MALAIKA GHUFRAN
10470
SEMANTIC WEB
Explain the key concepts of the semantic web and how they relate to the
traditional web. Provide examples of how semantic web technologies can
be used to improve web-based applications.
“Semantic Web” refers to W3C’s vision of the Web of linked data. Semantic Web technologies let
people to create data stores on the Web, build vocabularies, and write rules for handling data.
Linked data are empowered by technologies such as RDF, SPARQL, OWL, and SKOS.
Main Components of the Semantic Web
1. Knowledge Representation: structured data, such as machine-interpretable metadata written
in RDF , HTML5 , Micro-data, and JSON-LD annotations.
2. Knowledge Management: data modelling with RDF, knowledge organisation with SKOS,
machine-interpretable controlled vocabularies and ontologies in RDFS and OWL suitable for
integrity checking and the automatic discovery of relationships between seemingly
unrelated structured data (inference); querying RDF with SPARQL
3. Linked Data: best practices for publishing structured data
4. Query languages : Query languages go hand-in-hand with databases. If the Semantic Web is
viewed as a global database, then it is easy to understand why one would need a query
language for that data.
5. Inference : Near the top of the Semantic Web stack one nds inference reasoning over data
through rules. W3C work on rules, primarily through RIF and OWL, is focused on translating
between rule languages and exchanging rules among di erent systems.
Some of Core Semantic Web Features are ;
• Machine-interpretable, structured, interlinked, open access data repositories
• Globally edited adaptive information resources
• Unique web resource identi ers for every bit of information (e.g., each table data cell of a table
has a unique identi er)
Semantic Web technologies can be used in a variety of application areas; for example:
in data integration, whereby data in various locations and various formats can be integrated in
one, seamless application; in resource discovery and classi cation to provide better, domain
speci c search engine capabilities; in cataloging for describing the content and content
relationships available at a particular Web site, page, or digital library; by intelligent software
agents to facilitate knowledge sharing and exchange; in content rating; in describing collections of
pages that represent a single logical “document”; for describing intellectual property rights of Web
pages (see, eg, the Creative Commons), and in many others.
For example, a typical web page contains structuring elements, formatted text, and some
even multimedia objects. By default, the headings, texts, links, and other web site components
created by the web designer are meaningless to computers. While browsers can display web
documents based on the markup, only the human mind can interpret the meaning of information,
so there is a huge gap between what computers and humans understand. Even if alternate text is
speci ed for images (alt attribute with descriptive value on the img or gure elements), the data is
not structured or linked to related data, and human-readable words of conventional web page
paragraphs are not associated with any particular software syntax or structure. Without context,
the information provided by web sites can be ambiguous to search engines.
In contrast to the conventional Web (the “Web of documents”), the Semantic Web includes the
“Web of Data,” which connects “things” (representing real-world humans and objects) rather than
documents meaningless to computers. The machine-readable datasets of the Semantic Web are
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10470
SEMANTIC WEB
Explain the key concepts of the semantic web and how they relate to the
traditional web. Provide examples of how semantic web technologies can
be used to improve web-based applications.
“Semantic Web” refers to W3C’s vision of the Web of linked data. Semantic Web technologies let
people to create data stores on the Web, build vocabularies, and write rules for handling data.
Linked data are empowered by technologies such as RDF, SPARQL, OWL, and SKOS.
Main Components of the Semantic Web
1. Knowledge Representation: structured data, such as machine-interpretable metadata written
in RDF , HTML5 , Micro-data, and JSON-LD annotations.
2. Knowledge Management: data modelling with RDF, knowledge organisation with SKOS,
machine-interpretable controlled vocabularies and ontologies in RDFS and OWL suitable for
integrity checking and the automatic discovery of relationships between seemingly
unrelated structured data (inference); querying RDF with SPARQL
3. Linked Data: best practices for publishing structured data
4. Query languages : Query languages go hand-in-hand with databases. If the Semantic Web is
viewed as a global database, then it is easy to understand why one would need a query
language for that data.
5. Inference : Near the top of the Semantic Web stack one nds inference reasoning over data
through rules. W3C work on rules, primarily through RIF and OWL, is focused on translating
between rule languages and exchanging rules among di erent systems.
Some of Core Semantic Web Features are ;
• Machine-interpretable, structured, interlinked, open access data repositories
• Globally edited adaptive information resources
• Unique web resource identi ers for every bit of information (e.g., each table data cell of a table
has a unique identi er)
Semantic Web technologies can be used in a variety of application areas; for example:
in data integration, whereby data in various locations and various formats can be integrated in
one, seamless application; in resource discovery and classi cation to provide better, domain
speci c search engine capabilities; in cataloging for describing the content and content
relationships available at a particular Web site, page, or digital library; by intelligent software
agents to facilitate knowledge sharing and exchange; in content rating; in describing collections of
pages that represent a single logical “document”; for describing intellectual property rights of Web
pages (see, eg, the Creative Commons), and in many others.
For example, a typical web page contains structuring elements, formatted text, and some
even multimedia objects. By default, the headings, texts, links, and other web site components
created by the web designer are meaningless to computers. While browsers can display web
documents based on the markup, only the human mind can interpret the meaning of information,
so there is a huge gap between what computers and humans understand. Even if alternate text is
speci ed for images (alt attribute with descriptive value on the img or gure elements), the data is
not structured or linked to related data, and human-readable words of conventional web page
paragraphs are not associated with any particular software syntax or structure. Without context,
the information provided by web sites can be ambiguous to search engines.
In contrast to the conventional Web (the “Web of documents”), the Semantic Web includes the
“Web of Data,” which connects “things” (representing real-world humans and objects) rather than
documents meaningless to computers. The machine-readable datasets of the Semantic Web are
fi fi fi fffi fi fi
