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

A series of blog posts focusing on major Ontotext’s webinars and how they fit in the bigger picture of what we do. In this blog post, we'll talk about two webinars that give the bird’s eye view of the enterprise knowledge graph technology we have dedicated 20+ years to develop for some of the most knowledge intensive enterprises in various industries.

August 27, 2021 4 mins. read Gergana Petkova

This post continues the series of posts we started with At Center Stage: 2 Ontotext Webinars About Knowledge Graphs and Their Application in Data Management.

We want to give you the bigger picture of what we do and where Ontotext webinars fit into it – just a couple of webinars at a time. All of our webinars are available on demand. If you’ve missed one and if we’ve managed to pique your interest with this post, you can request a free recording.

Here, we’ve decided to present another two Ontotext webinars that give the bird’s eye view of the enterprise knowledge graph technology we have dedicated 20+ years to develop for some of the most knowledge intensive enterprises in various industries. These are: Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipes and Graph Analytics on Company Data and News. Both were presented by our CEO, Atanas Kiryakov.

Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipes

The first Ontotext webinar that takes center stage in this post is Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipes. It provides a brief introduction to logical reasoning and an overview of the most popular semantic schema and ontology languages: RDFS and the profiles of OWL 2. Atanas demonstrates how reasoning works using FactForge as a playground. FactForge is our public GraphDB demonstration service. It exposes a knowledge graph of more than 2 billion statements combining DBPedia, GeoNames, the Financial Industry Business Ontology and other data. Atanas also introduces the different reasoning implementation approaches and discusses their suitability for specific application scenarios.

You will learn about which OWL profiles are appropriate for reasoning with big knowledge graphs. You will also hear about the design choices for reasoning implementation, together with their pros and cons as well as how the popular triplestores implement reasoning. Finally, you will receive quick guidance for configuration and customization of reasoning with GraphDB.

Interesting attendee question: You said that inferencing doesn’t work with virtualization? Why is that?

Ontotext’s answer: If you have virtualization, it means that some of the data is in a remote system. This also means that you can’t pre-index it, you can’t do inferred closure there, you can’t do the inference at load time because the essence of virtualization is that you don’t load the data. So, your reasoner never sees all the data. Which means that if you use virtualization, you should resort to query time reasoning. And query time reasoning just doesn’t work on big datasets because you can’t optimize the queries.

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Graph Analytics on Company Data and News

The second Ontotext webinar Graph Analytics on Company Data and News focuses on the power of cognitive graph analytics to create links between various datasets and to lead to powerful knowledge discovery. Here again Atanas uses FactForge – this time to demonstrate how graph analytics on top of a big knowledge graph can provide entity awareness about the most common types of entities: people, organizations and locations. On top of combining several open data sources, FactForge has been mapped to the FIBO ontology and its entities have been interlinked to 1 million news articles.

You will learn about the importance of ranking of nodes based on graph centrality and about popularity ranking based on news mentions of the company and its subsidiaries. Atanas will also talk about retrieval of similar nodes in a knowledge graph and determining distinguishing features of an entity.

Interesting attendee question: How do you fuse two entities that are the same?

Ontotext’s answer: Essentially, to find the right match for a company from one source to a company in another source, first we have to find the likely candidates in the second source. This is called pre-selection. Then we evaluate each of these candidates and score them. For example, if one of the companies is registered in the US and the other is registered in Italy, this is strong evidence that they are not the same entity. If one of them is in the phone industry and the other is a bank, again, they are probably not the same entity.

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Wrap up

That covers the 2 Ontotext webinars we wanted to draw your attention to in this blog post. We hope you’ve heard enough to want to dive straight into one of them or why not even both.

Watch out for our next post in this series At Center Stage: Ontotext’s Webinars 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.

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