Relational Sequence Alignment

The need to measure sequence similarity arises in information extraction, music mining, biological sequence analysis, and other domains, and often coincides with sequence alignment: the more similar two sequences are, the better they can be aligned. Aligning sequences not only shows how similar sequences are, it also shows where there are differences and correspondences between the sequences. Traditionally, the alignment has been considered for sequences of flat symbols only. Many real world sequences such as protein secondary structures, however, exhibit a rich internal structures. This is akin to the problem of dealing with structured examples studied in the field of inductive logic programming (ILP). In this paper, we propose to use wellestablished ILP distance measures within alignment methods. Although straight-forward, our initial experimental results show that this approach performs well in practice and is worth to be explored.