Workspace Index

Upon opening a workspace, Mentor automatically builds a index of all RDF terms used across the files in your workspace. This index enables features like code lenses and the “Find Usages” and “Go to Definition” commands, allowing you to quickly navigate and explore your knowledge graph project.

The index is kept entirely in memory and will be rebuilt each time you open the workspace. When files have changed, are added or removed the index is updated incrementally to reflect these changes.

In addition to the term maps, Mentor also maintains an in-memory triple store containing all RDF triples found in the workspace. This allows you to run SPARQL queries against all of your files in the workspace directly from within Visual Studio Code.

Indexed Data

Mentor keeps track of the following RDF related data in your workspace:

Data	Description
References	A mapping of IRIs to the files and line numbers where they are used, either as a subject or an object of a triple.
Definitions	A mapping of IRIs to the files and line numbers where they are defined as a subject with an rdf:type predicate.
Prefixes	A mapping of prefixes to their corresponding namespace IRIs, along with the files where they are declared.
Triples	An in-memory representation of all RDF triples found in the workspace, allowing for querying the data with SPARQL.

Indexing Status

The indexing process runs in the background and may take a few moments to complete, depending on the size of your workspace. You can monitor the indexing status in the Mentor status bar at the bottom of the Visual Studio Code window:

Generated Graphs

When indexing RDF files then the contents of all files are stored in an internal in-memory triple store.

Framework Ontologies

Mentor automatically loads a set of built-in RDF ontologies into the triple store to provide foundational definitions and relationships for common terminology. These ontologies include:

Ontology	Graph IRI
rdf	http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfa	http://www.w3.org/ns/rdfa#
rdfs	http://www.w3.org/2000/01/rdf-schema#
owl	http://www.w3.org/2002/07/owl#
shacl	http://www.w3.org/ns/shacl#
skos	http://www.w3.org/2004/02/skos/core#
skos-xl	http://www.w3.org/2008/05/skos-xl#
xsd	http://www.w3.org/2001/XMLSchema#
dcat	http://www.w3.org/ns/dcat#

The IRIs of these ontologies are used as named graphs in the triple store. You can query these graphs directly using SPARQL to access the definitions and relationships they provide.

Documents

For every RDF document in your workspace, Mentor creates an internal named graph that contains all triples found in that document. The graph IRI is derived from the document’s URI in the workspace. For example, for a document located at /documents/example.ttl, the corresponding graph IRI would be:

workspace:/documents/example.ttl

Note

The workspace: scheme is custom to Mentor and is always resolved relative to the current workspace root.

It ensures that SPARQL queries are workspace-independent and can refer to documents in the workspace without using absolute file paths that may vary between different systems. This is important to enable version control and collaboration on knowledge graph projects.

For this reason the scheme does not allow for relative paths as they could refer to files outside of the workspace.

The file: IRI scheme is not used here because it does not support relative paths and thus cannot provide workspace-independent references.

Inference

To ensure that trees correctly displays all kinds of classes, such as OWL restrictions or equivalent classes, Mentor applies structural reasoning when parsing RDF documents. This means that a reasoning engine infers additional class, subclass relationships and property types based on the OWL and SHACL semantics.

Note

Structural reasoning applied by Mentor is optimized for performance and does not conform to any official OWL profile. It is solely intended to simplify the internal implementation of features such as the Definitions Tree.

For standards conformant reasoning, consider using a dedicated OWL reasoner such as OWL-RL.

The results of the reasoning process are stored in a separate graph that follows the same scheme as the document graph but appends ?inference to the graph IRI. For example:

workspace:/documents/example.ttl?inference

If the data graph IRI already contains query parameters, then the inference parameter is simply appended to the existing ones.

Memory Usage

The memory consumption depends on the size of your workspace and the number of RDF terms used. For large workspaces, this may lead to increased memory usage. You can monitor the memory consumption of Mentor in the Visual Studio Code Process Explorer.

Tip

To open the Process Explorer, go to the Command Palette (Ctrl+Shift+P), and type “Process Explorer”, then select “Open Process Explorer”.

Caution

Please be aware that very large workspaces may lead to out-of-memory errors. If you encounter such issues, consider excluding certain files or folders from being indexed using the settings described below.

Settings

To avoid long indexing times or out-of-memory errors in large workspaces, you can configure Mentor to exclude certain files or folders from being indexed. The following settings are available:

mentor.index.ignoreFolders An array of folder paths relative to the workspace root. Any folders specified in these ignore files will be excluded from the indexing process.

mentor.index.maxFileSize You can exclude large files from being indexed by setting a maximum file size limit in the Mentor settings. Files exceeding this size which is specified in bytes will be skipped during the indexing process.

mentor.index.useGitIgnore Enable or disable the use of .gitignore files for excluding files from the indexing process. When enabled, Mentor will respect the rules defined in your .gitignore files. The default value is true.