Trends
AI: Helper of knowledge graph construction
A knowledge graph, also known as a semantic network, represents a network of real-world entities such as objects, events or concepts.
Headline
A knowledge graph, also known as a semantic network, represents a network of real-world entities such as objects, events or concepts.
Context
Businesses are increasingly using AI applications to make decisions. However, AI systems have not yet been able to reach their full potential as reliable solutions to complex problems. Neither AI nor the knowledge graph are new technologies until recently when they have matured and joined forces. While data and computing power have fueled their rise over the past decade, it is the powerful combination of the two that has sparked interest in contextual AI. A knowledge graph , also known as a semantic network, represents a network of real-world entities—such as objects, events, situations or concepts—and illustrates the relationship between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term knowledge “graph.”
Evidence
Pending intelligence enrichment.
Analysis
A knowledge graph is made up of three main components: nodes, edges, and labels. Any object, place, or person can be a node. It is also worth noting that definitions of knowledge graphs vary, and there are studies that show that knowledge graphs are no different from knowledge bases or ontologies . Also read: Shanghai Binling combines AI with game development and education Data is everywhere. AI has become invaluable in storing and organizing vast amounts of information – using the “knowledge graph.” The Knowledge graph is a database that allows an AI system to process complex, interconnected data. It stores information as a network of data points connected by different types of relationships. The knowledge graph powers Internet search, recommendation systems, and chatbots. Over the past decade, deep learning and encoder-decoder transformer architectures have fundamentally changed the field of artificial intelligence, dramatically improving knowledge sensing techniques. Neural networks can now use network scale data to learn language models in a completely unsupervised manner, storing vast amounts of background knowledge. Most data in an enterprise usually exists in the form of text documents. Therefore, building a knowledge graph based on this data requires customized information extraction (IE) analysis for entity identification and relationship extraction. This process is also known as knowledge base filling (KBP), and one of its tasks is to fill slots.
Key Points
- A knowledge graph, also known as a semantic network, represents a network of real-world entities such as objects, events, situations or concepts.
- A knowledge graph is a database that allows AI systems to deal with complex, interrelated data.
Actions
Pending intelligence enrichment.
