Semantic Web technology

                                                  SEMANTIC WEB 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.

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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.

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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).

• RDF is the format Semantic Technology uses to store data on the Semantic Web or in a semantic graph database.
• SPARQL is the semantic query language specifically designed to query data across various systems and databases, and to retrieve and process data stored in RDF format.
• (optionally) OWL is the computational logic-based language that is designed to show the data schema and that represents rich and complex knowledge about hierarchies of things and the relations between them. It is complementary to RDF and allows for formalizing a data schema/ontology in a given domain, separately from the data.

By formalizing meaning independently of data and using W3C’s standards, Semantic Technology enables machines to “understand”, share and reason with data in order to create more value for us, humans.

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.