Knowledge Graphs

Knowledge Graphs are a type of data structure that is used to store and organize information. In the text analytics industry, Knowledge Graphs are used to store and organize information about entities and their relationships.

In addition to storing information about entities and their relationships, Knowledge Graphs can also be used to store additional information about entities, such as their properties and attributes.

Knowledge Graphs are similar to other data structures, such as ontologies and semantic networks. However, Knowledge Graphs are typically more flexible and easier to use than ontologies and semantic networks.

One of the main benefits of using Knowledge Graphs is that they can be used to improve the accuracy of text analytics algorithms. For example, if an entity is mention in a text document, but the algorithm does not know anything about that entity, then the algorithm will not be able to accurately analyze the document. However, if the entity is stored in a Knowledge Graph, then the algorithm can use the information in the Knowledge Graph to better understand the document.

There are many different ways to store and organize information in a Knowledge Graph. One common approach is to use a graph database, such as Neo4j. Another approach is to use a triple store, such as Apache Jena.

Why Google Uses Knowledge Graphs

Google uses Knowledge Graphs to improve the accuracy of its search results. For example, when you search for “Barack Obama,” Google will use the information in its Knowledge Graph to provide you with relevant information about Barack Obama, such as his spouse, his children, and his political party.

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