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Connected Data London 2024 recordings are finally here! Connected Data enthusiasts, thank you all for joining us at Connected Data London 2024. There's nothing like the in-person Connected Data London experience, and this one was a special event for all of us after a couple of

.Open Ownership and Open Data Services have updated the RDF vocabulary for the Beneficial Ownership Data Standard (BODS) to support people who want to use beneficial ownership data in a linked data format openownership.org/en/publication… #RDF #linkeddata #beneficialownership #openstandards

Flawless Semantic Modeling in OWL and SKOS - The Practice Hands-on experience of developing a semantic model in OWL and SKOS, with progressive exercises designed to illustrate pitfalls we need to avoid and dilemmas we need to break during the modeling process. Based on the

Validating semantic knowledge graphs using SHACL Prior to the Shape Constraint Language (SHACL) we had no W3C standardization for validation our semantic knowledge graphs against a set of constraints. By constraining RDF using SHACL we gain the possibility of exactly

Enhancing Graph Investigations with No-Code Entity Resolution in Linkurious Our friends from @Senzing and Linkurious join forces for this live conversation and demo, led by @Pacoid March 19, 12pm ET Financial crime investigations rely on clear, connected data. Fragmented

Aleksa Gordic, Machine Learning engineer @ Microsoft and Founder @ The AI Epiphany, shares his journey in the world of Graph Machine Learning. Aleksa started exploring the basics in the world of Graph Machine Learning, and ended up implementing and open sourcing his own Graph

Serving graph use cases on Google Cloud via @Puppyquery Integration with BigQuery and AlloyDB announced; support for Gremlin and openCypher 🧵 #Graph #Analytics #Databases #Datawarehouse #GoogleNext #GoogleCloud #BigTable #AlloyDB #PostegreSQL

From Unstructured Text to Interactive Knowledge Graphs Using LLMs How LLMs can used in a knowledge extraction and visualization pipeline Building a knowledge graph from raw text is not easy. It requires identifying entities and their relationships, hand-coded extraction rules,

We've worked with the awesome team Kùzu to put together this end to end guide to extracting, loading and visualizing entities for the Connected Data London Knowledge Graph Challenge using Kuzu and G.V(). Full details below! gdotv.com/blog/fast-scal…

CoDEx: A Comprehensive Knowledge Graph Completion Benchmark CoDEx is a set of knowledge graph COmpletion Datasets EXtracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx

The AI Risk Ontology (AIRO) was released in April. AIRO (AI Risk Ontology) is an ontology for expressing risk of AI systems based on the requirements of the AI Act, ISO/IEC 23894 on AI risk management and and ISO 31000 series of standards. AIRO assists stakeholders in

Game Changers: Python, Video Analysis, and Property Graphs in Sports Analytics In sports analytics, the fusion of Python programming, video analysis, and property graphs is transformative. This presentation offers an in-depth exploration of how these technologies converge

From Genesis to Maturity: Managing Knowledge Graph Ecosystems Through Life Cycles Knowledge graphs (KGs) play a crucial role in the integration and organization of heterogeneous data and knowledge, enabling advanced data analytics and decision-making across various industries.

Welcoming Semantic Partners to Connected Data London 2025 as a Silver Sponsor! Semantic Partners are a transformation technology consultancy with the mission of positioning data and AI as your distinctive competitive edge. Semantic Partners can help you unlock the full potential

The role of Ontology in the Flywheel of Business Operations Currently, AI suffers from some key issues. Today’s AI is prone to errors, and is plagued by bias and discrimination as well as privacy issues. Thus, among increasing calls for stringent AI governance and compliance,

How GraphRAG Works Step-by-Step GraphRAG is an enhancement to retrieval-augmented generation that leverages graph structures. There are many different implementations, with the original one having been open sourced by Microsoft. This article describes each step of the GraphRAG

LLMs, Agents, and Graph Memory Large Language Models (LLMs) have shown strong performance in generating contextually relevant responses. However, they are limited by their fixed context windows, which limit their ability to maintain coherence over extended interactions. This