Semantic Technology

Semantic Technology

Semantic technology uses formal semantics to help AI systems understand language and process information the way humans do. Thus, they are able to store, manage and retrieve information based on meaning and logical relationships. Various businesses are already using semantic technology and semantic graph databases such as Ontotext's GraphDB to manage their content, repurpose and reuse information, cut costs and gain new revenue streams.



Semantic Technology uses formal semantics to give meaning to the disparate data that surrounds us. Together with Linked Data technology, it builds relationships between data in various formats and sources, from one string to another, helping create context. Interlinked in this way, these pieces of raw data form a giant web of data or a knowledge graph, which connects a vast amount of descriptions of entities and concepts of general importance.

Semantic Technology defines and links data on the Web (or within an enterprise) by developing languages to express rich, self-describing interrelations of data in a form that machines can process. Thus, machines are not only able to process long strings of characters and index tons of data. They are also able to store, manage and retrieve information based on meaning and logical relationships. So, semantics adds another layer to the Web and is able to show related facts instead of just matching words.

Semantic Technology at a Glance

The core difference between Semantic Technology and other data technologies, the relational database, for instance, is that it deals with the meaning rather than the structure of the data.

World Wide Web Consortium’s Semantic Web initiative states that the purpose of this technology in the context of the Semantic Web is to create a ‘universal medium for the exchange of data’ by smoothly interconnecting the global sharing of any kind of personal, commercial, scientific and cultural data.

W3C has developed open specifications for Semantic Technology developers to follow and has identified, via open source development, the infrastructure parts that will be needed to scale in the Web and be applicable elsewhere.

The main standards that Semantic Technology builds on are the RDF (Resource Description Framework), SPARQL (SPARQL Protocol and RDF Query Language) and, optionally, OWL (Web Ontology Language).

Semantic Web technologies enable machines to understand and interpret data on the web, making it possible for machines to process information more intelligently. These technologies build upon the traditional web by enhancing data with meaning (semantics), allowing data to be linked and interpreted across various systems and contexts.

Here are some key Semantic Web technologies:

1. RDF (Resource Description Framework)

  • RDF is a framework for representing information about resources in a graph form. It expresses relationships between data in subject-predicate-object triples. This allows data from diverse sources to be linked and integrated.
  • Example:
    • Subject: <http://example.com/book/1>
    • Predicate: <http://example.com/hasTitle>
    • Object: "Semantic Web Basics"

2. OWL (Web Ontology Language)

  • OWL is a language for defining and instantiating ontologies, which are formal representations of a set of concepts and relationships within a domain. Ontologies allow for complex reasoning and classification.
  • OWL supports various levels of expressiveness, enabling precise modeling of complex relationships in data.

3. SPARQL (SPARQL Protocol and RDF Query Language)

  • SPARQL is a query language for retrieving and manipulating data stored in RDF format. It is analogous to SQL for databases but designed for querying graphs of linked data.
  • Example Query:
    sparql
    SELECT ?title WHERE {
  ?book <http://example.com/hasTitle> ?title .
}

4. RDFS (RDF Schema)

  • RDFS extends RDF with vocabulary for describing properties and classes, along with their relationships. It helps define hierarchies of classes and properties, enhancing RDF with semantic meaning.
  • Example: Define that a "book" is a type of "literary work."

5. Linked Data

  • Linked Data refers to best practices for publishing structured data on the web, so that it can be interlinked and become more useful. The core idea is that data published using RDF and SPARQL can be interlinked with other datasets, creating a "web of data."
  • Example: Linking a person’s profile across social media, job websites, and educational institutions.

6. Reasoning Engines

  • Semantic Web technologies often incorporate reasoning engines that can infer new knowledge from existing data. These engines use rules and logic to derive implicit information from explicitly stated facts.

7. FOAF (Friend of a Friend)

  • FOAF is an ontology used to describe people, their activities, and their relationships to other people and objects. It allows for the creation of social networks in a machine-readable format.

Benefits of Semantic Web Technologies:

  • Data Integration: Facilitates the integration of data across different sources and domains.
  • Interoperability: Enhances the ability of machines to interpret and integrate data from various formats and schemas.
  • Improved Search: Provides more intelligent and context-aware search capabilities, improving search accuracy and relevancy.

Industry Application of Semantic Technology

Semantic Technology helps enterprises discover smarter data, infer relationships and extract knowledge from enormous sets of raw data in various formats and from various sources. Semantic graph databases (which are based on the vision of the Semantic Web) such as Ontotext’s GraphDB, make data easier for machines to integrate, process and retrieve. This, in turn, enables organizations to gain faster and more cost-effective access to meaningful and accurate data, to analyze that data and turn it into knowledge. They can further use that knowledge to gain business insights, apply predictive models and make data-driven decisions.

Various businesses are already using Semantic Technology and semantic graph databases to manage their content, repurpose and reuse information, cut costs and gain new revenue streams.

  • In Media and Publishing, the BBC, the FT, SpringerNature and many others use semantic publishing to make data integration and knowledge discovery more efficient;
  • In Healthcare and Life Sciences, Astra Zeneca and other big Pharma companies make use of Semantic Technology for early hypotheses testing, monitoring of adverse reactions, analytics in patient records and much more.
  • In the financial industry and insurance sector, many companies have started adopting technologies to semantically enrich content and process complex and heterogeneous data.
  • In e-commerce, the automotive industry, the government and public sector, technology providers, the energy sector, the services sector and many more are employing Semantic Technology processes to extract knowledge from data by attributing meaning to various datasets. 
    • Advantages  
      • Enhanced search accuracy: Search engines can understand a user's query intent and deliver more relevant results. 
      • Smarter applications: Web applications can provide personalized experiences and automate tasks. 
      • Improved data integration: Different datasets can be integrated and analyzed to lead to new insights. 
      • Conflict detection: Semantic Web technology can help detect conflicts in various settings. 
    • Disadvantages 
      • Anonymity: The use of FOAF files and geolocation meta-data could make it difficult to determine the authorship of articles on personal blogs. 
      • Sharing and re-use of knowledge: The current use of linked data occurs in centralized data silos, making sharing and re-use cumbersome. 
      • Concept of Open Data: The concept of Open Data clashes with corporate interests. 
    • Censorship and privacy: An advanced implementation of the semantic web could make it easier for governments to control online information.                                                                                                                                              
    •                                                                                                             by                                                                                                                   M.RAGURAM 22USC037
                                                                                                    S.KEERTHIVASAN 22USC020


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