基于数据聚类的语义元数据对模型评价
Evaluation of Semantic Metadata Pair Modelling Using Data Clustering
摘要：
元数据为两个数据集之间的相关性的连接、阐述、检查和理解提供了一种媒介。元数据可以被丰富以计算不同分解数据集之间的连接的存在。为了做到这一点，第一个任务是获得域的通用元数据表示。这种表示缩小了元数据搜索空间。元数据搜索空间由属性、标签、语义内容、注释等组成以执行分类。现有技术限制了元数据带宽，即，用于匹配目的的操作集受到限制或限定。本研究的重点是生成一个映射函数称为同源，可以找到基于对分解数据集之间的属性的数学相关性。每一对都是使用现有的元数据和可用的元标签从所考虑的数据集之一设计的。在生成对之后，使用不同的对组合来构造样本。两个或多个对之间的相似性和相关性通过使用数据聚类技术来获得，以基于相似性指数从较小的组生成大的组。使用域划分器函数划分搜索空间，并使用相关性和标记作为主要概念创建较小的搜索空间。对于这项研究，最初的数据集仅限于文本信息。一旦生成了所有不相交的元集合，近似算法计算每个元集合的中心。这些中心服务于元指针的目的，即元域表示的集合。然后，每个指针可以基于内容（即元内容）加入集群。它还促进了跨功能域的可能同义词的处理。这可以使用元指针和图池来检查。
英文摘要：
Metadata presents a medium for connection, elaboration, examination, and comprehension of relativity between two datasets. Metadata can be enriched to calculate the existence of a connection between different disintegrated datasets. In order to do so, the very first task is to attain a generic metadata representation for domains. This representation narrows down the metadata search space. The metadata search space consists of attributes, tags, semantic content, annotations etc. to perform classification. The existing technologies limit the metadata bandwidth i.e. the operation set for matching purposes is restricted or limited. This research focuses on generating a mapper function called cognate that can find mathematical relevance based on pairs of attributes between disintegrated datasets. Each pair is designed from one of the datasets under consideration using the existing metadata and available meta-tags. After pairs have been generated, samples are constructed using a different combination of pairs. The similarity and relevance between two or more pairs are attained by using a data clustering technique to generate large groups from smaller groups based on similarity index. The search space is divided using a domain divider function and smaller search spaces are created using relativity and tagging as the main concept. For this research, the initial datasets have been limited to textual information. Once all disjoint meta-collection have been generated the approximation algorithm calculates the centers of each meta-set. These centers serve the purpose of meta-pointers i.e. a collection of meta-domain representations. Each pointer can then join a cluster based on the content i.e. meta-content. It also facilitates the process of possible synonyms across cross-functional domains. This can be examined using meta-pointers and graph pools.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
