@inproceedings{28cfc328c9db43f49f3d6875a1fe0eef,
title = "Refinement-based OWL class induction with convex measures",
abstract = "Beam-search may be used to iteratively explore and evaluate refinements of candidate hypotheses expressed in logical formalisms such as description logic. In this paper, we analyse heuristics for beam search methods over OWL classes and present a novel search algorithm, OWL-Miner which leverages the properties of convex measure functions to deliver an improved memory-bounded beam search. We present performance results on the mutagenesis benchmark that demonstrate world-best 10-fold cross-validated accuracy. Our improvements to the space and time-based efficiency of refinement-based learning algorithms are significant for expanding the size of learning problems that can be feasibly addressed by refinement learning and the quality of solutions that can be found with limited resources.",
author = "David Ratcliffe and Kerry Taylor",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 7th Joint International Conference on Semantic Technology, JIST 2017 ; Conference date: 10-11-2017 Through 12-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70682-5_4",
language = "English",
isbn = "9783319706818",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "49--65",
editor = "Zhe Wang and Kewen Wang and Anni-Yasmin Turhan and Xiaowang Zhang",
booktitle = "Semantic Technology - 7th Joint International Conference, JIST 2017, Proceedings",
address = "Germany",
}