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 problem and demonstrate superior performance relative to another state-of-the-art implementation, and present 10-fold cross-validated accuracy results which are comparable with those from a variety of other methods. 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.
The original version of this chapter was revised: The authors corrected errors (mainly in Section 5) regarding the results reported for OWL-Miner and DL-Learner. An erratum to this chapter can be found at https://doi.org/10.1007/978-3-319-70682-5_24
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Ratcliffe, D., Taylor, K. (2017). Refinement-Based OWL Class Induction with Convex Measures. In: Wang, Z., Turhan, AY., Wang, K., Zhang, X. (eds) Semantic Technology. JIST 2017. Lecture Notes in Computer Science(), vol 10675. Springer, Cham. https://doi.org/10.1007/978-3-319-70682-5_4
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