Using Concept Lattices to Reconcile Semantic Heterogeneity in Data

在现代信息系统中，数据捕获通常不能由旨在执行共同含义的传统组织机制来密切控制。此外，数据不再仅用于既定和预定的目的。事实上，大数据和开放数据的关键承诺之一是有机会将数据重新用于收集和存储数据的系统设计时没有预料到的用途。这种日益多样化的信息格局在以保留底层数据的语义（含义）的方式集成或组合来自各种来源的信息方面带来了重大挑战。集成的困难来自于源的独立性，导致源之间的高度语义异构性。实现高质量的语义集成对于充分利用大数据提供的机会来支持决策至关重要。

In modern information systems, data capture often cannot be closely controlled by traditional organizational mechanisms intended to enforce common meaning. Additionally, data are no longer used only for established and predetermined purposes. Indeed, one of the key promises of Big Data and Open Data is the opportunity to repurpose data for uses that were not anticipated when systems to collect and store the data were designed. This increasingly diverse information landscape creates significant challenges in integrating or combining information from various sources in ways that retain the semantics (meaning) of the underlying data. Integration difficulties arise from the independence of sources, resulting in a high level of semantic heterogeneity between sources. Achieving high-quality semantic integration is essential to take full advantage of the opportunities afforded by Big Data to support decision-making.