$$\mu $$XL: explainable lead generation with microservices and hypothetical answers

Abstract

AbstractLead generation refers to the identification of potential topics (the ‘leads’) of importance for journalists to report on. In this article we present $$\mu $$ μ XL, a new lead generation tool based on a microservice architecture that includes a component of explainable AI. $$\mu $$ μ XL collects and stores historical and real-time data from web sources, like Google Trends, and generates current and future leads. Leads are produced by a novel engine for hypothetical reasoning based on temporal logical rules, which can identify propositions that may hold depending on the outcomes of future events. This engine also supports additional features that are relevant for lead generation, such as user-defined predicates (allowing useful custom atomic propositions to be defined as Java functions) and negation (needed to specify and reason about leads characterized by the absence of specific properties). Our microservice architecture is designed using state-of-the-art methods and tools for API design and implementation, namely API patterns and the Jolie programming language. Thus, our development provides an additional validation of their usefulness in a new application domain (journalism). We also carry out an empirical evaluation of our tool.