A knowledge graph is a structured representation of the relationships between different domains (entities). It goes beyond traditional databases to provide a semantic framework for information. In a knowledge graph, information is organized as a graph, where nodes (nodes) and edges are different elements.
Measurement of a knowledge graph
The image of knowledge graphs has grown significantly in today’s era because they provide context (context) and meaning to data. By organizing information in a graph structure, knowledge graphs help to present knowledge in a more comprehensive and connected way. They can be viewed as hidden patterns, new relationships can be imagined, and insights that are not usually found in databases can be obtained.
Development and evolution of knowledge graphs
The concept of a knowledge graph has evolved, with knowledge decisions being made on it. Its roots lie in symbolic artificial intelligence (Symbolic AI) and knowledge-based systems, where attempts are made to encapsulate human knowledge in a rule-based system. Later, semantic networks and frames emerged, which represent the relationships between concepts in the form of graphs.
How does a knowledge graph work?
A knowledge graph is a way of organizing and making information understandable to machines. It is used in various applications, such as search engines, question-and-answer systems, and others. The way it works consists of the following steps:
1. Data integration
A knowledge graph collects various digital data, such as structured databases, unstructured text, and real-time feeds. This data is transformed into a standardized format so that we can manipulate it.
2. Entity extraction
It identifies the entities (entities) in the data. These include people, places, sequences, events, or other information. For example, people, places, or topics mentioned in a news story.
3. Relationship identification (relationship extraction)
Once entities have been identified, it is possible to find relationships between them. These tell us how different investments have been made in you. For example, relationships about a person might include their employment history, social connections, or affiliation with an organization.
4. Graph structuring
The identified entities and their relationships are organized as a graph, where entities appear at nodes and as edges. This structure helps to present information in an organized and efficient way.
5. Knowledge representation
A knowledge graph uses a semantic language or ontology to provide a standardized description of the entities and their relationships. Ontologies describe the development of the state, their relationships, and their properties so that they can be understood as taxonomies in different applications.
6. Queries and Reasoning (Query and Reasoning)
The knowledge graph can be used to query using different languages such as SPARQL or GraphQL. These queries help to find specific information, search for what is being searched, or reason for the given data.
7. Continuous Update (Continuous Update)
The knowledge graph is dynamic, meaning that new data can be added over time and old information can be updated. In this way, the knowledge graph always provides the latest and best information.
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Structure and Components of a Knowledge Graph
1. Nodes and Entities (Nodes and Entities)
Nodes are the basic components of a knowledge graph that are publicly known to be people, places, concepts, or any meaningful information. Each node is a unique identifier and falls into a specific category.
2. Relationships and Relations (Edges and Relations)
Rings represent the relationships between nodes, which is the basic element of a knowledge graph. For example, in a film knowledge graph, the relationships “Acted by”, “Directed by”, “Produced by” or the like are basic.
Conclusion
A knowledge graph is a structured way of organizing and representing information by capturing relationships between different entities. Unlike traditional databases, it provides a semantic framework that enhances data connectivity, enabling better insights and analysis. Knowledge graphs play a crucial role in AI, search engines, and various data-driven applications by integrating diverse information sources, extracting meaningful relationships, and facilitating intelligent querying.