Schema and its Role in Modern Semantic Search
Schema.org took its name from the word “schema” as a good fit for its purposes to be a reference website that publishes documentation, guidelines, and code examples to using structured data mark-up on web pages. Its main objective is to standardize HTML tags to be used by webmasters for creating rich results about a certain topic of interest.
A group of volunteers and Data Scientists work to develop and maintain the schema.org site. It is useful for anyone seeking instruction to add content on websites that can be marked with the help of JSON-LD, HTML Microdata, and RDFa. The intent is to add structure to web content in order to be more easily recognized by the search engines involved. This standardized markup language makes work easier for webmasters and semantic search marketers, so they have less work to mark elements for multiple search engines.
On the machine learning level, it helps search engines to display more relevant search results through better marking. With the “Structured Data Testing Tool”, Google provides a web application with which HTML code fragments or entire Internet pages can be identified, evaluated, and validated for the use of cataloging structured data.
Schematic Elements
A database schema ( ) of a database system is its structure described in a formal language supported by the database management system (DBMS) and refers to the organization of data as a blueprint of how a database is constructed (divided into database tables in the case of Relational Databases). The formal definition of database schema is a set of formulas (sentences) called integrity constraints imposed on a database. These integrity constraints ensure compatibility between parts of the schema. All constraints are expressible in the same language. A database can be considered a structure in the realization of the database language. The states of a created conceptual schema are transformed into an explicit mapping, the database schema. This describes how real-world entities are modeled in the database. “A database schema specifies, based on the database administrator’s knowledge of possible applications, the facts that can enter the database, or those of interest to the possible end-users.”
The schema property for Author identifies the author of a content piece and may influence possible ratings.
The notion of a database schema plays the same role as the notion of theory in predicate calculus. A model of this “theory” closely corresponds to a database, which can be seen at any instant of time as a mathematical object. Thus a schema can contain formulas representing integrity constraints specifically for an application and the constraints specifically for a type of database, all expressed in the same database language. Using semantic triples in schema markup is a great help for knowledge graph building.
Schema is Used to Define Things
In a relational database, the schema defines the tables, fields, relationships, views, indexes, packages, procedures, functions, queues, triggers, types, sequences, materialized views, synonyms, database links, directories, XML schemas, and other elements. Schemas are generally stored in a data dictionary. Although a schema is defined in text database language, the term is often used to refer to a graphical depiction of the database structure. In other words, schema is the structure of the database that defines the objects in the database or dataset. In an Oracle Database system, the term “schema” has a slightly different connotation.
Knowledge graph schema
Enterprise level knowledge graphs are now prevalent on the Web and demonstrate their value for several tasks. knowledge graph schema management is the necessity to maintain a knowledge graph schema (which is often defined manually) that correctly represents the knowledge graph content. The heart of the knowledge graph is a schematic knowledge model: It is made up of of interlinked descriptions of concepts, entities, node relationships and events. A schematic knowledge model is built on the active acquisition of information from similar categories of data.