Probabilistic inductive logic programming : theory and applications - De Raedt, L. (Springer, 2017)
One of the key open questions within artificial intelligence is how to combine probability and logic with learning. This question is getting an increased at-tention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously, resulting in the newly emerging subfield known as statistical relational learning and probabilis-tic inductive logic programming. A major driving force is the explosive growth in the amount of heterogeneous data that is being collected in the business and scientific world. Example domains include bioinformatics, chemoinformat-ics, transportation systems, communication networks, social network analysis, link analysis, robotics, among others. The structures encountered can be as sim-ple as sequences and trees (such as those arising in protein secondary structure prediction and natural language parsing) or as complex as citation graphs, the World Wide Web, and relational databases.