![](bg5.png)
4.2.2 Two-Rules-Transitivity-Based Approach
In this subsection we present an example of a two-
rules-transitivity-based inter-event scenario genera-
tion. The example is presented in the table 6.
Table 6: Two-rules-transitivity-based approach example.
LHS RHS
Rule 1 ‘least’, ‘syria’ ‘news’, ‘car’,
‘kill’, ‘bomb’
Rule 2 ‘news’, ‘car’,
‘kill’, ‘bomb’
‘soldier’,
‘sever’
Rule 1, from the table 6, describes an event with
description: “A car bomb explodes at Sassine Square
in the Lebanese capital of Beirut, killing at least eight
people and wounding up to 78 others. (BBC)”. Rule 2
describes an event with description: “Syrian civil war:
A Jordanian soldier dies during a gunfight between
Jordanian troops and Islamic militants attempting to
cross the border into Syria. (CTV News)”.
From that rules, the following counterfactual sce-
nario was generated: “if ‘news’, ‘car’, ‘kill’, ‘bomb’
occurs together with the ‘least’, ‘syria’ then ‘soldier’,
‘sever’ are also likely to occur”.
5 CONCLUSION
In this paper, we proposed methods for time-window
constrained topic-based what-if scenario generation,
in the counterfactual perspective, founded on market-
basket analysis and association rules extraction. Def-
initions of counterfactual scenarios, both intra-event
and inter-event, are given. Preliminary results illus-
trate the extraction of coherent causal effects and re-
quire more analysis and controlled experiments. Fu-
ture work will apply the methods to events detected
using other ToI dictionaries. We will also include
evaluating the proposed methods in the context of the
usefulness of association rules and scenarios.
ACKNOWLEDGEMENTS
This work was supported by the grant of the Min-
istry of Science and Higher Education of the Republic
of Kazakhstan, project BR10965311 “Development
of intelligent information and telecommunication sys-
tems for urban infrastructure: transport, ecology, en-
ergy, and data-analytics in the Smart City concept”.
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