W3C’s Semantic Web logo

The Semantic Web is a collaborative movement led by international standards body the World Wide Web Consortium (W3C).^[1] The standard promotes common data formats on the World Wide Web. By encouraging the inclusion of semanticcontent in web pages, the Semantic Web aims at converting the current web, dominated by unstructured and semi-structured documents into a “web of data”. The Semantic Web stack builds on the W3C’s Resource Description Framework (RDF).^[2]

According to the W3C, “The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries”.^[2] The term was coined by Tim Berners-Lee for a web of data that can be processed by machines.^[3]

While its critics have questioned its feasibility, proponents argue that applications in industry, biology and human sciences research have already proven the validity of the original concept. Scholars have explored the social potential of the semantic web in the business and health sectors, and for social networking.^[4]

The original 2001 Scientific American article by Berners-Lee, Hendler, and Lassila described an expected evolution of the existing Web to a Semantic Web,^[5] but this has yet to happen. In 2006, Berners-Lee and colleagues stated that: “This simple idea…remains largely unrealized”.^[6]

History[edit]

The concept of the Semantic Network Model was formed in the early 1960s by the cognitive scientist Allan M. Collins, linguist M. Ross Quillian and psychologist Elizabeth F. Loftus in various publications,^{[7][8][9][10][11]} as a form to represent semantically structured knowledge. It extends the network of hyperlinked human-readable web pages by inserting machine-readable metadata about pages and how they are related to each other, enabling automated agents to access the Web more intelligently and perform tasks on behalf of users. The term “Semantic Web” was coined by Tim Berners-Lee,^[3] the inventor of the World Wide Web and director of the World Wide Web Consortium (“W3C“), which oversees the development of proposed Semantic Web standards. He defines the Semantic Web as “a web of data that can be processed directly and indirectly by machines”.

Many of the technologies proposed by the W3C already existed before they were positioned under the W3C umbrella. These are used in various contexts, particularly those dealing with information that encompasses a limited and defined domain, and where sharing data is a common necessity, such as scientific research or data exchange among businesses. In addition, other technologies with similar goals have emerged, such as microformats.

Purpose[edit]

The main purpose of the Semantic Web is driving the evolution of the current Web by enabling users to find, share, and combine information more easily. Humans are capable of using the Web to carry out tasks such as finding the Estonian translation for “twelve months”, reserving a library book, and searching for the lowest price for a DVD. However, machinescannot accomplish all of these 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 can be readily interpreted by machines, so machines can perform more of the tedious work involved in finding, combining, and acting upon information on the web.

The Semantic Web, as originally envisioned, is a system that enables machines to “understand” and respond to complex human requests based on their meaning. Such an “understanding” requires that the relevant information sources be semantically structured.

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 “Semantic Web”, which makes 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 “intelligent agents” people have touted for ages will finally materialize.^[12]

The Semantic Web is regarded as an integrator across different content, information applications and systems. It has applications in publishing, blogging, and many other areas.

Often the terms “semantics“, “metadata“, “ontologies“, and “Semantic 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 regard to the current status of the enabling technologies envisioned to realize the Semantic Web. Gerber, Barnard, and Van der Merwe chart the Semantic Web landscape and provide a brief summary of related terms and enabling technologies in a paper.^[13] The architectural model proposed by Tim Berners-Lee is used as basis to present a status model that reflects current and emerging technologies.^[14]

Limitations of HTML[edit]

Many files on a typical computer can also be loosely divided into human readable documents and machine readable 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.

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 provide a method by which computers can categorise the content of web pages, for example:

<meta name="keywords" content="computing, computer studies, computer" />
<meta name="description" content="Cheap widgets for sale" />
<meta name="author" content="John Doe" />

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 extend HTML syntax to create machine-readable semantic markup about objects including people, organisations, events and products.^[15] Similar initiatives include RDFa, Microdata and Schema.org.

Semantic Web solutions[edit]

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.

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,^[16] 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>blog</item>

Encoding similar information in a semantic web page might look like this:

<item rdf:about="http://example.org/semantic-web/">Semantic Web</item>

Tim Berners-Lee calls the resulting network of Linked Data the Giant Global Graph, in contrast to the HTML-based World Wide Web. Berners-Lee posits that if the past was document sharing, the future is data sharing. His answer to the question of “how” provides three points of instruction. One, a URL should point to the data. Two, anyone accessing the URL should get data back. Three, relationships in the data should point to additional URLs with data.

