Knowledge Graph

Structure of a Knowledge Graph

A knowledge graph consists of a network of real-world entities (i.e., objects, situations, events, or concepts) and illustrates the relationship between them. Those entities can, in addition to other options, also be visualised in a graph structure, hence the name knowledge “graph”. 

Typically, a knowledge graph is created from various datasets from different sources, varying in structure. Through using ontologies as a schema layer, the knowledge graph allows for logical interference for retrieving implicit knowledge rather than only requesting explicit knowledge via queries. Therefore, the knowledge graph provides unified structure to diverse data, contextualizes, as well as links it and gives a framework for data integration, unification, analytics and sharing. 

Use of Knowledge Graphs

Knowledge graphs are prominently associated with search engines and other question-answering services such as the Google Search Engine or Amazon’s Alexa. The importance of knowledge graphs here lies with the ability to distinguish words with multiple meanings, for example the difference between apple, the fruit, and apple, the tech brand. Therefore, by using a knowledge graph, search engines are able to give more context than traditional results. 

brox uses knowledge graphs primarily for use cases surrounding data integration. Knowledge graphs are very useful in this domain because they are very flexible but still allow the usage of powerful query languages such as SPARQL, schema definitions such as SHACL as well as disambiguation. These properties can speed up data integration processes significantly and will help you reduce costs and achieve more in data integration projects. 

• Pharma Semantic Search
• Generating Lead Information