使用语义丰富的大众分类法的自适应知识检索

Dimitrios Pappas, I. Paraskakis
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引用次数: 0

摘要

组织在个性化系统上投入了大量的开发工作,以获得知识管理的好处。竞争和创新的需要意味着不断变化和新出现的需求,导致重新设计这些系统的代价高昂。灵活的知识管理方法应该通过适应组织的领域来实现组织的个性化,同时简化对未来知识检索需求的整合。我们描述了这样一种方法,它以知识管理平台为核心,支持添加和删除多个大众分类法,不断地用语义丰富它们,并提供特定于领域的建议和语义搜索功能。作为HOMER项目的一部分,这种知识检索方法已应用于自制爆炸物和反恐工作领域。从最终用户-执法、安全和相关机构-的角度提出了初步评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive knowledge retrieval using semantically enriched folksonomies
Organizations invest considerable development effort in personalized systems to reap the benefits of knowledge management. Competition and the need for innovation imply in ever-changing and emerging needs that result in costly effort to redesign such systems. A flexible knowledge management approach should achieve personalization for organizations by adapting to the domain of the organization, meanwhile simplifying the incorporation of future knowledge retrieval needs. We describe such an approach that employs a knowledge management platform at its core, which supports adding and removing multiple folksonomies, constantly enriches them with semantics and provides domain-specific recommendations and semantic search capabilities. This approach to knowledge retrieval has been applied to the domain of homemade explosives and counter-terrorism efforts as part of the HOMER project. Preliminary evaluation from the perspective of the end-users - law enforcement, security and related agencies - is presented.
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