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Poset Ontologies and Concept Lattices as Semantic Hierarchies

机译:Poset本体和概念格作为语义层次

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We describe some aspects of our research in relational knowledge discovery and combinatorial scientific computing, with special emphasis on the relation to the research portfolio of the conceptual structures community. We have recently been developing a combinatorial approach to the management and analysis of large ontologies such as the Gene Ontology (GO). Our approach depends on casting the GO as a labeled partially ordered set (poset), and then using scores based on pseudo-distance measures which we have developed to categorize lists of labels (in the case of the GO, genes and gene products) concerning their clustering and depth within the GO. We hold that such taxonomic semantic hierarchies serve as the core conceptual structures underlying all ontological databases, and through this work we have developed a number of what we believe to be both fundamental and novel ideas about treating such large posets as data objects, in particular the nature of distance in such structures, and the nature of level as an interval-valued property. After laying out this basic framework, we can then bring these ideas to a particular kind of poset, namely the concept lattice. Considering a concept lattice as a poset, we are then prepared to develop techniques for anomaly detection in relational data by measuring the relative level of concepts vs. their cardinalities.
机译:我们描述了我们在关系知识发现和组合科学计算中研究的某些方面,并特别强调了与概念结构社区研究组合的关系。我们最近一直在开发一种组合方法来管理和分析大型本体,例如基因本体(GO)。我们的方法依赖于将GO强制转换为标记的部分有序集(姿势),然后使用基于伪距测度的分数,该测度已被开发出来,用于将涉及以下内容的标记(包括GO,基因和基因产物)列表进行分类它们在GO中的聚类和深度。我们认为,此类分类语义层次结构是所有本体数据库的基础概念核心结构,通过这项工作,我们已经开发出了一些我们认为是基本的和新颖的想法,可以将大型物态视为数据对象,特别是这种结构中距离的性质,以及级别作为间隔值属性的性质。在布局了这个基本框架之后,我们可以将这些思想带入一种特殊的姿势,即概念格。考虑到概念晶格作为摆放物,我们准备通过测量概念相对于基数的相对水平来开发用于关系数据中异常检测的技术。

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