What are graphene used for?

In computer science, graphene theory is used to represent and analyze relationships between objects. Graphene is therefore an important tool in network research.

For example, Facebook uses the social graph to analyze relationships between profiles. Google has long used the link graph to analyze and evaluate relationships between documents and websites. Social Media Marketing Lahore explain that the Knowledge Graph uses Google to map and analyze relationships between entities.

The development of the Knowledge Graph by Google seems closely related to the purchase of the semantic knowledge base Freebase connected. I also like to refer to Freebase as a playground through which Google was able to gain its first experiences with structured data.

In 2012, Google introduced the Knowledge Graph, which initially included i.a. was fed by the data collected in Freebase and Wikipedia. Today, Google also uses other sources to gather information about the entities.

The open project Freebase was finished in 2014 and transferred to the closed project Wikidata. For the representation of entities Box Google checks to see if a record in Wikidata or a page on Wikipedia is available.

What is Google's Knowledge Graph?

The Knowledge Graph is Google's semantic database. Social Media Marketing Lahore define the algorithm the entities are related to each other, provided with attributes and brought into thematic context or ontologies.

With the introduction of Hummingbird ranking algorithm Google in 2013 gave the official go-ahead for the construction of a semantic search engine. The idea behind it was to understand content of any format yourself and to give a suitable and high-quality answer to every search query. The basis is a new arrangement of data, which are now no longer hierarchical but network-like, ie in the form of graphs sorted.

The Google Knowledge Graph: From Keywords to Entities

Search engines need a basis for deciding how to determine the order in which the web pages should be displayed. Google's original ranking factors included keyword density (and later strategically positioning it) and PageRank, which was calculated based on the number of links pointing to a webpage. Later, the keyword density by more complex text analytical methods such as TF-IDF or WDF * IDF was replaced.

In addition to the links, the focus in search engine optimization is still on keywords. But in recent years you can see that Hummingbird is getting smarter by the use of machine learning. The rankings are no longer based exclusively on keywords that are used, but on topics and entities.

A basic concept is that Google does not sort websites into categories or index them using (individual) keywords, but rather wants to understand the pages in their entire context. Google wants to understand the ulterior motives of a page and I suspect that this is happening at the domain level. This means that it is no longer just a matter of putting individual URLs into particularly profitable keywords in the index, but that the environment or the entire domain in its entirety is to be understood as a source entity.

From the Entities Catalog to the Google Knowledge Graph

The knowledge graph is based on three levels:

Entities Catalog: stores all entities that have been identified over time.

Knowledge Repository: The entities are merged in a knowledge repository with the information or attributes from the various sources. The Knowledge Repository is primarily about the merging and storage of descriptions and the formation of semantic classes or groups in the form of entity types. Google's Knowledge Repository is currently the Knowledge Vault which is described already by Social Media Marketing Lahore.

What are the key elements of the Google Knowledge Graph?

The basic structure of graphene consists of so-called nodes and edges. With respect to the Knowledge Graph, the nodes are the entities and the edges describe the nature of the relationship between these entities. Entities are described by a name or name and various attributes or properties.

This structure allows to answer questions in which a topic or entity is sought which is not mentioned in the question.

In the following example, "Australia" and "Canberra" are the entities and the value "Capital" describes the nature of the relationship.

This graphic says nothing more than: "Canberra is the capital of Australia." So Google can give the right answer to the question: "Which city is the capital of Australia?". It does not matter if you explicitly ask or implicitly ask the question about the search term "capital Australia". The result is the same. These are the description of google knowledge graph and for more information check Social Media Marketing Lahore.