Classifying and querying very large taxonomies with bit-vector encoding

摘要

In this article, we address the question of how efficiently Semantic Web (SW) reasoners perform in processing (classifying and querying) taxonomies of enormous size and whether it is possible to improve on existing implementations. We use a bit-vector encoding technique to implement taxonomic concept classification and Boolean-query answering. We describe the technique we have used, which achieves high performance, and discuss implementation issues. We compare the performance of our implementation with those of the best existing SW reasoning systems over several very large taxonomies under the exact same conditions for so-called TBox reasoning. The results show that our system is among the best for concept classification and several orders-of-magnitude more efficient in terms of response time for query answering. We present these results in detail and comment them. We also discuss pragmatic issues such as cycle detection and decoding.