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Semantic Architecture

What is Semantic Architecture, and How to Build One?

If you can get access to a major chunk of your company’s data with a basic search through common business terms, then your company may be the lucky one leveraging a semantic data layer.

It allows organizations to capture, store, and present basic business terms and context as a layer setting over complex data. It’s why the semantic data layer is nicknamed ‘the brain’ or ‘the hub’ by most users.

semantic layer driven by intelligent OLAP technology delivers unrivaled performance on vast chunks of data. People need to understand what this architecture layer actually means before they know its architecture.

What is a Semantic Layer?

It is not a single application but the realization of a semantic methodology to tackle organizational problems by handling data so that it is ready to capture business meaning.

It also allows businesses to design the purpose for the end-user experience. At its roots, this layer consists of one or more of the given methodologies.

  • Ontology model – It outlines the kinds of things that are present in an enterprise domain and how they can be described with properties.
  • Enterprise knowledge graph – It makes use of the ontology to factor in actual data. It allows the demonstration of the knowledge domain of an enterprise so that both machines and humans can gauge it.

Thus, the semantic data layer incorporates these models to enable a company to map independent data sources into a unified data model.

The Process of Creating a Semantic Data Layer Architecture

For building a scalable layer, there are three basic steps that you should follow.

1. Defining and listing the organizational needs

When developing a semantic enterprise solution, properly-outlined use cases provide the critical questions that the semantic architecture will answer. It, in turn, gives a better knowledge of the stakeholders and users, defines the business value, and facilitates the definition of measurable success criteria.

2. Survey the relevant business data

Many enterprises possess a data architecture founded on data warehouses, relational databases, and an array of hybrid cloud systems and applications that aid analytics and data analysis abilities

In such enterprises, employing relevant unification processes and model mapping practices based on the enterprise’s use cases, staff skill-sets, and enterprise architecture capabilities will be an effective approach for data modeling and mapping from source systems.

3. Using semantic web standards for ensuring governance and interoperability

The most appropriate standards are listed below.

  • Deploying a data description framework to provide organizational context to the data. It facilitates natural language data meaning and a greater human understanding of the data.
  • Utilize standard methodologies for data sharing and management via core data representation formats.
  • Implementing an adaptable schema or logic to map knowledge, relationships, and hierarchies among your enterprise’s data,
  • A semantic query language to analyze and access the artificial intelligence system and the data natural language.

4. Make use of semantic technology

It includes graph management apps that function as middleware. They drive the storage and recovery of semantic data. In scaled implementations, the semantic data layer architecture consists of a graph database for keeping the knowledge and data ontology and a data cataloging tool for proper metadata governance.

5. Support for employee/customer-facing apps

The most feasible semantic architecture plugs in customers or employee-facing applications, like data visualization tools, enterprise search, and chatbots.

It allows the organization to leverage advanced AI capacities like knowledge and text extraction tools for natural language processing. Semantic layers optimally work when they get naturally integrated for bringing interoperability of a company’s information assets.

An effective semantic layer leverages the flexibility of cloud and on-premise data lakes for building the cubes. You cannot compare its performance with partial aggregation or in-memory solutions.

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