A Knowledge Graph is a close-to-reality model that aims to represent existing entities and relationships, serving as a central knowledge platform. Knowledge Graphs, fueled by machine learning, utilize natural language processing (NLP) to construct a comprehensive view of nodes, edges, and labels through semantic enrichment. This process allows knowledge graphs to identify individual objects and understand their relationships when data is ingested. This working knowledge is then compared and integrated with other datasets, which are relevant and similar. Once a knowledge graph is complete, it allows question answering and search systems to retrieve and reuse comprehensive answers to given queries.
While consumer-facing products demonstrate its ability to save time, Knowledge Graphs can also be applied in a business setting, eliminating manual data collection to support business decision-making.
Knowledge Graphs are not new; they’ve been generating impact for over a decade across several types of businesses, including Google, which uses its Knowledge Graph, a database of billions of facts about people, places, and things, to populate search results. Another example is the Amazon Product Graph used to categorize and showcase products to customers as they search.
In this video, Wizeline Technology Director Benjamin Gil describes how various organizations use Knowledge Graphs as the leading approach for hyper-personalization, compliance risk & fraud detection, real-time recommendation, and supply chain and logistics analysis.
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