Knowledge Graph
Knowledge Graph do , is a knowledge base of Google 's search system that aims to improve the results of its search with semantic search information .
With a graph, the model created to handle data can be as large, complex, and deep as you need it to be, because it deals with connections between complex and high-quality data, allowing the use of sparse and incomplete data, making them usable.
A knowledge graph represents a collection of interconnected descriptions of entities – real-world objects and events, or abstract concepts (e.g., documents) – where these have semantic that allow people and computers to process them efficiently and unambiguously;
The descriptions of entities contribute to one another, forming a network where each entity represents part of the description of the entities related to it, providing context for their interpretation.
Google Knowledge Graph
The Google Knowledge Graph is a knowledge base used by Google and its services to improve search engine results with information gathered from a variety of sources. This information is presented to users in an infobox alongside search results.
These information boxes were added to Google's search engine in May 2012, starting in the United States, with international expansion planned for the end of the year.
Google has referred to these infoboxes , which appear to the right (top on mobile) of search results, as "knowledge panels".
The information covered by Google's knowledge graph grew rapidly after its launch, tripling in size within seven months (covering 570 million entities and 18 billion facts).
In mid-2016, Google reported that it contained 70 billion facts and answered “approximately one-third” of the 100 billion monthly searches they handled. By May 2020, this had grown to 500 billion facts across 5 billion entities.
Knowledge graphs combine features from several data management paradigms:
- Database , because the data can be explored through structured queries;
- Graphs , because they can be analyzed like any other network data structure;
- Knowledge base , because they have formal semantics, which can be used to interpret the data and infer new facts.
Knowledge graphs, as represented in RDF , provide the best framework for data integration, unification, linking, and reuse because they combine:
- Expressiveness : the standards in the Semantic Web – RDF and OWL – allow for a fluent representation of various types of data and content: data schemas, taxonomies and vocabularies, all types of metadata, reference and master data. The RDF extension makes it easy to model provenance and other structured metadata.
- Performance : All specifications have been carefully considered and proven in practice to allow for efficient management of charts containing billions of facts and properties.
- Interoperability : There is a range of specifications for data serialization, access (SPARQL protocol for endpoints), management (SPARQL Graph Store), and federation. The use of globally unique identifiers facilitates data integration and publication.
- Standardization : Everything is standardized through the W3C to ensure that the requirements of different stakeholders are met.
The extensive use of knowledge graphs allows questions asked by all types of users to be mapped onto an organized set of information that can provide the answers we want.
In summary, knowledge graphs use semantic patterns to describe the structure of information to support reasoning and inferences.
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