KR是首屈一指的知识表示和推理国际会议。与一般AI会议相比,KR会议系列为研究人员研究由推理算法操纵的知识的显式表示提供了更为私密的环境,这为从事人工智能,计算机科学和软件工程的大量工作提供了重要基础。会议强调了知识表示和推理的理论原理以及这些原理及其在工作系统中的实施方式之间的关系。 官网地址:http://dblp.uni-trier.de/db/conf/kr/

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We present Knowledge Rocks, an implementation strategy and guideline for augmenting visualization systems to knowledge-assisted visualization systems, as defined by the KAVA model. Visualization systems become more and more sophisticated. Hence, it is increasingly important to support users with an integrated knowledge base in making constructive choices and drawing the right conclusions. We support the effective reactivation of visualization software resources by augmenting them with knowledge-assistance. To provide a general and yet supportive implementation strategy, we propose an implementation process that bases on an application-agnostic architecture. This architecture is derived from existing knowledge-assisted visualization systems and the KAVA model. Its centerpiece is an ontology that is able to automatically analyze and classify input data, linked to a database to store classified instances. We discuss design decisions and advantages of the KR framework and illustrate its broad area of application in diverse integration possibilities of this architecture into an existing visualization system. In addition, we provide a detailed case study by augmenting an it-security system with knowledge-assistance facilities.

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