Kadaster Knowledge Graph
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The document discusses the Kadaster Knowledge Graph (KG) and LOKI system developed by Kadaster, the Dutch land registry. The KG links and integrates data from various sources to provide a unified view. It uses web standards like linked data to make the data interoperable, accessible, and reusable. LOKI is a question answering system built on top of the KG that allows users to ask questions about properties and receive location-based answers from integrated Kadaster and other data sources. The KG goes beyond traditional data publication approaches by making the data findable, accessible, interoperable, and reusable through open standards and decentralization.
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The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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Kadaster Knowledge Graph
- 1. The Kadaster Knowledge Graph & LOKI Ronzhin, S.; Folmer, E.; Maria, P.; Brattinga, M.; Beek, W.; Lemmens, R.; van’t Veer, R. Kadaster Knowledge Graph: Beyond the Fifth Star of Open Data. Information 2019, 10, 310. https://www.mdpi.com/548974
- 2. ן Data Science Team Lead @ Kadaster ן Research Associate on Interoperabilty & Standards @ University of Twente ן Board Member @ Platform Linked Data Netherlands
- 4. 4 | Dutch system of Key Registers
- 5. 6 |
- 6. The dream: Self Service GIS
- 14. Data story
- 15. 17 |
- 16. 18 | Dutch system of Key Registers
- 17. 19 | Dutch Humour: System of Silos
- 18. 20 |
- 20. Can you imagine?
- 21. My dream! Paleis het Loo https://brt.basisregistraties.overheid.nl/top10nl/id/gebouw/101895841
- 22. Citizens want a complete overview of their environment, which includes Kadaster data, but also municipality data, statistical data, etc. Based on the Knowledge Graph we are able to provide this integrated view on data.
- 28. Essence of Kadaster KG project It is not new, it was already possible……but expensive, not scaleable, not open, etc... The main differences are: ● Web standards based (linked data, interoperable) ● No spatial links, but administrative links. ● Data at the Source (in demo: 6 sparql endpoints federation) ● Application independent.
- 34. Demo LOKI
- 37. Solution Loki, which houses in Apeldoorn have a surface of more than 100 square meters? Location based Kadaster Information (btw, not really chat….but more Q&A)
- 38. 43 | Inleiding Doelstellingen Oplossing Projectaanpak Kosten & Baten Bijlagen
- 39. 44 | Inleiding Doelstellingen Oplossing Projectaanpak Kosten & Baten Bijlagen
- 41. The Kadaster Knowledge Graph & LOKI Ronzhin, S.; Folmer, E.; Maria, P.; Brattinga, M.; Beek, W.; Lemmens, R.; van’t Veer, R. Kadaster Knowledge Graph: Beyond the Fifth Star of Open Data. Information 2019, 10, 310. https://www.mdpi.com/548974
- 42. Some issues