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The Knowledge Graph Conference (KGC) is a world-class curated program that brings experienced practitioners, technology leaders, cutting-edge researchers, academics and vendors together for two days of presentations, discussions and networking on the topic of knowledge graphs.
The Knowledge Connexions Conference (KnowCon) is adressing the transition from data to knowledge by connections and metadata. Graph Databases, Semantic Technology, Knowledge Graphs and Graph AI are solid building blocks that support this process.
The one-day conference with pre-conference workshops and masterclasses is organized by Connected Data London, in partnership with the Knowledge Graphs Conference.
2021 Knowledge Graph Conference (KGC), May 5–6, 2021, Virtual venue
Conference Theme: Connecting the Knowledge Ecosystem
Pre-conference Workshops: (May 3-4)
Knowledge Graphs for Social Good; K-iL2021: Workshop on Knowledge-infused Learning; Second Workshop on Personal Health Knowledge Graphs
Pre-conference Tutorials: (May 3-4)
Semantic Modeling for Knowledge Graphs; Validating semantic knowledge graphs using SHACL; Graph-Based Data Science; DBpedia Knowledge Graph Tech Tutorial; Annotating Tabular Data using Semantic Data Dictionaries; Integrating Data through Virtual Knowledge Graphs with Ontop; An Introduction to Knowledge Graphs; Foundation for a Knowledge Graph: Taxonomy Design Best Practices; How Graphs Improve Systems Thinking
Knowledge Connexions Conference (KnowCon), November 30 – December 2, 2020, Virtual venue
Conference Theme: Connecting data, people, and ideas: Building a global knowledge ecosystem
(Organizer: Connected Data London, Knowledge Graphs Conference)
Pre-conference Workshops (Nov 30):
Knowledge Graphs and SEO: The next chapter; AI + Knowledge – a match made in heaven?; From databases to platforms: the evolution of Graph databases; Knowledge Graph adoption in the Enterprise (Executive RoundTable)
Pre-conference Masterclasses (Dec 1):
Flawless Semantic Modeling in OWL and SKOS – The Theory; From Knowledge Graphs to AI-powered SEO – The Theory; Validating semantic knowledge graphs using SHACL; Graph Powered Machine Learning; Promoting Enterprise Knowledge Graphs with Metaphors and Stories; KG 101: Hands-on coding in Python
Conference Tracks (Dec 2):
Secret Knowledge: Visualizing Complexity
Sales AI – Building and maintaining a knowledge graph
Rebooting AI: Adding Knowledge to Deep Learning
Powering the Connected Enterprise in a Hybrid, Multicloud World
2020 Knowledge Graph Conference (KGC), May 4-7, 2020,
Columbia University, New York, NY, USA Virtual venue
Conference Theme: Knowledge Graphs for Artificial Intelligence in the Enterprise
Pre-conference Workshops (May 4-5):
Knowledge Graphs for Social Good (KGSG) – Best Practices, Methods, and Challenges; Personal Health Knowledge Graphs (PHKG): Challenges and Opportunities
Pre-conference Tutorials (May 4-5):
Building a Knowledge Graph from schema.org annotations; Data discovery on Siren Community Edition: a hybrid BI/Search/Knowledge graph platform for investigative intelligence; Virtualized Knowledge Graphs for Enterprise Applications; Designing and Building Enterprise Knowledge Graphs from Relational Databases; Introduction to Logic Knowledge Graphs, Succinct Data Structures and Delta Encoding for Modern Databases, and the Web Object Query Language; Rapid Knowledge Graph development with GraphQL and RDF databases; Modeling Evolving Data in Graphs While Preserving Backward Compatibility: The Power of RDF Quads
Product Demos; Lightning Talks; Ask Me Anything Sessions (AMA); Knowledge Graph Conference Prize
Frontiers in Data Intelligence
Side events (May 5):
Enterprise Knowledge Graph Foundation (EKG Foundation) launch; Inaugural investor/startup pitch event
2019 Knowledge Graph Conference (KGC), May 7-8, 2019, Columbia University, New York, NY, USA
Conference Theme: Learn to develop enterprise-wide knowledge graphs
Knowledge Graph Architecture (Storing and Querying Knowledge Graphs; Metadata, Schemas, Ontologies, Taxonomies; Integrating multiple source systems; Aligning multiple data models); Use Cases (Machine Learning and Artificial IntelligenceNatural Language Processing; Understanding Knowledge Graph Embeddings; Data integration; Semantic data lakes; Security and Privacy; Business Intelligence and Data Analytics; Reasoning and Inference; Customer 360; Distributed ledger; Fraud detection); Tools (Visualization; Mapping and Integration); Formats and Languages; Data Management (Data Governance Data Quality; Data Responsibility)
Graph Databases, Graph Query Languages, Graph Data Models