expectation maximization enhancement with evolutionstrategy for stochastic ontology mapping
This work presents an evolutionary algorithm for automatic ontology mapping, which attempts to map similar objects based on their hierarchical structures from an unclassified to a classified ontology. Alignment is performed by swapping branches between the two ontologies and comparing their similarities to find possible missing terms in the unclassified ontology. Our algorithm is a stochastic implementation of the expectation maximization (EM) algorithm, which attempts to find and insert possibly missing terms, and measures the resulting improvements through iterative E-steps and M-steps. Our approach evolves the E-step to find these terms, while the M-step maps the classified ontology onto the unclassified one. Only taxonomic information is evaluated. We extract high-level descriptions of low-level definitions, and create subsections of the search-spaces, and thus obtain a satisfactory representation of the entire search-space to sample.
expectation maximization ontology mapping heuristics self-adaptation stochastic algorithm