Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Alignment of trees: an alternative to tree edit
Theoretical Computer Science
Distance Between Herbrand Interpretations: A Measure for Approximations to a Target Concept
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Relational IBL in classical music
Machine Learning
Gradient boosting for sequence alignment
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
TildeCRF: conditional random fields for logical sequences
ECML'06 Proceedings of the 17th European conference on Machine Learning
CONTRAlign: discriminative training for protein sequence alignment
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
Enhancing language learning and translation with ubiquitous applications
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
Pervasive language learning on modern mobile devices
Journal of Mobile Multimedia
| Hi-index | 0.00 |
The need to measure sequence similarity arises in many applicitation 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 natural language sentences and protein secondary structures, however, exhibit 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 introduce Real, which is a powerful, yet simple approach to align sequence of structured symbols using well-established ILP distance measures within traditional alignment methods. Although straight-forward, experiments on protein data and Medline abstracts show that this approach works well in practice, that the resulting alignments can indeed provide more information than flat ones, and that they are meaningful to experts when represented graphically.