Modeling Semantic Heterogeneity in Dataspace: A Machine Learning Approach

Mrityunjay Singh, S. Jain, V. Panchal
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引用次数: 2

Abstract

A data space system facilitates a new way for sharing and integrating the information among the various distributed, autonomous and heterogeneous data sources. To provide the best effort answer of a user query, a data space system needs to resolve the semantic heterogeneity in its core. There are many solutions being proposed to address this problem widely. We are exploring the problem of semantic heterogeneity in a data space system as a part of our PhD work. In this paper, we have addressed the semantic heterogeneity in the context of a data space system, and presented an abstract framework to model the semantic heterogeneity in data space. The proposed model is based on machine learning and ontology approaches. The machine learning technique analyzes the semantically equivalent data items (or entities) in data space, and the ontology conceptualizes the structural entities in a data space. This model resolves the semantic heterogeneity of a data space system, and creates a conceptual model using "from-data-to-schema" approach. The proposed approach implicitly creates the domain ontology by finding the most similar concepts comming from different data sources and enriches the performance of the system by finding the semantic relationships among them.
数据空间语义异构建模:一种机器学习方法
数据空间系统为各种分布式、自治和异构数据源之间的信息共享和集成提供了一种新的方式。为了提供用户查询的最佳答案,数据空间系统需要解决其核心的语义异构问题。为了广泛解决这个问题,人们提出了许多解决方案。我们正在探索数据空间系统中的语义异构问题,作为我们博士工作的一部分。本文讨论了数据空间系统中的语义异构问题,并提出了一个抽象框架来对数据空间中的语义异构进行建模。该模型基于机器学习和本体方法。机器学习技术对数据空间中语义等价的数据项(或实体)进行分析,本体对数据空间中的结构实体进行概念化。该模型解决了数据空间系统的语义异构问题,并使用“从数据到模式”的方法创建了概念模型。该方法通过寻找来自不同数据源的最相似的概念来隐式地创建领域本体,并通过寻找它们之间的语义关系来丰富系统的性能。
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