A Comparison of Reasoning Techniques for Querying Large Description Logic ABoxes
A Comparison of Reasoning Techniques for Querying Large Description Logic ABoxes
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DOI：
10.1007/11916277_16
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发表时间：
2006-11
期刊：
影响因子：
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通讯作者：
B. Motik;U. Sattler
中科院分区：
文献类型：
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作者：
B. Motik;U. Sattler
描述逻辑（dl）的许多现代应用程序需要回答根据相对简单的本体构建的大量数据的查询。对于这样的应用程序，我们推测重用演绎数据库的思想可能会提高深度学习系统的可伸缩性。因此，在我们之前的工作中，我们开发了一种算法，用于将DL知识库简化为析取数据程序。为了验证我们的猜想，我们在一个新的深度学习推理器KAON2中实现了我们的算法，我们在本文中对此进行了描述。此外，我们创建了一个全面的测试套件，并使用它来进行性能评估。结果表明，在具有大box和简单tbox的知识库上，我们的技术确实表现出良好的性能；相比之下，在具有大型和复杂tbox的知识库上，现有技术仍然表现更好。这让我们对这两种方法的优缺点有了更深入的了解。
Many modern applications of description logics (DLs) require answering queries over large data quantities, structured according to relatively simple ontologies. For such applications, we conjectured that reusing ideas of deductive databases might improve scalability of DL systems. Hence, in our previous work, we developed an algorithm for reducing a DL knowledge base to a disjunctive datalog program. To test our conjecture, we implemented our algorithm in a new DL reasoner KAON2, which we describe in this paper. Furthermore, we created a comprehensive test suite and used it to conduct a performance evaluation. Our results show that, on knowledge bases with large ABoxes but with simple TBoxes, our technique indeed shows good performance; in contrast, on knowledge bases with large and complex TBoxes, existing techniques still perform better. This allowed us to gain important insights into strengths and weaknesses of both approaches.