Abstract
Lead generation refers to the identification of potential topics (the ‘leads’) of importance for journalists to report on. In this paper we present a new lead generation tool based on a microservice architecture, which includes a component of explainable AI. The lead generation tool collects and stores historical and real-time data from a web source, like Google Trends, and generates current and future leads. These leads are produced by an engine for hypothetical reasoning based on logical rules, which is a novel implementation of a recent theory. Finally, the leads are displayed on a web interface for end users, in particular journalists. This interface provides information on why a specific topic is or may become a lead, assisting journalists in deciding where to focus their attention. We carry out an empirical evaluation of the performance of our tool.
Work partially supported by Villum Fonden, grants no. 29518 and 50079, and the Independent Research Fund Denmark, grant no. 0135-00219.
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Notes
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- 2.
Rules have to be stratified, i.e., they cannot have circular dependencies [4].
- 3.
This rule captures the idea that if Peyton Manning and Tom Brady are both in the news, then it might be interesting to write an article about NFL Quarterbacks.
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We thank Narongrit Unwerawattana for his technical support.
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Cruz-Filipe, L., Kostopoulou, S., Montesi, F., Vistrup, J. (2023). \(\mu \)XL: Explainable Lead Generation with Microservices and Hypothetical Answers. In: Papadopoulos, G.A., Rademacher, F., Soldani, J. (eds) Service-Oriented and Cloud Computing. ESOCC 2023. Lecture Notes in Computer Science, vol 14183. Springer, Cham. https://doi.org/10.1007/978-3-031-46235-1_1
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