What Is Semantic Technology?
Semantic technology uses formal semantics to help artificial intelligence systems understand language and process information like humans.
Therefore, they can store, manage, and retrieve data based on meaning and logical relationships. So several companies are already using semantic technology and semantic graph databases. Like Ontotext’s GraphDB to manage their content, and reuse and reuse information, reduce costs and create new revenue streams.
Semantic technology uses formal semantics to make sense of the disparate raw data surrounding us. because of Semantic technology, along with linked data technology as envisioned by the inventor of the World Wide Web, Sir Tim Berners-Lee. Establishes relationships between data in different formats and sources from one chain to another. And helps provide context to create and make connections these.
Relationships Semantic technology defines and connects data on the Web (or within an enterprise) by developing languages to express rich. Because self-describing interrelationships of data in a form that machines can process.
Therefore, the devices are capable of processing long strings and indexing vast amounts of data. So they can also store, manage, and retrieve information based on meaning and logical relationships. So semantics adds a layer to the Web and can show related facts instead of just putting words together.
Semantic Technology At A Glance
The main difference between semantic technology and other data technologies, such because interpersonal databases, deals with the sense rather than the data structure.
The World Wide Web Consortium’s Semantic Web Initiative states. The goal of this technology in the background of the Semantic Web is to create a “universal medium for data exchange” by seamlessly connecting the worldwide exchange of any personal information, scientific and cultural data.
The W3C has developed open specifications for developers of semantic technologies to follow. And through open source development. And it has identified the infrastructure elements needed to scale on the Web and be applicable elsewhere.
The primary standards on which Semantic Technology is based are RDF (Resource Description Framework), SPARQL (SPARQL Protocol and RDF Query Language) and possibly OWL (Web Ontology Language).
(optional) OWL is a computer logic-based language designed to show the schema of data. Because It represents a rich and complex knowledge of the hierarchies of things and their relationships.
It complements RDF and allows a data schema/ontology to be formalized in a given domain, independent of the data.
By formalizing meaning independent of data and using W3C standards. Sem technology enables machines to “understand”,, share and reason with data to create more value for us humans.
Industrial Application Of Sem Technology
Semantic technology helps companies discover more brilliant data, infer relationships, and extract insights. From large sets of raw data in multiple formats and various sources. For example, semantic graph databases (based on the Semantic Web view) such because Ontotext’s GraphDB make it easier for machines to integrate. And process and retrieve data. So this in turn, enables organizations to access meaningful and accurate data, analyze that data. And turn it into insights faster and more cost-effectively. Additionally, they can use this knowledge to generate business insights, apply predictive models, and make data-driven decisions.
Several companies are already using semantic technology and semantic graph databases. And manage their content, reuse and reuse information, reduce costs and generate new revenue streams.
In media and publishing, BBC, FT, SpringerNature and many others use semantic publishing to make data integration and knowledge discovery more efficient;
Astra Zeneca and other big pharma companies use technology to test early hypotheses, monitor adverse events. And analyze patient records, and more in the healthcare and life sciences sectors.
Many companies have started using technology to semantically enrich content and process complex and heterogeneous data in the finance and insurance industry.
In e-commerce, automotive, government and public sector, technology providers, energy, service industries and many others use technology courses to extract data and assign meaning to different sets of information.