Classifying and querying very large taxonomies with bit-vector encoding

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• 作者：Hassan Aït-Kaci ; Samir Amir
• 关键词：Binary encoding ; Taxonomic reasoning ; Query optimization ; Semantic web
• 刊名：Journal of Intelligent Information Systems
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：48
• 期：1
• 页码：1-25
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Information Storage and Retrieval; Data Structures, Cryptology and Information Theory; Artificial Intelligence (incl. Robotics); IT in Business; Document Preparation and Text Processing;
• 出版者：Springer US
• ISSN：1573-7675
• 卷排序：48

文摘

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.