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Maverick: Discovering Exceptional Facts from Knowledge Graphs 12/03/19 Paper published in Proc. ACM SIGMOD International Conference on Management of Data, 2018. Presented by: Juan Carrillo Candidate for MASc. in Computer Software Department

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Maverick: Discovering Exceptional Facts from Knowledge Graphs

Paper published in Proc. ACM SIGMOD International Conference on Management of Data, 2018. Presented by: Juan Carrillo Candidate for MASc. in Computer Software Department of Electrical & Computer Engineering University of Waterloo

12/03/19

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Agenda

  • 1. Introduction
  • 2. Maverick core features
  • 3. Experiments
  • 4. Conclusions
  • 5. Discussion

Maverick: Discovering Exceptional Facts from Knowledge Graphs

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Maverick: Discovering Exceptional Facts from Knowledge Graphs

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Introduction

1

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  • 1. Introduction

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From knowledge graphs to exceptional facts

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  • 1. Introduction

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Knowledge graphs (Linked Data) Maverick approach

Manually designed queries Automated detection of exceptional facts

The problem, and the Maverick approach

Pattern generator

Context evaluator Exceptionality evaluator

Fact reporter

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  • 1. Introduction

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Related background

2010

Outlier detection J Gao - 2010

On community

  • utliers and their

efficient detection in information networks 2016

Outlying aspect mining F Angiulli - 2016

Outlying property detection with numerical attributes 2018

Maverick: Discovering Exceptional Facts from Knowledge Graphs SIGMOD’18

Comprehensive description and math basis 2018

VLDB’18

High-level description and demo

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Maverick: Discovering Exceptional Facts from Knowledge Graphs

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Maverick core features

2

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  • 2. Maverick core features

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Entity, context, pattern

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  • 2. Maverick core features

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The overall framework

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  • 2. Maverick core features

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Main Algorithm

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  • 2. Maverick core features

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Description of components

Context Evaluator

  • Uses a graph query

system (Neo4j)

  • Takes a pattern as

input and returns the matches

  • Agnostic to query

processing system

Exceptionality Evaluator Pattern generator

  • Takes the entity of

interest and its contexts

  • Looks for the k

subspaces with highest scores

  • Implements scoring

functions

  • Uses beam search to

look for promising patterns

  • Implements domain

specific heuristics

  • Beam width can be

tuned to requirements

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Maverick: Discovering Exceptional Facts from Knowledge Graphs

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Experiments

3

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  • 3. Experiments

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Experimental setup

Single node: 16-core, 32GB RAM

Datasets

WCGoals 49.078 nodes, 158.114 edges, 13 different edge labels, and 11 entity types. OscarWinners 42.148 nodes, 63.187 edges, 24 distinct edge labels, and 13 entity types.

Methods compared

▪ Beam-Rdm ▪ Beam-Opt ▪ Beam-Conv ▪ Breadth-First

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  • 3. Experiments

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Efficiency

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  • 3. Experiments

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Efficiency

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  • 3. Experiments

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Effectiveness

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Conclusions

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  • 4. Conclusions

✓ The authors model an exceptional fact as a context-pattern pair on a

knowledge graph

✓ Exponential complexity of search is handled using beam search ✓ The framework is adaptable to domain specific requirements

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Takeaways and paper contributions

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Maverick: Discovering Exceptional Facts from Knowledge Graphs

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Thanks for your attention

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Discussion

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  • 5. Discussion
  • 1. What other heuristics could be proposed in addition to the two presented

in the paper? Design requirements for a third heuristic?

  • 2. How Maverick would perform over a completely different dataset?

Different proportions among nodes, edges, edge labels, and entity types.

  • 3. What if we add attributes to the nodes and edges? Constraints
  • 4. How to adapt Maverick to work over multiple/linked knowledge graphs?

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Research Industry

  • 5. What is an example of an application over Google knowledge graph?