Tag Archives: database

Ontotext GraphDB on DBpedia

DBpedia Member Features – In the coming weeks we will give DBpedia members the chance to present special products, tools and applications and share them with the community. We will publish several posts in which DBpedia members provide unique insights. Ontotext will start with the GraphDB database. Have fun while reading!

 by Milen Yankulov from Ontotext

GraphDB is a family of highly efficient, robust, and scalable RDF databases. It streamlines the load and use of linked data cloud datasets, as well as your own resources. For easy use and compatibility with the industry standards, GraphDB implements the RDF4J framework interfaces, the W3C SPARQL Protocol specification, and supports all RDF serialization formats. The database offers open source API and it is the preferred choice of both small independent developers and big enterprise organizations because of its community and commercial support, as well as excellent enterprise features such as cluster support and integration with external high-performance search applications – Lucene, Solr, and Elasticsearch. GraphDB is build 100% on Java in order to be OS Platform independent.

GraphDB is one of the few triplestores that can perform semantic inferencing at scale, allowing users to derive new semantic facts from existing facts. It handles massive loads, queries, and inferencing in real-time.

GDB Architecture

GraphDB Workbench

Workbench is the GraphDB web-based administration tool. The user interface is similar to the RDF4J Workbench Web Application, but with more functionality.

GraphDB Engine

The GraphDB Workbench REST API can be used for managing locations and repositories programmatically, as well as managing a GraphDB cluster.  It includes connecting to remote GraphDB instances (locations), activating a location, and different ways for creating a repository.

It includes also connecting workers to masters, connecting masters to each other, as well monitoring the state of a cluster.

GraphQL access via Ontotext Platform 3

GraphDB enables Knowledge Graph access and updates via GraphQL. GraphDB is extended to support the efficient processing of GraphQL queries and mutations to avoid the N+1 translation of nested objects to SPARQL queries.

Ontotext offers three editions of GraphDB: Free, Standard, and Enterprise.

Free – commercial, file-based, sameAs & query optimizations, scales to tens of billions of RDF statements on a single server with a limit of two concurrent queries.

Standard Edition (SE) – commercial, file-based, sameAs & query optimizations, scales to tens of billions of RDF statements on a single server and an unlimited number of concurrent queries.

Enterprise Edition (EE) – high-availability cluster with worker and master database implementation for resilience and high-performance parallel query answering.

Why GraphDB is preferred choice of many data architects and data ops?

3 Reasons:

1. High Availability Cluster Architecture

GraphDB offers you a high-performance cluster proven to scale in production environments. It supports 

  • (1) coordinating all read and write operations, 
  • (2) ensuring that all worker nodes are synchronized,
  • (3) propagating updates (insert and delete tasks) across all workers and checking updates for inconsistencies, 
  • (4) load balancing read requests between all available worker nodes

Improved resilience

failover, dynamic configuration

Improved query bandwidth

larger cluster means more queries per unit time

Deployable across multiple data centres

Elastic scaling in cloud environments

Integration with search engines

Cluster Management and Monitoring

It supports

(1) automatic cluster reconfiguration in the event of failure of one or more worker nodes, 

(2) a smart client supporting multiple endpoints.

2. Easy Setup

GraphDB is 100% Java based in order to be Platform Independent. It is available through Native Installation Packages or Open Maven. It supports also Puppet and could be Dockerized. GraphDB is Cloud agnostic – It could be deployd on AWS, Azure, Google Cloud, etc.

3. Support

Based on the Edition you are using you could use the Community Support (StackOverFlow monitoring)

Ontotext has its Dedicated Support Team tha could assist through Customized Runbooks, Easy Slack communication, Jira Issue-Tracking System 

A big thank you to Ontotext for providing some insights into their product and database.


DBpedia Association

Retrospective: GSoC 2018

With all the beta-testing, the evaluations of the community survey part I and part II and the preparations for the Semantics 2018 we lost almost sight of telling you about the final results of GSoC 2018. Following we present you a short recap of this year’s students and projects that made it to the finishing line of GSoC 2018.


Et Voilà

We started out with six students that committed to GSoC projects. However, in the course of the summer, some dropped out or did not pass the midterm evaluation. In the end, we had three finalists that made it through the program.

Meet Bharat Suri

… who worked on “Complex Embeddings for OOV Entities”. The aim of this project was to enhance the DBpedia Knowledge Base by enabling the model to learn from the corpus and generate embeddings for different entities, such as classes, instances and properties.  His code is available in his GitHub repository. Tommaso Soru, Thiago Galery and Peng Xu supported Bharat throughout the summer as his DBpedia mentors.

Meet Victor Fernandez

.. who worked on a “Web application to detect incorrect mappings across DBpedia’s in different languages”. The aim of his project was to create a web application and API to aid in automatically detecting inaccurate DBpedia mappings. The mappings for each language are often not aligned, causing inconsistencies in the quality of the RDF generated. The final code of this project is available in Victor’s repository on GitHub. He was mentored by Mariano Rico and Nandana Mihindukulasooriya.

Meet Aman Mehta

.. whose project aimed at building a model which allows users to query DBpedia directly using natural language without the need to have any previous experience in SPARQL. His task was to train a Sequence-2-Sequence Neural Network model to translate any Natural Language Query (NLQ) into the corresponding sentence encoding SPARQL query. See the results of this project in Aman’s GitHub repositoryTommaso Soru and Ricardo Usbeck were his DBpedia mentors during the summer.

Finally, these projects will contribute to an overall development of DBpedia. We are very satisfied with the contributions and results our students produced.  Furthermore, we like to genuinely thank all students and mentors for their effort. We hope to be in touch and see a few faces again next year.

A special thanks goes out to all mentors and students whose projects did not make it through.

GSoC Mentor Summit

Now it is the mentors’ turn to take part in this year GSoC mentor summit, October 12th till 14th. This year, Mariano Rico and Thiago Galery will represent DBpedia at the event. Their task is to engage in a vital discussion about this years program, about lessons learned, highlights and drawbacks they experienced during the summer. Hopefully, they return with new ideas from the exchange with mentors from other open source projects. In turn, we hope to improve our part of the program for students and mentors.

Sit tight, follow us on Twitter and we will update you about the event soon.

Yours DBpedia Association