructure. http://www.mkbergman.com/?page_id=325. Retrieved January 5, 2009.
Further reading
Grigoris Antoniou, Frank van Harmelen (March 31, 2008). A Semantic Web Primer, 2nd Edition. The MIT Press. ISBN 0262012421. http://www.amazon.com/Semantic-Primer-Cooperative-Information-Systems/dp/0262012421/.
Dean Allemang, James Hendler (May 9, 2008). Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL. Morgan Kaufmann. ISBN 9780123735560. http://www.amazon.com/Semantic-Web-Working-Ontologist-Effective/dp/0123735564/.
John Davies (July 11, 2006). Semantic Web Technologies: Trends and Research in Ontology-based Systems. Wiley. ISBN 0470025964. http://www.amazon.com/Semantic-Web-Technologies-Research-Ontology-based/dp/0470025964/.
Pascal Hitzler, Markus Krtzsch, Sebastian Rudolph (August 25, 2009). Foundations of Semantic Web Technologies. CRCPress. ISBN 142009050X. http://www.semantic-web-book.org.
Thomas B. Passin (March 1, 2004). Explorer’s Guide to the Semantic Web. Manning Publications. ISBN 1932394206. http://www.amazon.com/Explorers-Guide-Semantic-Thomas-Passin/dp/1932394206/.
Liyang Yu (June 14, 2007). Introduction to Semantic Web and Semantic Web Services. CRC Press. ISBN 1584889330. http://www.amazon.com/Introduction-Semantic-Web-Services/dp/1584889330/.
Jeffrey T. Pollock (March 23, 2009). Semantic Web For Dummies. For Dummies. ISBN 0470396792. http://www.amazon.com/gp/product/0470396792.
Martin Hilbert (April, 2009). The Maturing Concept of E-Democracy: From E-Voting and Online Consultations to Democratic Value Out of Jumbled Online Chatter. Journal of Information Technology & Politics. http://www.informaworld.com/smpp/content~db=all~content=a911066517.
External links
Wikimedia Commons has media related to: Semantic Web
Official website
W3C Semantic Web Activity
links collection on Semantic Overflow
v d e
Semantic Web
Background
World Wide Web Internet Hypertext Databases Semantic networks Ontologies
Sub-topics
Linked Data Data Web Hyperdata Dereferenceable URIs Rule bases Data Spaces
Applications
Semantic wiki Semantic publishing Semantic search Semantic advertising Semantic reasoner Semantic matching Semantic mapper Semantic broker Semantic analytics Semantic service oriented architecture
Related topics
Folksonomy Library 2.0 Web 2.0 Open Database Connectivity References Information architecture Knowledge management Collective intelligence Topic Maps Mindmapping Metadata Geotagging Description logic
Standards
Syntax & Supporting Technologies : RDF (Notation 3 Turtle N-Triples) SPARQL URI HTTP XML
Schemas, Ontologies & Rules : RDFS OWL Rule Interchange Format Semantic Web Rule Language
Semantic Annotation : RDFa eRDF GRDDL Microformats
Common Vocabularies : FOAF SIOC Dublin Core SKOS
Others: Plain Old Semantic HTML
Categories: Semantic Web | Buzzwords | Web servicesHidden categories: Articles lacking reliable references from November 2009 | Wikipedia articles needing style editing from November 2009 | All articles needing style editing | Articles needing cleanup from November 2009 | All pages needing cleanup | Articles to be merged from November 2009 | All articles to be merged | Articles to be merged from October 2008 | All articles with unsourced statements | Articles with unsourced statements from August 2008 | Wikipedia articles needing clarification from November 2008 | Articles with too many examples
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