Knowledge Graphs in Knowledge & Data Management

Knowledge is more than just information entered into a database. It is an intricate, dynamic model that places each item of data into a wider context, creates a world around it, and displays its relationships and significance in a particular environment.

However, a typical database may only hold a small portion of the information of a corporation. Unstructured records, photos, and people's ideas all contain a wealth of important data and knowledge that is not found in a database. 

Hence, companies are searching for intelligent solutions that can help in the arrangement of their data so that it may be placed in the proper context and no information is lost, allowing them to go beyond typical data, capture knowledge, and scale at a lightning speed. The solution is Knowledge Graphs. 

Knowledge Graphs (KG) are attracting the interest of businesses from a variety of industries because this technology applies semantics to give context and relationships to data. The knowledge is extracted using natural language processing and computer vision, AI capabilities from different domains, data models, and heterogeneous data formats. This knowledge is then represented graphically without altering its original form.

This technology enables businesses to utilize the potential of group intelligence. By design, an enterprise knowledge graph will make it simple to integrate all of a company's knowledge sources, enabling its AI technologies to go beyond traditional machine learning.

From early data discovery to flexible analytics, KGs provide an additional context dimension and enable corporations to ask business questions and receive valuable responses from the business world. It provides firms with fresh perspectives on their industries, enabling them to develop new products and enhance R&D.