Knowledge graph convolutional networks - London 2018
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As humans we use our knowledge, our reasoning and our understanding of situational context to make accurate predictions about the world around us; machine learning doesn’t typically make use of any of this rich information. The ability to leverage highly interrelated data will yield a step-change in the quality and complexity of predictions that can be made for the same volume of data. We present Knowledge Graph Convolutional Networks: a method for performing machine learning over a Grakn Knowledge Graph, which captures micro-context and macro-context for any Concept within the graph. This methodology demonstrates how we can usably combine knowledge, learning and reasoning to build systems that start to look truly intelligent. Associated blog post: https://blog.grakn.ai/knowledge-graph-convolutional-networks-machine-learning-over-reasoned-knowledge-9eb5ce5e0f68 Associated video: https://youtu.be/Jx_Twc75ka0 This is a clip from the Grakn London Meetup at the Royal Academy of Engineering (November 2018). Join the community: grakn.ai/community
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Knowledge graph convolutional networks - London 2018
- 1. Knowledge Graph Convolutional Networks James Fletcher, Principal Researcher
- 2. Follow us @GraknLabs Machines aren’t as smart as we’d like Highly domain constrained, generalisation and model-wise Reliant on big data Require complex learning models Only use flat arrays of input data Don’t exploit data context
- 3. Follow us @GraknLabs Human Capabilities Long-term memory Basic body function Basic emotions Appropriate social response Skilled movement coordination Language Vision Sensory processing Planning Motivation Motor control Working memory Reasoning Abstract thought Hearing Personality Learning
- 4. Follow us @GraknLabs Human Capabilities Long-term memory Reasoning Learning
- 5. Follow us @GraknLabs Human Capabilities Long-term memory Reasoning Learning
- 7. Learning
- 8. Follow us @GraknLabs Human Capabilities Long-term memory Reasoning Learning
- 9. Follow us @GraknLabs ? Reasoning Not popular when taxon $a has super $b taxon $b has super $c then taxon $a has super $c rule This allows us to infer additional information Species: Balaenoptera acutorostrata Genus: Balaenoptera Family: Balaenopteridae Order: Cetacea Class: Mammalia
- 10. Follow us @GraknLabs Reasoning How can we get machines to do reasoning for us? Symbolic Automation Requires structured data storage Species: Balaenoptera acutorostrata Genus: Balaenoptera Family: Balaenopteridae Order: Cetacea Class: Mammalia
- 11. Follow us @GraknLabs Human Capabilities Long-term memory Reasoning
- 12. Follow us @GraknLabs Long-term Memory Sounds like a database, but with crazy complexity But how do we humans store memories? More of a distributed web - a graph Hard to search without governing structure (how many times did you forget what you went there for?)
- 13. Follow us @GraknLabs Structured Long-term Memory We can define a structure for our data This way, it’s: • Easy to search • Possible to automate reasoning Model: Entity, Relationship, Attribute as nodes Roles as edges Demo:
- 14. Follow us @GraknLabs Combining the three Long-term memory Reasoning Learning
- 15. Follow us @GraknLabs High-Level Architecture Learner Reasoner Knowledge Graph Learning Reasoning Long-term Memory A learner talks to a knowledge graph via a reasoner
- 16. Follow us @GraknLabs High-Level Architecture KGCN Reasoner Knowledge Graph Graql Learner
- 17. Follow us @GraknLabs Query for a batch of concepts Downstream Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Embedding for a single concept Knowledge-set for training/testing/prediction KGCNmatch $x isa iris; get; Knowledge Graph Convolutional Network Concepts
- 18. Follow us @GraknLabs Knowledge Graph Convolutional Network An adaptation of the GraphSAGE paper from Stanford SNAP 1 1 1 1 1 2 2 2 2 2 . . . . . . . . . . . . . . . . . . AGGREGATION COMBINATION . . . . . . . . . . . . . . . . . . . +
- 20. Follow us @GraknLabs Combination 1 x 9 Concat . . . . . . 9 x 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . weights . . . . 1 x 4 combined output
- 21. Follow us @GraknLabs High-Level Architecture KGCN Reasoner Knowledge Graph Graql
- 22. Follow us @GraknLabs Example: CITES Animal Trade Dataset Convention on International Trade in Endangered Species of Wild Fauna and Flora • Lists exchanges (imports & exports) of items between countries • Forms a sparse graph of interconnected data Scenario: We’re looking for suspicious trade to enforce protection of the most endangered animals Demo: predicting attribute values using a KGCN
- 23. Follow us @GraknLabs Predicting endangerment-level associated with traded-items 20 9 1 7 22 1 5 3 22 Confusion Matrix Actual Class Predicted Class 1 2 3 1 2 3
- 24. Follow us @GraknLabs Ingesting the results
- 25. Follow us @GraknLabs Hunting down suspicious activity
- 26. Follow us @GraknLabs Machine Learning over Reasoned Knowledge Long-term memory Reasoning Learning
- 27. Follow us @GraknLabs What does Grakn do? Knowledge schema Flexible Entity-Relationship concept-level schema to build knowledge models Model complex domains Automated Reasoning Automated deductive reasoning of data points during runtime (OLTP) Derive implicit facts & simplification Distributed Analytics Automated distributed algorithms (BSP) as a language (OLAP) Automated large scale analytics Higher-Level Language Strong abstraction over low- level constructs and complex relationships Easier to work with complex data
- 28. Get in touch Twitter: @jmsfltchr Slack: grakn.ai/slack Blog post
- 29. Follow us @GraknLabs Selfie Time
- 30. Knowledge Graph Convolutional Networks James Fletcher, Principal Researcher