Relational Sequence Learning

Kristian Kersting, Luc De Raedt, Bernd Gutmann, Andreas Karwath, Niels Landwehr

Research output: Chapter in Book/Report/Conference proceedingChapter

10 Citations (Scopus)


Sequential behavior and sequence learning are essential to intelligence. Often the elements of sequences exhibit an internal structure that can elegantly be represented using relational atoms. Applying traditional sequential learning techniques to such relational sequences requires one either to ignore the internal structure or to live with a combinatorial explosion of the model complexity. This chapter briefly reviews relational sequence learning and describes several techniques tailored towards realizing this, such as local pattern mining techniques, (hidden) Markov models, conditional random fields, dynamic programming and reinforcement learning.
Original languageEnglish
Title of host publicationProbabilistic Inductive Logic Programming - Theory and Applications
Place of PublicationBerlin Heidelberg, Germany
PublisherSpringer Verlag
Number of pages28
ISBN (Print)978-3-540-78651-1
Publication statusPublished - 2008


  • inductive logic programming, machine learning, relational learning, scientific knowledge


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