# An Interactive Tool for Goal Model Construction using a Knowledge Graph

Shahin Abdoul-Soukour, William Aboucaya, Nikolaos Georgantas

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## To cite this version:

Shahin Abdoul-Soukour, William Aboucaya, Nikolaos Georgantas. An Interactive Tool for Goal Model Construction using a Knowledge Graph. Journées nationales du GDR Génie de la Programmation et du Logiciel - GDR GPL 2025, Jun 2025, Pau, France. 2025. [hal-05207665](https://hal-INRA.hal.sciences.psf.cdn.cdn.cern.fr/hal-05207665v1)

HAL Id: hal-05207665

https://inria.hal.science/hal-05207665v1

Submitted on 12 Aug 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# An Interactive Tool for Goal Model Construction using a Knowledge Graph

## CONTEXT

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* The goal model is fundamental in **Goal-Oriented Requirements Engineering**

* Describes system goals hierarchically, refining high-level goals into specific ones

## SOLUTION

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Interactive graphical tool for leveraging existing domain knowledge in software system design

## RESEARCH TOPICS

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* Interactive design: Allows online step-by-step refinement of goals

* KG knowledge extraction:

* Semantic similarity

* Natural Language Inference (NLI)

* Sentiment analysis

* Graph-to-Text (G2T) generation

* Restricted KG exploration: Limits KG exploration depth and width for efficiency

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## CONCLUSION

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* Facilitates goal model creation interactively

* Balances exploration efficiency and relevance

## NEXT STEPS

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* Empirical evaluation of the tool

* To assess the degree of facilitation for the designer and the quality of the resulting goal models

## AUTHORS:

Shahin ABDOUL SOUKOUR, PhD Student, Inria Paris

William ABOUCAYA, Research engineer, Université PSL

Nikolaos GEORGANTAS, Researcher, Inria Paris

## AFFILIATIONS:

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PSL

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UNIVERSITÉ PARIS

OUR PAPER

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## TOOL WORKFLOW FOR GOAL MODELING

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## EVALUATION

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* Scenario: Flood management system

* KG details: 90 triples covering flood causes, detection techniques, impacts, and mitigation measures

* Generated 8 goal models by varying 2 core parameters: max depth and beam width of KG exploration

* Hardware: RTX 3080, 32GB

* Results:

* Optimal configuration: max depth = 3, beam width = 1

* Relevance: 45.45%

* Execution time: ~11.86 seconds

Evaluate the trade-off between time efficiency and quality of the resulting goal models when bounding KG exploration

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Impact of the maximum depth and the beam width on:

(a) Relevance vs. time performance

(b) Produced goals vs. time performance