@inbook{e8184a4375e34e6ca69a294bdeafb917,
title = "μ XL: Explainable Lead Generation with Microservices and Hypothetical Answers",
abstract = "Lead generation refers to the identification of potential topics (the {\textquoteleft}leads{\textquoteright}) 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.",
keywords = "Explainable AI, Lead generation, Microservices",
author = "Lu{\'i}s Cruz-Filipe and Sofia Kostopoulou and Fabrizio Montesi and Jonas Vistrup",
note = "Funding Information: Work partially supported by Villum Fonden, grants no. 29518 and 50079, and the Independent Research Fund Denmark, grant no. 0135-00219.; 10th IFIP WG 6.12 European Conference on Service-Oriented and Cloud Computing, ESOCC 2023 ; Conference date: 24-10-2023 Through 25-10-2023",
year = "2023",
month = oct,
day = "12",
doi = "10.1007/978-3-031-46235-1\_1",
language = "English",
volume = "14183",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature Switzerland",
pages = "3--18",
booktitle = "Service-Oriented and Cloud Computing. ESOCC 2023.",
}