by Luke Feeney from TerminusDB

This post introduces TerminusDB as a member of the DBpedia Association – proudly supporting the important work of DBpedia. It will also show you how to use TerminusDB’s unique collaborative features to access DBpedia data.

TerminusDB – an Open Source Knowledge Graph

TerminusDB is an open-source knowledge graph database that provides reliable, private & efficient revision control & collaboration. If you want to collaborate with colleagues or build data-intensive applications, nothing will make you more productive.

TerminusDB provides the full suite of revision control features and TerminusHub allows users to manage access to databases and collaboratively work on shared resources.

• Flexible data storage, sharing, and versioning capabilities
• Collaboration for your team or integrated in your app
• Work locally then sync when you push your changes
• Easy querying, cleaning, and visualization
• Integrate powerful version control and collaboration for your enterprise and individual customers.

The TerminusDB project originated in Trinity College Dublin in Ireland in 2015. From its earliest origins, TerminusDB worked with DBpedia through the ALIGNED project, which was a research project funded by Horizon 2020 that focused on building quality-centric software for data management.

While working on this project and especially our work building the architecture behind Seshat: The Global History Databank, we needed a solution that could enable collaboration among a highly distributed team on a shared database whose primary function was the curation of high-quality datasets with a very rich structure. While the scale of data was not particularly large, the complexity was extremely high. Unfortunately, the linked-data and RDF toolchains was severely lacking – we evaluated several tools in an attempt to architect a solution; however, in the end we were forced to build an end-to-end ourselves.

Evolution of TerminusDB

In general, we think that computers are fantastic things because they allow you to leverage much more evidence when making decisions than would otherwise be possible. It is possible to write computer programs that automate the ingestion and analysis of unimaginably large quantities of data.

If the data is well chosen, it is almost always the case that computational analysis reveals new and surprising insights simply because it incorporates more evidence than could possibly be captured by a human brain. And because the universe is chaotic and there are combinatorial explosions of possibilities all over the place, evidence is always better than intuition when seeking insight.

As anybody who has grappled with computers and large quantities of data will know, it’s not as simple as that. Computers should be able to do most of this for us. It makes no sense that we are still writing the same simple and tedious data validation and transformation programs over and over ad infinitum. There must be a better way.

This is the problem that we set out to solve with TerminusDB. We identified two indispensable characteristics that were lacking in data management tools:

1. A rich and universally machine-interpretable modelling language. If we want computers to be able to transform data between different representations automatically, they need to be able to describe their data models to one another.
2. Effective revision control. Revision control technologies have been instrumental in turning software production from a craft to an engineering discipline because they make collaboration and coordination between large groups much more fault tolerant. The need for such capabilities is obvious when dealing with data – where the existence of multiple versions of the same underlying dataset is almost ubiquitous and with only the most primitive tool support.

TerminusDB and DBpedia

Team TerminusDB took part in the DBpedia Autumn Hackathon 2020. As you know, DBpedia is an extract of the structured data from Wikipedia.

You can read all about our DBpedia Autumn Hackathon adventures in this blog post.

Open Source

Unlike many systems in the graph database world, TerminusDB is committed to open source. We believe in the principals of open source, open data and open science. We welcome all those data people that want to contribute to the general good of the world. This is very much in alignment with the DBpedia Association and community.

DBpedia on TerminusHub

TerminusHub is the collaborative point between TerminusDBs. You can push data to you colleagues and collaborators, you can pull updates (efficiently – just the diffs) and you can clone databases that are made available on the Hub (by the TerminusDB team or by others). Think of it as GitHub, but for data.

The DBpedia database is available on TerminusHub. You can clone the full DB in a couple of minutes (depending on your internet connection of course) and get querying. TerminusDB uses succinct data structures to compress everything so it makes sharing large database feasible – more technical detail here: https://github.com/terminusdb/terminusdb/blob/dev/docs/whitepaper/terminusdb.pdf for interested parties.

TerminusDB in the DBpedia Association

We will contribute to DBpedia by working to improve the quality of data available, by introducing new datasets that can be integrated with DBpedia, and by participating fully in the community.

We are looking forward to a bright future together.

A big thank you to Luke and TerminusDB presenting how TerminusDB works and how they would like to work with DBpedia in the future.

Yours,

DBpedia Association

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 by Irene Martínez and Susana López from GNOSS

GNOSS (https://www.gnoss.com/) is a Spanish technology manufacturing company that has developed its own platform for the construction and exploitation of knowledge graphs. GNOSS technology operates within the framework of the set of technologies that concur in the Artificial Intelligence Program semantically interpreted: NLU (Natural Language Understanding); identification, extraction, disambiguation and linking of entities; as well as the construction of interrogation and knowledge discovery systems based on inferences and on systems that emulate the forms of natural reasoning.