Web 3.0[edit]

Tim Berners-Lee has described the semantic web as a component of “Web 3.0″.^[17]

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

“Semantic Web” is sometimes used as a synonym for “Web 3.0″,^[18] though each term’s definition varies.

Challenges[edit]

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 many billions of pages. The SNOMED CT medical terminology ontologyalone 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.^[19]

Standards[edit]

Standardization for Semantic Web in the context of Web 3.0 is under the care of W3C.^[20]

Components[edit]

The term “Semantic Web” is often used more specifically to refer to the formats and technologies that enable it.^[2] The collection, structuring and recovery of linked data are enabled by technologies that provide a formal description of concepts, terms, and relationships within a given knowledge domain. These technologies are specified as W3C standards and include:

• Resource Description Framework (RDF), a general method for describing information
• RDF Schema (RDFS)
• Simple Knowledge Organization System (SKOS)
• SPARQL, an RDF query language
• Notation3 (N3), designed with human-readability in mind
• N-Triples, a format for storing and transmitting data
• Turtle (Terse RDF Triple Language)
• Web Ontology Language (OWL), a family of knowledge representation languages
• Rule Interchange Format (RIF), a framework of web rule language dialects supporting rule interchange on the Web

The Semantic Web Stack.

The Semantic Web Stack illustrates the architecture of the Semantic Web. The functions and relationships of the components can be summarized as follows:^[21]

• XML provides an elemental syntax for content structure within documents, yet associates no semantics with the meaning of the content contained within. XML is not at present a necessary component of Semantic Web technologies in most cases, as alternative syntaxes exists, such as Turtle. Turtle is a de facto standard, but has not been through a formal standardization process.
• 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 (“web resources“) and their relationships. An RDF-based model can be represented in a variety of syntaxes, e.g., RDF/XML, N3, Turtle, and RDFa.^[22] RDF is a fundamental standard of the Semantic Web.^[23][24][25]
• RDF Schema extends RDF and 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.
• RIF is the W3C Rule Interchange Format. It’s an XML language for expressing Web rules which computers can execute. RIF provides multiple versions, called dialects. It includes a RIF Basic Logic Dialect (RIF-BLD) and RIF Production Rules Dialect (RIF PRD).

Current state of standardization[edit]

Well-established standards:

• Unicode
• Uniform Resource Identifier
• XML
• RDF
• RDFS
• SPARQL
• Web Ontology Language (OWL)
• Rule Interchange Format (RIF)

Not yet fully realized:

• Unifying Logic and Proof layers

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.) or it could be purely metadata representing a set of facts (such as resources and services elsewhere on the site). Note that anythingthat 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

Skeptical reactions[edit]

Practical feasibility[edit]

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 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 Gärdenfors and Timo Honkela point out that logic-based semantic web technologies cover only a fraction of the relevant phenomena related to semantics.^[26][27]

Core, specialized communities and organizations for intra-company projects tended to practically adopt semantic web technologies greater than peripheral and less-specialized communities.^[28] 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.^[28]

Censorship and privacy[edit]

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 geolocation meta-data, there would be very little anonymity associated with the authorship of articles on things such as a personal blog. Some of these concerns were addressed in the “Policy Aware Web” project^[29] and is an active research and development topic.

Doubling output formats[edit]

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, manyweb 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. Another argument in defense of the feasibility of semantic web is the likely falling price of human intelligence tasks in digital labor markets, such as Amazon‘s Mechanical Turk.

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.

Projects[edit]

This section needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (November 2011)

This section lists some of the many projects and tools that exist to create Semantic Web solutions.^[30]

DBpedia[edit]

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.^[31]

FOAF[edit]

A popular vocabulary on 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;^[32] 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.

SIOC[edit]

The Semantically-Interlinked Online Communities project (SIOC, pronounced “shock”) provides a vocabulary of terms and relationships that model web data spaces. Examples of such data spaces include, among others: discussion forums, blogs,blogrolls / feed subscriptions, mailing lists, shared bookmarks and image galleries.

