At Center Stage VI: Ontotext and Semantic Web Company on Creating and Scaling Big Enterprise Knowledge Graphs

A series of blog posts focusing on major Ontotext webinars and how they fit into the bigger picture of what we do. In this post, we talk about our partnership with Semantic Web Company and how our technologies complement each other and bring even greater momentum to knowledge graph management.

February 18, 2022 8 mins. read Gergana Petkova

In the last few posts dedicated to Ontotext webinars, we have focused on some of the major benefits of our flagship product GraphDB. So you should know by now that GraphDB is easy to integrate with and operate in today’s enterprise IT architecture. But no system, even such a highly mature RDF database for knowledge graphs as ours, is an island.

The very first post of this series presented two knowledge graph maps with 20+ applications and 30+ capabilities. So how can one single company claim to do it all and do it well? It takes a whole village to raise a child so it can grow in a healthy and multi-flavored environment. And based on our long experience of crafting big knowledge graphs to enable unified data access and cognitive analytics, we believe that the same applies to end-to-end enterprise solutions.

It Takes a Technology Ecosystem

To effectively cover all capabilities and requirements of end-to-end solutions and roll out knowledge graphs at scale, one needs a partner technology ecosystem. With the increasingly demanding requirements for knowledge graphs in enterprise environments we’ve seen in the last few years, such partnerships are becoming very important for the global market.

Fortunately, we are privileged to have such a technology ecosystem and it powers the next generation of content and data management applications for many of the world’s most knowledge-intensive enterprises – all the way from Life Sciences and Financial Services to Publishing, Government and Industry.

Today we want to draw your attention to a couple of webinars we’ve done jointly with one of our partners – Semantic Web Company (SWC).

We are very happy to partner with SWC because we complement each other’s strengths. Our two companies have implemented a seamless integration of the PoolParty Semantic Suite™ v.8 with GraphDB. This enables our users to work with a rich user-friendly toolset to manage a graph composed of billions of edges that is hosted in data centers around the world. As a result, enterprises can unlock the potential hidden in knowledge they already have.

Read on and watch the webinars to get to the details!

Knowledge Graphs: 5 Use Cases and 10 Steps to Get There

Our first webinar Knowledge Graphs: 5 Use Cases and 10 Steps to Get There is presented by the founders and CEOs of two of the leading companies in the field of graph technologies and implementation of knowledge graphs: Atanas Kiryakov of Ontotext and Andreas Blumauer of SWC. They explain why knowledge graphs play a central role in improving data analytics, data governance and content management as well as how they support business decision making, customer satisfaction and knowledge discovery.

You will get a guided tour through our ten steps to create, use, evolve and maintain knowledge graphs. Atanas and Andreas will also show you how graph technologies and related methodologies can enable humans and computers to work together in knowledge management, information extraction and data analytics. The webinar highlights technologies and methodologies that automate the routine work of data scientists, librarians, editors and other knowledge workers, while – based on explainable AI – enabling them to control these processes and contribute with explicit knowledge, sample data or feedback.

Interesting attendee question: Convincing organizations to start using knowledge graphs can be pretty tricky. They want to see it in action and at a low cost. What are the low cost approaches that one can use to demonstrate the power of knowledge graphs for organizations that are unfamiliar with them?

Ontotext & SWC answer: There are several ways to reduce the initial cost of giving it a try. What Ontotext and SWC are doing is to have pre-packaged solutions for different industry verticals. This allows us to shorten the time of doing a PoC from what would otherwise take a year down to one month. And we do it by adjusting and customizing knowledge graphs that are already out there. If you take a domain like Life Sciences, for example, you have all these public databases that everyone in this space must use. Luckily, we have those already pre-integrated and available as a knowledge graph, so in each following project we only take care to bring the specific knowledge that our customer needs. And this is also possible in other domains. What we did to shorten the time to demonstrate the effect is making GraphQL interfaces to a knowledge graph, which eliminates the need for middleware that stands between the knowledge graph and the front end applications. So, we think that being able to do a demo in a couple of days is the best answer to this question.

New call-to-action


Build Knowledge Graphs at Scale with PoolParty 8 and GraphDB

The first webinar explores the capabilities and applications of knowledge graphs and how they help enterprises get a competitive advantage in dynamic environments. Our second joint webinar Build Knowledge Graphs at Scale with PoolParty 8 and GraphDB focuses on how to increase efficiency and scalability with the new PoolParty 8 and how to benefit from the performance and reliability of GraphDB as a build-in database.

The webinar is presented by four speakers. From Ontotext, we have our CTO Vassil Momtchev and the Head of Semantic Analytics Solutions Ivaylo Kobakov and from SWC we have their CTO Robert David and the Product Owner Christian Blaschke. Besides talking about the release of PoolParty 8.0 and going over the benefits of having GraphDB as a built-in database, they show Ontotext’s NOW (News on the Web) demo service and how to use the PoolParty Graph Editor to edit a knowledge graph with over 300 million edges. They also present PoolParty’s new front-end search and exploration tool, which was developed using the new GraphQL interfaces to GraphDB.

Working with the comprehensive pallett of the PoolParty Semantic Suite while managing large knowledge graphs in GraphDB allows users to efficiently deal with a wide range of problems that require comprehensive domain knowledge. Our two companies are constantly improving the integration of our flagship products to meet the highest requirements of knowledge workers and experts as well as enterprise architects and system operations teams.

Interesting attendee question: Does NOW use the complete up-to-date information from the live Wikidata data or is the data dated or filtered in some way?

