Glossary - Technical Terms and Jargon explained

In the context of mapping, dictionaries are a common pattern for deciding the class of an entity depending on the values in a database column. The support built into Ontopic Studio for this pattern simplifies the use of the pattern reducing mistypings and misalignments between values and classes.

A node in the Knowledge Graph. An entity is typically identified by an IRI.

A column (or collection of columns) that refers to the primary key (non-nullable uniquely identified values) in another table. It acts as a cross-reference between tables. A foreign key always must refer to a primary key somewhere.

The Internationalized Resource Identifier identifies a logical or physical resource used by web technologies. Example: http://data.mycompany.com/employee/1234.

In Ontopic Studio, a lens is a logical view over the original database table, which allows to visualize and edit the content without altering the original data stored in the data source.

In the case of a relational data source, a (schema) mapping connects tables and columns from the source to the components of the semantic layer, which for a Knowledge Graph are classes and properties. A common standard language for mapping relational databases to RDF graphs is R2RML, but other languages exist. A mapping is composed of single mapping entries.

A mapping entry is the detailed rule descrbing how to create a particular part of the Knowledge Graph from a well defined part of the data source.

The creation of RDF triples in the Knowledge Graph. In this context, it is typically data from relational databases, from CSV files or spreadsheet files being transformed to RDF triples and stored in specialized graph databases, also called triple stores.

Ontop can build Virtual Knowledge Graphs as described by R2RML mappings or in the custom OBDA file format. Both files formatsare equivalent, as far as expressivity is concerned. OBDA file format is preferred by many practitioners, being perceived as easier to read.

Ontop translates queries from SPARQL, the query language used for RDF graphs, into SQL, the query language for traditional databases. Data from relational (traditional) databases appears therefore as a Knowlegde Graph, but without the need to copy and transform data from a relational database into a specialized graph database. This is then called a virtual knowledge graph. Ontop also allows to transform batch-wise whole database tables, as well as entire databases, to RDF graphs. This modality is then called materialization. Ontop is Open Source software and is been developed by several universities and companies. Ontopic provides commercial support and services for Ontop.

In the context of Knowledge Graphs, an ontology defines classes and properties, as well the relationships between them and is used to provide a struture to data. Ontologies can also be used for automatic reasoning.

Mappings from relational databases to RDF datasets can be expressed in terms of the R2RML mapping language. R2RML is a W3C standard.

SPARQL is a language for querying RDF datasets. It is deliberately similar to SQL, but adapted to query graphs, whereas SQL is designed for querzing relational databases. SPARQL is a W3C standard.

A SPARQL endpoint is characterized by a process, typically associated with a database, able to understand SPARQL queries, either returning the requested data or modyfying the data, as specified in the query. SPARQL endpoints use the HTTP protocol for communication and each endpoint has a defined URL.

A template is used to generate subject IRIs for entities. Example: http://data.mycompany.com/employee/{empno}, where {empno} will be substituted with a unique key in order to create unique IRIs.

SPARQL query answering does not involve any materialization in RDF. Instead SPARQL queries are translated to SQL queries, which are then executed over the data sources.One of the most used and advanced Virtual Knowledge Graph engines is Ontop.