Tracking Data Sources of Fused Entities in Law Enforcement Graphs

© 2022 Neo4j, Inc. All rights reserved.
Tracking Data Sources of

Fused Entities in

Law Enforcement graphs
Luanne Misquitta,

VP of Engineering, GraphAware

Graphs and Law Enforcement
2

3
Connect the dots

4
Connect the dots

5
Connect the dots

6
Connect the dots

7
Connect the dots

Outline
• Key properties of law enforcement graphs

◦ Data sources

◦ Data quality and reliability

◦ Fused entities

• Modelling considerations
8

POLE
9
Person Location
Event
Object

Graph model
10

The graph
11

Disconnected events
12

13
Data Provenance
• The source of a piece of
information and the process that
led to its existence

• Used to form assessments
about the quality, reliability and
trustworthiness of the
information at hand

14
The INTs
• Intelligence Collection
disciplines

• Hierarchical

• Examples are SIGINT, OSINT,
MASINT, GEOINT

Local Crime data
15

Bring in Forensics
16

Modelling sources- use cases
• Do analysts want to only see the source that provided the information or do
more with it?

• Do analysts expect to traverse the graph within a particular source?

• Are sources attached only to nodes or also to relationships?

• Are sources hierarchical and does querying a source at a higher level imply
including all child sources?

• Does access control need to be set up to grant/deny access to certain
sources?

• Security classification levels by source
17

Modelling sources- as a node
• Hierarchies are modelled naturally

• Source based access control handled through the presence or absence of a path

• Sources on relationships do not fit into this model

• Supernode problem
18

Modelling sources- as a label
• For nodes, not relationships

• Hierarchy can be replicated through labels

• Label based access control
19

Modelling sources- as a property
• Both nodes and relationships

• Source hierarchy maintained separately, parameters passed via queries

• Access control on property values not supported natively

• Test performance when filtering on sources when traversing
20

Layering for analysis
21

Information and Source Ratings
Source Reliability is the degree of confidence assigned to the source with
respect to its capacity to provide valuable and accurate information.

https://en.wikipedia.org/wiki/Intelligence_source_and_information_reliability
22

Information and Source Ratings
Information Credibility is the confidence assigned to the information with
respect to its veracity.

https://en.wikipedia.org/wiki/Intelligence_source_and_information_reliability
23

Missing Information and Source Ratings
24

Drawing a different conclusion
25

Modelling Information and Source ratings
• A property for each on nodes and relationships

• Source hierarchy defaults

◦ Compose for display purposes

◦ Copy to filter during navigation
26

Same entity, different sources
27

Hypothesis links
28

Using node representations
29

Grouping
30

Create a fused entity
• Create a new entity which fuses information from facts determined to be the
same

• Determine the source and confidence in the new fused entity
31

Copy relationships from the facts
• The fused entity stands in for the underlying facts

• Relationships are transferred from the facts to the fused entity
32

Path finding
33

What about the facts?
• Create a FACT relationship from the fused entity to the facts

• Allows analysts to drill in and view individual facts

• Could affect node expansion and path finding

• Clutters visual exploratory analysis
34

Offloading facts to another graph
Create the fused entity in the fact graph (shell entity)
35
Main graph Main graph Fact graph

Recreate each fact in the fact graph and create the FACT relationship to the
fused entity
36
Fact graph

Copy each entity (shell) that the fact in the main graph is linked to and link
them to the facts in the fact graph
37
Fact graph

Remove the facts from the main graph
38
Main graph

Loading facts on demand
39

Challenges with updates to fused entities
• Do fused entities automatically get updated when the facts are updated?

• Materialised vs shell fused entities

• Information and source ratings may no longer be valid

• Conflicts need to be handled
40

Takeaways
• Data sources

◦ Consider use cases before modelling- single/multivalued, nodes/relationships, hierarchy, access
control

◦ Performance testing if filtering during traversals

• Importance of Information and Source Ratings

• Fused entities

◦ Via links or nodes

◦ Materialise or compose at query time

◦ Representing facts- same or different graph

◦ Consider flavours of fused entities if attribute level sources are to be tracked

• Fact update challenges

◦ System of record

◦ Validity of information and source rating of the fused entity post update

◦ Handling conflicts
41

42
Thank you!
Or, contact us at

info@graphaware.com
Visit GraphAware’s booth to explore the graph

Tracking Data Sources of Fused Entities in Law Enforcement Graphs

Recommended

Recommended

More Related Content

Similar to Tracking Data Sources of Fused Entities in Law Enforcement Graphs

Similar to Tracking Data Sources of Fused Entities in Law Enforcement Graphs (20)

More from Neo4j

More from Neo4j (20)

Recently uploaded

Recently uploaded (20)

Tracking Data Sources of Fused Entities in Law Enforcement Graphs