Ontotext & SWC answer: No, NOW doesn’t use the live data. The reason for this is that, in order to turn this data into a cohesive knowledge graph, which is required for the natural language processing of that data, there is a reconciliation process that happens. An ETL transforms all this data from Wikidata and DBPedia and loads it into GraphDB. We usually do this in offline batch mode, so we can handle all cases of reconciling the data, using single URIs for identifying the entities, etc.

Interesting attendee question: How to ingest heterogeneous source data types into Ontotext GraphDB?

Ontotext & SWC answer: GraphDB provides multiple interfaces for integrating data and also works well with PoolParty’s UnifiedViews. Our two technologies complement each other because GraphDB is a development tool whereas the PoolParty’s goal is to provide an out-of-the-box product where you can get decent results with minimal configuration. To come back to data integration, GraphDB supports data virtualization and also provides the OpenRefine interface, which allows you to perform data cleaning, entity resolution and importing data into the database. So, Ontotext has some tools that are mostly optimized to have a no-code development such as GraphDB’s interfaces, but you also use PoolParty’s UnifiedViews to approach the challenge of data integration, which is a massive task.

New call-to-action


Wrap Up

This covers the two webinars we wanted to present to you today.

Our extensive partner technology ecosystem effectively covers the requirements of end-to-end enterprise solutions (dive in Atanas Kiryakov’s webinar: Knowledge Graph Maps: 20+ Application and 30+ Capabilities for a comprehensive overview of the different applications of knowledge graphs and the capabilities that enable them). The blend of our technologies provides a healthy and multi-flavored environment for content and data management applications in many knowledge-intensive enterprises.

We hope you’ve heard enough to want to dive straight into one of them or why not both?

Watch out for our next post in this series At Center Stage or visit our Webinars directly to check out what is upcoming!


Article's content

Content Manager at Ontotext

Gergana Petkova is a philologist and has more than 15 years of experience at Ontotext, working on technical documentation, Gold Standard corpus curation and preparing content about Semantic Technology and Ontotext's offerings.

Knowledge Graphs: Redefining Data Management for the Modern Enterprise

Read this post about some of the primary problems of today’s enterprise data management and how knowledge graphs can solve them

Knowledge Graphs: Breaking the Ice

Read about the nature and key characteristics of knowledge graphs. It also outlines the benefits of formal semantics and how modeling graphs in RDF can help us easily identify, disambiguate and interconnect information

GraphDB in Action: Navigating Knowledge About Living Spaces, Cyber-physical Environments and Skies 

Read about three inspiring GraphDB-powered use cases of connecting data in a meaningful way to enable smart buildings, interoperable design engineering and ontology-based air-traffic control

Your Knowledge Graph Journey In Three Simple Steps

A bird’s eye view on where to start in building a knowledge graph solution to help your business excel in a data-driven market

Data Management Made Easy: The Power of Data Fabrics and Knowledge Graphs

Read about the significance of data fabrics and knowledge graphs in modern data management to address the issue of complex, diverse and large-scale data ecosystems

GraphDB in Action: Powering State-of-the-Art Research

Read about how academia research projects use GraphDB to power innovative solutions to challenges in the fields of Accounting, Healthcare and Cultural Heritage

At Center Stage VIII: Ontotext and Enterprise Knowledge on the Role of Knowledge Graphs in Knowledge Management

Read about our partnership with Enterprise Knowledge and knowledge management as an essential business function and lessons learned from developing content recommenders using taxonomies and GraphDB.

At Center Stage VII: Ontotext and metaphacts on Creating Data Fabrics Built on FAIR Data

Read about our partnership with metaphacts and how one can use the metaphactory knowledge graph platform on top of GraphDB to gain value from their knowledge graph and accelerate their R&D.

At Center Stage VI: Ontotext and Semantic Web Company on Creating and Scaling Big Enterprise Knowledge Graphs

Read about our partnership with Semantic Web Company and how our technologies complement each other and bring even greater momentum to knowledge graph management.

At Center Stage V: Embedding Graphs in Enterprise Architectures via GraphQL, Federation and Kafka

Read about the mechanisms for building a big enterprise software architectures by embedding graphs via GraphQL, Federation and Kafka

Ontotext’s Perspective on an Energy Knowledge Graph

Read about how semantic technology can advance energy data exchange standards and what happens when some energy data is integrated in a knowledge graph.

At Center Stage IV: Ontotext Webinars About How GraphDB Levels the Field Between RDF and Property Graphs

Read about how GraphDB eliminates the main limitations of RDF vs LPG by enabling edge properties with RDF-star and key graph analytics within SPARQL queries with the Graph Path Search plug-in.

At Center Stage III: Ontotext Webinars About GraphDB’s Data Virtualization Journey from Graphs to Tables and Back

Read this second post in our new series of blog posts focusing on major Ontotext webinars and how they fit in the bigger picture of what we do

At Center Stage II: Ontotext Webinars About Reasoning with Big Knowledge Graphs and the Power of Cognitive Graph Analytics

Read this second post in our new series of blog posts focusing on major Ontotext webinars and how they fit in the bigger picture of what we do

At Center Stage I: Ontotext Webinars About Knowledge Graphs and Their Application in Data Management

Read the first post in our new series of blog posts focusing on major Ontotext’s webinars and how they fit in the bigger picture of what we do

The Gold Standard – The Key to Information Extraction and Data Quality Control

Read about how a human curated body of data is used in AI to train algorithms for search, extraction and classification, and to measure their accuracy

Study of the Holocaust: A Way Out of Data Confusion

Learn how a ML algorithm trained to replicate human decisions helped the EU-supported EHRI project on Holocaust research with record linking.