Abstract
Knowledge discovery, as an area focusing upon methodologies for extracting knowledge through deduction (a priori) or from data (a posteriori), has been largely studied in Database and Artificial Intelligence. Deductive reasoning such as logic reasoning gains logically knowledge from pre-established (certain) knowledge statements, while inductive inference such as data mining or learning discovers knowledge by generalising from initial information. While deductive reasoning and inductive learning are conceptually addressing knowledge discovery problems from different perspectives, they are inference techniques that nicely complement each other in real-world applications. In this chapter we will present how techniques from machine learning and reasoning can be reconciled and integrated to address large scale problems in the context of (i) transportation in cities of Bologna, Dublin, Miami, Rio and (ii) spend optimisation in finance.
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Prediction part of the live IBM STAR-CITY system (http://208.43.99.116:9080/simplicity/demo.jsp).
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Lecue, F. (2017). Applying Machine Reasoning and Learning in Real World Applications. In: Pan, J., et al. Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering. Reasoning Web 2016. Lecture Notes in Computer Science(), vol 9885. Springer, Cham. https://doi.org/10.1007/978-3-319-49493-7_7
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