How knowledge graphs are the answer to better decisions for your business
Author: Sanjeev Kumar | Senior Data Scientist | AI @scale, Machine vision & conversational AI
Knowledge graphs are increasingly powering AI applications today. Yet, for scalable implementations and solving enterprise data integration challenges, leaders must take an agile approach to knowledge graph development. According to Gartner 1 , by 2024, enterprises using knowledge graphs and semantic approaches will have 75% less AI technical debt than those that don’t. Knowledge graphs can solve multiple data integration challenges that are still barriers to AI adoption related to data complexity, quality, and accessibility. With knowledge graphs, there is a fluid data environment using uniform identifiers, flexible schemas, and triples instead of tables. However, there are varied perspectives and interpretations of what a knowledge graph constitutes. Data management focuses on the creation, use, and representation of metadata. Knowledge engineers see knowledge graphs as a concept of domain understanding, and business users look towards the hidden insights that can be surfaced by utilizing specific links and data relationships.
Taking advantage of the potential collective intelligence, knowledge graphs are being used by:
Pharma & Life Sciences: For drug discovery Financial Services: To detect frauds and better investment decisions Manufacturing & Electronics: To ward off risks and improve investment analytics Manufacturing: To optimize production lines and supply chains Search & Chatbots: For recommendations and question answering Knowledge graphs give businesses a bird’s eye-view of the entire data, allowing them to capture interesting insights and establish relationships between entities involved, which would have otherwise been difficult to envision before.
Let’s find out how knowledge graphs are the answer to better decisions for your business.
Using knowledge graphs
A knowledge graph is a semantic network of three components — nodes, edges, and labels. Nodes are represented by anything — people, places, objects. The nodes are connected by edges which signify a relationship between the nodes. We assign a label to the edge which defines the meaning of relationships. These labels are part of the ontology that drives a knowledge graph’s schema. Businesses use knowledge graphs to link and integrate data (most of which exist in silos) and form multiple interconnections. The data from various sources — complex or simple, structured or unstructured — get organized.
1 https://www.gartner.com/en/documents/3985680
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