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Technique for Transformation of DL Knowledge Base to Boolean Representation

机译:DL知识库到布尔表示的转换技术

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Knowledge Cartography (KC) allows for fast answering of Description Logic (DL) knowledge base queries, but requires expensive preprocessing to represent knowledge in internal representation, i.e., the algorithm for computation of map of concepts as binary signatures is exponential time (however, for taxonomies-as many practical cases have shown-it is at most quadratic time). Preprocessing is already part of DL reasoning process and some computations are pre-calculated before user issues query to knowledge base. Using another method-Tableaux, no knowledge preprocessing is performed, however, all reasoning is done after user issues query. That's why KC is faster than Tableaux during query answering. The chapter focuses on preprocessing issue for KC. It mainly considers the research on efficient generation of binary signatures and signatures rebuilding by employing the methods for logic synthesis. It has been confirmed that logic synthesis Complement algorithm is efficient when applied to the construction of the map of concepts. The research has shown that strategy of construction should be adjusted depending on ontology size. For smaller ontologies-the non-recursive approach should be used, on the contrary-for larger ontologies-recursive approach with bi-partitioning of the ontology graph. The recursive procedure indicated good scaling for large taxonomies. Another observation was that Complement algorithm works faster for non-sorted CNFs.
机译:知识制图(KC)可以快速回答描述逻辑(DL)知识库查询,但需要进行昂贵的预处理才能以内部表示形式表示知识,即,作为二进制签名的概念图计算算法是指数时间(但是,对于分类法-如许多实际案例所示-最多是二次时间)。预处理已经是DL推理过程的一部分,并且在用户向知识库发出查询之前预先进行了一些计算。使用另一种方法-Tableaux,不执行知识预处理,但是,所有推理都在用户发出查询后完成。这就是查询查询期间KC比Tableaux更快的原因。本章重点介绍KC的预处理问题。它主要考虑采用逻辑综合方法有效地生成二进制签名和重新生成签名的研究。已经证实,将逻辑综合补码算法应用于概念图的构造是有效的。研究表明,应根据本体大小调整构建策略。相反,对于较小的本体,应使用非递归方法,而对于较大的本体,则应使用对本体图进行双向划分的递归方法。递归过程表明大型分类法具有良好的扩展性。另一个观察结果是,补余算法对未分类的CNF更快地起作用。

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