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Natural language why-question answering system in business intelligence context

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Abstract

Business Intelligence is the key technologies that ensures effective decision making through extracting relevant information and providing adapted systems as the Data Warehouses. To access decisional information, the decision maker should express his requirements in Natural Language interfaces without any technical skills, avoiding the IT-Designer intervention. Often, the decision maker’s requirements are expressed as WH-questions (What, Who, Where, etc.) or Keyword-like questions. In this paper, we emphasize on a Why-Question asked in Business Intelligence context. This question has not been well dealt in the literature in terms of produced answers. Indeed, to respond this type of question, it is necessary to provide explanations. These explanations are determined by identifying causal relationships between the phenomenon highlighted in the Why-Question and factors that can influence this phenomenon. In this context, we propose an approach on which a system can address a causality problem related to answering a decisional Why-Question. To validate our approach a tool called BI Why Q/A is developed. In order evaluate our proposal in terms of efficiency and relevance, a set of experimental studies is carried out and presented.

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Enquiries about data availability should be directed to the authors.

Notes

  1. https://github.com/microsoft/powerbi-desktop-samples.

  2. https://github.com/microsoft/powerbi-desktop-samples.

  3. https://onlinehelp.tableau.com/current/pro/desktop/fr-fr/trendlines-model.html.

  4. This depends on the periodic supplying of the DW as well as on the exploitation of new external data sources.

  5. Causal Bayesian network CBN requires an initial causal network (variable and value parameters) defined by a domain expert [45]. Unfortunately, the CBN can not be adapted in our context.

  6. https://www.eviews.com/home.html.

  7. Since the data available in the DW concern the duration [2012,2017], then we extract the average temperature for sales territories, recorded in this period.

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Djiroun, R., Guessoum, M.A., Boukhalfa, K. et al. Natural language why-question answering system in business intelligence context. Cluster Comput 27, 11039–11067 (2024). https://doi.org/10.1007/s10586-024-04327-4

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