New technologies like ChatGPT are all the rage, as they aim to answer questions and provide information that makes our lives easier. Yet, the validity of the results generated has come under scrutiny and, as a result, much emphasis has been made on how organizations can get relevant and trustworthy data into the hands of users. Even with the vast amount of information available, achieving insights is challenging if the platforms used cannot make sense of the inquiry, understand the inferences of the question, identify where the information resides, and deliver the data required to answer the question.

Data fabrics, which Gartner defines as an emerging Data Management design for attaining flexible, reusable, and augmented data integration pipelines, services, and semantics, are helping to ensure data is accessible by business and technology users alike. Businesses are applying data fabrics to support both operational and analytics use cases delivered across multiple deployment and orchestration platforms and processes, but they need a variety of technologies and design concepts to be effective. They require a combination of active metadata, knowledge graphs, semantics, and machine learning to augment data integration design and delivery. Of these, adopting and establishing semantics and establishing semantic standards that create context and meaning (through knowledge graph implementations) are some of the most important and confusing parts of the puzzle and deserve some explanation.

Semantic Technology Defined

Semantic technology uses formal semantics to give meaning to the disparate and raw data that surrounds us. Semantic technology, together with Linked Data technology – as envisioned by the inventor of the World Wide Web, Sir Tim Berners-Lee – builds relationships between data in various formats and sources, from one string to another, helping build context and creating links out of these relationships. When used with formal semantics – which studies the logical aspects of meaning, such as sense, reference, implication, and logical form – the technology helps AI systems understand language and process information the way humans do, which allows them to store, manage, and retrieve information based on meaning and logical relationships.

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. As a result, these machines can process long strings of characters and index tons of data and then store, manage, and retrieve information based on meaning and logical relationships. More importantly, it helps show related facts instead of just matching words which helps enterprises infer relationships to discover smarter data, and extract knowledge from enormous sets of raw data in various formats and from various sources.

This is particularly important because, according to another Gartner report, the growing levels of data volume and distribution are making it hard for organizations to leverage their data assets efficiently and effectively. Data and analytics leaders need to consider a semantic approach to their enterprise data; otherwise, they will face an endless battle with data silos. The core difference between semantic technology and other data technologies, such as the relational database, is that it deals with the meaning rather than the structure of the data. The World Wide Web Consortium (W3C)’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. 

The W3C developed open specifications for semantic technology for developers and has identified, via open-source development, the infrastructure needed to scale in the Web and elsewhere and include:

• Resource Description Framework (RDF): The format semantic technology uses to store data on the Semantic Web or in a semantic graph database. 

• SPARQL (SPARQL Protocol and RDF Query Language): The semantic query language specifically designed to query data across various systems and databases, and to retrieve and process data stored in RDF format.

• Web Ontology Language (OWL): Used optionally, the computational logic-based language 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. 

Put simply, by formalizing meaning independently of data, semantic technology enables machines to “understand,” share, and reason with data to create more value for humans. 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 – 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, analyze that data, and turn it into knowledge that enables them to gain business insights, apply predictive models, and make data-driven decisions. As early as 2007, Sir Berners-Lee told Bloomberg, “Semantic technology isn’t inherently complex. The semantic technology language, at its heart, is very, very simple. It’s just about the relationships between things. Chances are the ‘relationships between things’ will help organizations manage data more efficient.”

Semantic Data Integration Defined

Semantic data integration is the process of combining data from disparate sources and consolidating it into meaningful and valuable information through the use of semantic technology. As organizations scale up in size, so does their data. Without the right data management strategy, intradepartmental and/or application-specific data silos quickly arise and hinder productivity and cooperation. Semantic data integration offers a solution that goes beyond standard enterprise application integration solutions by employing a data-centric architecture built upon a standardized model for data publishing and interchange, namely the RDF. 

In this framework, all of an organization’s heterogeneous data – be it structured, semi-structured, and/or unstructured – is expressed, stored, and accessed in the same way. As the data structure is expressed through the links within the data itself, it is not constrained to a structure imposed by the database and does not become obsolete with the evolution of the data. When changes in the data structure occur, they are reflected in the database through changes in the links within the data. In addition, and as the backbone of semantic technology, RDF enables the inference of new facts from the existing data as well as the enrichment of the available knowledge by accessing Linked Open Data (LOD) resources.

Semantic Data in Action: Achieving a 360-Degree View 

In a world where complete visibility, accurate analysis, and solving data complexity challenges dominate the business landscape, integrating disparate data into a synchronized 360-degree perspective is paramount. Much like ChatGPT, organizations today are looking for solutions that allow them to manage all of their data and make it consumable for decision-making and a variety of business use cases. 

Whether their database operates standalone or is integrated into a larger enterprise ecosystem like a data fabric, companies need a complete set of data integration tools that can perform complex tasks and are easy to use. The ability to easily import and transform heterogeneous data from multiple sources, integrate and interlink the data as RDF statements and merge two or more graph databases are all essential functions that support world-class semantic solutions.