How is our business focus

The GNOSS project is positioned in the emerging market for Deep AI (Deep Understanding AI). By Deep AI we mean the convergence of symbolic AI and sub-symbolic AI.

GNOSS is the leading company in Spain in the construction of solutions aimed at the construction of knowledge ecosystems interpretable and queryable (interrogable) by machines and people, which integrate heterogeneous and distributed data represented by technical vocabularies and ontologies written in programming languages (OWL-RDF ) interpretable by machines, which are consolidated and exploited through knowledge graphs

The technology developed by GNOSS facilitates the construction, within the framework of the aforementioned ecosystems, of intelligent interrogation and search systems, information enrichment and context generation systems, advanced recommendation systems, predictive Business Intelligence systems based on dynamic visualizations and NLP/NLU systems.

GNOSS works in the cloud and is offered as a service. We have a complex and robust technological infrastructure designed to compute intelligent data in a framework that offers the maximum guarantee of security and best practices in technology services.

Products and Solutions

PRODUCTS

GNOSS Knowledge Graph Builder is a development platform upon which third parties can deploy their web projects, with a complete suite of components to build Knowledge Graphs and deploy an intelligent web semantically aware in record time. The platform enables the interrogation of a Knowledge Graph by both machines and people. The main modules of the platform are 1) Metadata and Knowledge Graph Construction and Management; 2)Discovery, reasoning and analysis through Knowledge Graphs; 3) Semantic Content Management. It also includes some configurable characteristics and functions for fast, agile and flexible adaptation and evolution of intelligent digital ecosystems

SOLUTIONS

Thanks to GNOSS Knowledge Graph Builder and GNOSS Sherlock Services, we have developed a suite of transversal solutions and some sectorial solutions based on the creation and exploitation of Knowledge Graphs.

The transversal solutions are: GNOSS Metadata Management Solution (for the integration of heterogeneous and distributed information into semantic data layer consolidating information into a knowledge graph), GNOSS Sherlock NLP-NLU Service (Intelligent software services for machines to understand us, based on natural language processing and on entity recognition and linking; and dynamic graphic visualizations), GNOSS Search Cloud (which includes intelligent information search, interrogation and retrieval systems; inferences; recommendations and generation of significant contexts), GNOSS Semantic BI&Analytics (expressive and dynamic Business Intelligence based on Smart Data).

We have developed sectorial solutions in Education and University, Tourism, Culture and Museums, Healthcare, Communication and MK, Banking, Public Administration; Catalogs and support to supply chain.

What significance does DBpedia for us

We think that the foundations for the construction of the great European Project of Symbolic AI are being created thanks to DBpedia and other Linked Open Data projects, by turning the internet into a Universal Knowledge Base, which works according to the principles and standards of Linked Open Data and Semantic Web. This knowledge base, as the brain of the internet, would be the basis of the IA of the future. In this context, we consider that DBpedia plays a central role as an open general knowledge base and, therefore, as the core of the European Project of Symbolic AI.

Currently, some projects developed with GNOSS platform are already using DBpedia to access a large amount of structured and ontologically-represented information, in order to link entities, enrich information and offer contextual information. Two examples of this are the ‘Augmented Reading’ of Museo del Prado in the descriptions of the artworks of the Museum Prado, and the Graph of related entities in Didactalia.net.

The ‘Augmented Reading’ of Museo del Prado in the descriptions of the artworks of the Museum (see for instance ‘The Family of Carlos IV’, by Francisco de Goya) recognizes and extracts the entities contained in them, thereby providing additional and contextual information about them, so that anyone who can read them without giving up understanding them in depth.

In Didactalia.net, for a given educational resource, its Graph of related entities works as a conceptual map of the resource to support the teacher and the student in the teaching-learning process (see for instance this resource about Descartes).

How do we envision our future work with DBpedia

GNOSS can contribute to DBpedia at different levels, from making suggestions for further development to participating in strategy and commercialization.

We could collaborate with DBpedia contributing to tests of the releases of DBpedia and giving our feedback of the use of DBpedia in projects applied to public and private organizations developed with GNOSS. Based on this, we could make suggestions for future work considering our experience and customer needs in this context.

We could participate in the strategy and commercialization, in order to gain more presence in sectors in which we work, such as healthcare, education, culture or communication, and to achieve that the private companies can appreciate and benefit from the great value that DBpedia can offer them.

A big thank you to GNOSS for presenting their product and envisioning how they would like to work with DBpedia in the future.

Yours,

DBpedia Association

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