Introduction to Semantic Technologies

Semantic:
Refers to meaning or understanding in language or logic.
Web is a collection of vast amount of information and messy and disseminated documents. The Semantic Web, Web 3.0, the Linked Data, the Web of Data…whatever we call it, the Semantic Web represents the next major advancement in connecting information. It enables facts to be interrelated from a source to any other source to be understood by computers.

Semantic web:

Semantic Web is an effort to make the content in the WWW accessible and readable for a machine. Its goal is to change the current unstructured web into the web of structured data. In order to share information across different applications since data can be processed automatically by machines.

As such, the fundamental difference between Semantic Web technologies and other technologies related to data (such as relational databases or the World Wide Web itself) is that the Semantic Web is concerned with the meaning and not the structure of data.

It uses Set of standards for sharing data and the semantics of that data for use by applications end results will be understandable by machines.
 Set of Standards:  RDF data model, SPARQL and OWL.

These Semantic Web technologies enable machines to infer new facts from existing facts and data.  That is, Semantic Web technologies enable computers not only to store and retrieve information, but also to come up with entirely new information on their own.


Figure 1 : Web Evolution
RDF:

RDF (Resource Description Framework or Format) is one of the three foundational Semantic Web technologies, the other two being SPARQL and OWL.
RDF is the data model of the Semantic Web. It is a data model used to represent resources like sun book earth computer anything in this world.
Basically RDF in order to define a resource we need to have triple!
It is basic building block of a statement.
Universal machine readable exchange format.
All triples have same format subject predicate object à subject property value
Triples are the statements about things.
Subject n predicate will be URI’s Object is literal value.
All triples are graphs.
RDF is not like the tabular data model of relational databases. Nor is it like the trees of the XML world. Instead, RDF is a graph.
 XML based syntax is used to define a RDF. Multiple Notations include like N3,JSON,Turtle and few others.


Figure 2: RDF Model 
Resource: In theory a resource could be anything with URI but here for simplicity we assume resource as a “Semantic” exist on web and accessible on web via URL Uniform Resource Locator. An example of such a resource could be a web page like

http://cstechpause.blogspot.in//Blog/Semantic

Properties: Properties define a relationship for a resource. Like a property writtenBy defining its relationship with others. These properties itself could be a resource like

http://cstechpause.blogspot.in//Blog/Semantic/WrittenBy.

The idea of representing properties as resources is to develop a common vocabulary and naming scheme for relations that are similar to different domains. It makes easy for machines to process the data with help of their common metadata. E.g. if all Blog sites use the same property writtenBy from a common vocabulary to show the writer of Blog then it will be very
easy for a machine to understand this term from different sites.

Properties Values: Properties values are the values of the properties like in above example value of property WrittenBy will be a name e.g. Reddy. The property values either could be a string literal or a web resource having a URI like

http://cstechpause.blogspot.in//Blog/Semantic/Author/Reddy.

Jena is an open source Semantic Web framework for Java. It provides an API to extract data from and write to RDF graphs.
RDF provides a way to express simple statements about resources, using subject – predicate –object triples. However, to use RDF we also need the possibility to the define the vocabulary that is used in the RDF statements. This controlled vocabulary is also called ontology

For a given domain the ontology defines the concepts found in the domain, the relationships between these concepts, and the properties used to describe the concepts.

The semantic web technology relies on ontology as its backbone. Semantic web contributes several mechanisms that can be used to classify information and characterize its context for intelligently retrieving information on web. This is mainly done using knowledge representation languages that create explicitly domain conceptualizations, such as ontologies.


Ontology:
Ontology: What exists in a domain and how they relate with each other?
Ontology defines the common words and concepts (the meaning) used to describe and represent an area of knowledge.

 
 
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 Ontology is a formal explicit specification of shared conceptualization
Formal refers to the facts that Ontology should be machine readable.
Explicit specification means type of concepts, the relations between concepts.
Shared conceptualization refers to relevant concepts involved in particular domain.

Let’s make ontology for a specific domain: Organization. What are the concepts involved in an Organization? CEO, Account, Manager Operation, Manager Delivery, Worker. We all agree on this, right? This is the shared conceptualization. How are these concepts related? A Worker works under a manager. A Manager assigns some work to worker. Particular work is offered by a Manager Operation. These are all explicit specifications of the concepts that we are talking about. Now, we need to represent them in a way that a computer can understand it. In other words, we need a formal computer language.
For example OWL is a computer language used to write ontologies. OWL (Web Ontological Language) is the W3C standard to represent ontologies on the web.

SPARQL:
It is acronym for SPARQL for SPARQL protocol and RDF query language. Used for RDF repositories. (Triple collection). Like SQL which is used to retrieve data from a relational database such as MS SQL or MySQL.


Semantic technologies don't refer to a single technology, but rather to a wide variety of tools and technologies that have to do with meaning. Natural language processing (NLP),
Sentiment Analysis Sentiment Analysis measures the "sentiment" of an article, typically meaning whether the article's tone is positive, negative, or neutral. For example, a business owner might ask an application to "alert me when someone says something negative regarding my company on Facebook."
Question Answering: This is the new hot topic in NLP, as evidenced by Watson.
Some focus on structure, some on text.

for more information @reddy


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