1. Visual analytics for discovering entity relationship on text data
Hanbo Dai
Ee-Peng Lim
Hady Wirawan Lauw
HweeHwa Pang

2. Analysis scenario A homeland security analyst
Finds out relationships between two terrorists on complex, large information sources
Needs user judgments
Mas Selamat
Jemaah Islamiah
Al-Qaeda
Osama Bin Laden
Born in Central Java
Was not directly connected
Justinus Andjarwirawan
Abu Latif

3. Visual analytics system architecture

4. Two TUBE (Text-Cube) instances for entity relationship discovery
Document Evidence e.g. {d1, d2,…}
Mask value (0/1) nodes
Measures e.g. Path_strength e0 e1 e2 e3 e4
T1=<S1, B1, M1, D> e0 e1 e2 e3 e4 e0
Document Evidence e.g. {d3, d4,…}
Mask value (0/1) edges
Measures e.g. strength e1
T2 =<S2, B2, M2, D> e2 e3 e4

5. ER-Explorer interface

6. Visual analytical operations
Insert
Cluster
Delete

7. Our tool helps to discover new relationships

8. Conclusion
Interactive visual method to discover entity and relationships embedded in text data
ER-Explorer equipped with TUBE model and operations
Our tool assisted analysts in finding relationships between two terrorists

9. Back up slides

10. Case study Dataset: The hijacking of IC814
Entities of type Person, Organization, Event, GPE are extracted
Co-occurrence Relationships are identified on sentence level.
Each sentence is considered as a document.

11. Text-Cube Model Represents Entities and Relationships
An entity is either a named entity or a conceptual entity.
A n-dimensional TUBE is a tuple T= <S, B, M, D>
S: Schema = {s1, s2,…, sn}
Si denotes the list of entities of dimension i
B: Mask
0 or 1 value
M: Measure= {m1, m2,…, m|M|}
Each measure mi is associated with a measure function mfi
D: Document Collection
A TUBE T has | s1|×|s2|×…×| sn | cells
A cell c
Has document evidence denoted as Fd(c)
Is present if B(c)=1 , or hidden if B(c)=0
Has measure value denoted as c.mj , computed by mfj(c)
Represent the co-occurrence relationship, if Fd(c) is not empty

12. Measure formulas

13. Two TUBE Instances for entity relationship discovery
A discovery task is to find interesting paths between two entities source (s) and target (t)
A path represents a chain of relationships
1-Dimension TUBE instance: T1=<S1, B1, M1, D>
S1 initiated as all named entities
M1= {path_strength}
The strength of shortest path through an entity between s and t
2-Dimension TUBE instance: T2=<S2, B2, M2, D>
S2 initiated as all named entities on both dimensions
M2= {name_sim, strength, dom_entity}
name_sim
Computed by edit distance
strength
Computed by Jaccard Coefficent or Dice Coefficent
dom_entity
Whenever ei appears ej is always there, ej dominate ei

14. Related Work Social network visualization
assume entities and relations
have been identified and verified.
can be studied without supporting document
Use only measures of graph structure, such as degree, centrality.
Automatic path/subgraph finding algorithms
Users have little control over the relations and entities involved
Do not consider semantically identical entities.

15. Formal definition of entity
Entity e is defined as a named object or a set of other entities.

16. Tube operations Insert Remove SelectCell Cluster
Add an entity to a dimension
Remove
Remove an existing entity from a dimension
SelectCell
Assign 0 or 1 to a entry (a cell in T) in Mask
Cluster
Add a new conceptual entity representing a subset of entities to a dimension

17. Visual Analytics Operations
Insert an entity
SelectCell in T1 and T2
Reveals all relationships this entity has with all entities in the network
Delete
Delete a named entity
SelectCell in T1
Delete a conceptual entity
Remove in T1 and T2
Delete a relationship (a cell)
SelectCell in T2
Cluseter
Cluster in in T1 and T2