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/kgcns-machine-learning-over-knowledge-graphs-with-tensorflow-a1d3328b8f02 Associated video: https://www.youtube.com/watch?v=3adsYypRDsQ This is a clip from the Grakn Berlin Meetup (Berlin 2019). Join the community: grakn.ai/community

1. Knowledge Graph
Convolutional Networks
James Fletcher, Principal Researcher

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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

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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

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Human Capabilities
Long-term memory
Reasoning
Learning

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Human Capabilities
Long-term memory
Reasoning
Learning

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Learning

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Learning

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Human Capabilities
Long-term memory
Reasoning
Learning

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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

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?
Reasoning
Not popular
This allows us to infer
additional information
Species: Balaenoptera
acutorostrata
Genus: Balaenoptera
Family: Balaenopteridae How can we get machines to do
reasoning for us?
Symbolic Automation
Requires structured data
storage
taxonomic-ranking sub rule,
when {
(super-taxon: $grandparent, sub-taxon: $parent) isa taxonomic-hierarchy;
(super-taxon: $parent, sub-taxon: $child) isa taxonomic-hierarchy;
}, then {
(super-taxon: $grandparent, sub-taxon: $child) isa taxonomic-hierarchy;
};
$grandparent
$parent
$child
t.-hierarchy
t.-hierarchy
t.-hierarchy

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Human Capabilities
Long-term memory
Reasoning

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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?)

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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, Relation, Attribute (nodes)
Roles (edges)
Demo:
match $s isa taxonomic-species, has name $n;
$n "Balaenoptera acutorostrata";
$f isa taxonomic-family;
$h($s, $f) isa taxonomic-hierarchy;
get;

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Human Capabilities
Long-term memory
Reasoning
Learning

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High-Level Architecture
Learner
Reasoner
Knowledge Graph
Learning
Reasoning
Long-term Memory
A learner talks to a knowledge graph
via a reasoner

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High-Level Architecture
KGCN
Reasoner
Knowledge Graph
Graql
Learner
Grakn

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Query for a batch of concepts
Learning
Task
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Embedding for a single concept
Use as the Knowledge-set for
training/testing/prediction
KGCNmatch $x isa traded-item; get;
KGCNs - Objective
Create a bridge to ML

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Node Connection Inferred Connection
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Knowledge Graph
with information for
each node .
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Aggregate
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Information at Node
Combine .
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.One vector
represents
whole context
KGCNs – How do they work?

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Chained
result
KGCNs

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Maxpool
Corresponding outputs
KGCNs: Aggregation
Representations of
connected current Nodes

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Concat
KGCNs: Combination
Aggregated
context
representation
Representation
of current Node
Combined
representation
of current Node
and its context
.
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Supervised
Learner
Application
KGCN
Grakn
Query for
example Things
Retrieve
example Things
Get input data Call train()
Build feed dicts
Query for
neighbours
Build context
arrays
Find neighbour
Things
Run training
loop
Generate
embeddings
Calculate
loss
Optimise
weights
Make
predictions
Report Loss
< max_steps
> max_steps
KGCNs

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2-hop arrays 1-hop arrays
Aggregate 2-hop
Combine with 1-hop
Aggregate 1-hop
Combine with example
Example (0-hop) arrays
Embeddings
labels
Dense Layer
Class predictions 0 0 1
Softmax Cross Entropy
Loss
KGCNs

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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

25. 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

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Ingesting the results

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Hunting down suspicious activity

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Machine Learning over Reasoned Knowledge
Long-term memory
Reasoning
Learning

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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

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What does KGCN do?
Data Agnostic
Generic method with simple
network architecture; use
directly or customise
Easy to plug-and-play for
immediate results
Inductive Approach
Only concepts of interest are
considered, not the whole
graph
Scales up for large graphs
Downstream Learning
Use embeddings for your
own learning pipeline via
sup/unsup training
Leverage KGCNs to fulfil your
own objectives
Combines human skills
Performs learning and
reasoning over a hypergraph
A step change in cognitive
system capabilities

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Pre-release out now!
kglib repo
Twitter: @jmsfltchr
Slack: grakn.ai/slack

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Selfie Time

33. Knowledge Graph
Convolutional Networks
James Fletcher, Principal Researcher

34. Follow us @GraknLabs
Full Architecture
Reasoner
Knowledge Graph
Graql
Grakn
KGCN
Interface
TensorFlow Model
Neighbour Trees To Arrays
Grakn Python
Client
Neighbour Traverser
Application
Grakn Python
Client
Data Import
match $x isa animal-item; get; concepts
performance results / predictions

35. Follow us @GraknLabs
Full Architecture
Reasoner
Knowledge Graph
Graql
Grakn
KGCN
Interface
TensorFlow Model
Neighbour Trees To Arrays
Grakn Python
Client
Neighbour Traverser
Application
Grakn Python
Client
Data Import
match $x isa animal-item; get; concepts
performance results / predictions