GoPubMed[edit]

GoPubMed is a knowledge-based search engine for biomedical texts. The Gene Ontology (GO) and Medical Subject Headings (MeSH) serve as “Table of contents” in order to structure the millions of articles of the MEDLINE database.^[33]The search engine allows its users to find relevant search results significantly faster than Pubmed.^{[citation needed]}

eagle-i.net[edit]

eagle-i is an open source, semantic web platform for entering and publishing information about resources used in biomedical research.^[34] The platform consists of the Semantic Web Entry and Editing Tool (SWEET), an RDF database, and a Search tool. All components of the eagle-i platform are driven by a central ontology to promote uniformity and interoperability with other platforms.^[35][36] The eagle-i software, documentation, and information are accessible through Harvard medical school’s open.med website.^[37] The eagle-i project started as a consortium of nine universities (Harvard,Oregon Health & Science University, Dartmouth, Jackson State, Montana State, University of Puerto Rico, Morehouse College, University of Alaska, and University of Hawaii), but is now being used by more than thirty universities.^[38]

NextBio[edit]

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 expression or protein expression data and sequence centric data and is steadily expanding to support other biological data types.

See also[edit]

Book: Semantic Web

References[edit]

• Roger Chaffin: “The concept of a semantic Relation”. In: Adrienne Lehrer u. a. (Hrsg.): Frames, Fields and contrasts. New essays in semantic and lexical organisation, Erlbaum, Hillsdale, N.J. 1992, ISBN 0-8058-1089-7, S. 253–288.
• Hermann Helbig: Die semantische Struktur natürlicher Sprache. Wissenspräsentation mit MultiNet, Springer, Heidelberg 2001, ISBN 3-540-67784-4.
• M. Ross Quillian: “Word concepts. A theory and simulation of some basic semantic capabilities”. In: Behavioral Science12 (1967), S. 410–430.
• M. Ross Quillian: “Semantic memory”. In: Marvin Minsky (Hrsg.): Semantic information processing, MIT Press, Cambridge, Mass. 1988.
• Klaus Reichenberger: Kompendium semantische Netze: Konzepte, Technologie, Modellierung, Springer, Heidelberg 2010, ISBN 3-642-04314-3.
• John F. Sowa: Principles of semantic networks. Explorations in the representation of knowledge, Morgan Kaufmann, San Mateo, Cal. 1991, ISBN 1-55860-088-4.

Further reading[edit]

• Aaron Swartz’s A Programmable Web: An unfinished Work donated by Morgan & Claypool Publishers after Aaron Swartz’s death in January 2013.
• Liyang Yu (January 6, 2011). A Developer’s Guide to the Semantic Web. Springer. ISBN 978-3-642-15969-5.
• Grigoris Antoniou, Frank van Harmelen (March 31, 2008). A Semantic Web Primer, 2nd Edition. The MIT Press.ISBN 0-262-01242-1.
• Dean Allemang, James Hendler (May 9, 2008). Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL. Morgan Kaufmann. ISBN 978-0-12-373556-0.
• John Davies (July 11, 2006). Semantic Web Technologies: Trends and Research in Ontology-based Systems. Wiley.ISBN 0-470-02596-4.
• Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph (August 25, 2009). Foundations of Semantic Web Technologies. CRCPress. ISBN 1-4200-9050-X.
• Thomas B. Passin (March 1, 2004). Explorer’s Guide to the Semantic Web. Manning Publications. ISBN 1-932394-20-6.
• Liyang Yu (June 14, 2007). Introduction to Semantic Web and Semantic Web Services. CRC Press. ISBN 1-58488-933-0.
• Jeffrey T. Pollock (March 23, 2009). Semantic Web For Dummies. For Dummies. ISBN 0-470-39679-2.
• 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 (Taylor & Francis Group) 6 (2): 87–110. doi:10.1080/19331680802715242.
• “Tim Berners-Lee Gives the Web a New Definition”
• Folmer, Erwin; Oude Luttighuis, Paul; Hillegersberg, Jos (April 2011). “Do semantic standards lack quality? A survey among 34 semantic standards”. Electronic Markets 21 (2): 99–111. doi:10.1007/s12525-011-0058-y. Retrieved 2012-05-19.