Release Notes DISQOVER 6.01

Release Notes DISQOVER 6.01

29 September 2020 | version 6.01

This document contains the release notes of the DISQOVER version 6.01 and instructions to upgrade. Please make sure you have read these before updating or installing this new release.

1 Introduction

The strategic vision for DISQOVER 6.01 product development relies on 6 pillars: Superior UI/UX, Interactive Search, Links & Visual Analytics, Highly Customizable, High quality Public Data Integration, Data Ingestion Pipelines and Plugin Architecture.

Superior UI/UX is key to DISQOVER. We aim to provide a general purpose tool with an expert layer that is attractive and engaging, interactive and near-real-time. A continuously improving user interface and more intuitive data integration further contributes to a seamless user experience and a growing number of solvable use cases.

By enabling interactive search, links & visual analytics DISQOVER facilitates interactive data journeys that rely on semantic and linked data concepts, but shield complexity. Views and navigational journeys are highly customizable to the end user’s expectations and use cases. With the self-service customization from the UI, users can prepare views for their peers.

We envision DISQOVER not as a standalone tool, but rather as a part of a customer’s solutions ecosystem. The plugin architecture enables customers the possibility to extend the product with custom functionality, possibly integrating 3rd party tools.

To allow users a flexible integration with proprietary data, DISQOVER offers a comprehensive integrated and linked data set for high quality public data integration. With a data ingestion pipeline, we enable self-service data ingestion with provenance attribution, focus on advanced QC and the possibility of multi-tier data architecture.


  • Improvements for configuring the layout of a Search Page.
  • A new feature enables to select the destination Canonical Types when following semantic links to instances that belong to multiple Canonical Types.
  • Improvement in the usability of the Remote Data Synchronization (RDS) services:
    • added flexibility to select and customize specific predicates during import;
    • a scanner to detect predicates during import and to autosuggest component options;
    • clear display of metadata of available RDS data sets on an RDS publisher server;
    • status and progress indicator during RDS data retrieval.
  • New Python API wrapper library is available

2 Key Features

2.1 Search Page Layout Configuration

Usability improvements are available for users with elevated rights to configure search page templates. Firstly, more flexibility is added to resize widgets. Vertical resizing is now possible when switching off automatic height adjustment

Figure 1: Search page with predefined links to find domain experts via different routes. On the top right is a button to direct to another search page.

Figure 1: New option to toggle on or off automatic height adjustment.

Next, text formatting using markdown syntax is enabled for static text widgets.
Finally, the widget to organize content vertically, the vertical group widget, is now collapsible.

2.2 Semantic Links

Instances may belong to multiple Canonical Types (e.g. a molecule can be part of ‘Chemical’ and ‘Active Substance’). When clicking on a link to go to an instance that belongs to multiple Canonical Types, a popup will list all relevant Canonical Types and enables to actively go to a specific Canonical Type.

2.3 Remote Data Subscription

The features of the RDS services are extended with more flexibility during import and with improved usability in general. These features are only available for users with elevated configuration and data administrator rights.

When using an RDS import component in the Data Ingestion Engine, it is possible to specify which predicates to import and to customize their names. In case this option is not used, the former default behavior remains, all predicates will be imported, and a deprecation warning is shown. In addition, the RDS import component is now equipped with a scanner to detect available predicates and to autosuggest component options.

The RDS configuration page is extended and displays now the catalog of available Remote Data Sets on an RDS publisher server. The metadata retrieved from an RDS publisher server is:

  • unique ID of the Data Set on the server;
  • name of the Data Set;
  • number of resources (pieces of information);
  • list of available predicates;
  • list of original data sources contributing;
  • publication date.

Also, the status and progress of an RDS data retrieval process, is now visually supported and displays:

  • the status of an ongoing retrieval (if applicable);
  • the date of the last retrieval;
  • publication date of the data set.

2.4 Python API Wrapper Library

A new Python API wrapper is available and provides a Doman Specific Language (DSL) that encapsulates the concepts provided in DISQOVER in a uniform manner. The library can be installed via the following PIP command:

pip install disqover-api --extra-index-url=

A detailed documentation is available via ONTOFORCE support at

3 Upgrade Instructions

Upgrading DISQOVER from version 6.00.x to version 6.01 can be achieved by updating the disqover-installer package through yum or apt, and running the disqover-update command.

To upgrade from an earlier version, you potentially need to run additional commands or to upgrade first to version 5.20.x. DISQOVER deployments configured with an external PostgreSQL host or with custom Docker containers, are required to follow additional guidelines.  More details are available in Chapter 3 of the DISQOVER 6.01 System Administrator Manual or via ONTOFORCE support at

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Try the free Community Edition or upgrade to DISQOVER 6.10 Enterprise

Experience the DISQOVER 6.10 Community Edition right now:

  • Create a free account
  • Enjoy unlimited action to public data
  • Access ~150 data sources
  • Create your own dashboards and share them with peers

Contact us to unlock the full DISQOVER experience with the ability to link internal and third-party data sources to create a truly data ecosystem. 

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