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KDBI special issue: MapIntel: A Visual Analytics Platform for Competitive Intelligence
  • David Silva,
  • Fernando Bacao
David Silva
Universidade Nova de Lisboa Escola de Gestao de Informacao

Corresponding Author:[email protected]

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Fernando Bacao
Universidade Nova de Lisboa Escola de Gestao de Informacao
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Competitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision-making. The system searching module uses a retriever and re-ranker engine that first finds the closest neighbors to the query embedding and then sifts the results through a cross-encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto two dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modeling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups dataset, using the semantic document labels provided, and demonstrate the superiority of Transformer-based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months.
02 Nov 2022Submitted to Expert Systems
07 Nov 2022Submission Checks Completed
07 Nov 2022Assigned to Editor
20 Feb 2023Reviewer(s) Assigned
28 Apr 2023Review(s) Completed, Editorial Evaluation Pending
05 Jun 2023Editorial Decision: Revise Minor
14 Jul 20231st Revision Received
17 Jul 2023Assigned to Editor
17 Jul 2023Submission Checks Completed
17 Jul 2023Reviewer(s) Assigned
24 Jul 2023Review(s) Completed, Editorial Evaluation Pending
16 Aug 2023Editorial Decision: Revise Minor
20 Aug 20232nd Revision Received
21 Aug 2023Assigned to Editor
21 Aug 2023Submission Checks Completed
22 Aug 2023Review(s) Completed, Editorial Evaluation Pending
25 Aug 2023Editorial Decision: Accept