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严密控制。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环境在以保留底层数据的语义（含义）的方式集成或组合来自不同来源的信息方面带来了重大挑战。源的独立性导致了集成困难，从而导致源之间的语义高度异构。实现高质量的语义整合对于充分利用大数据提供的机会来支持决策至关重要。***提出的研究引入了“概念格”的概念，作为一种机制，为可用于集成异构独立源的数据提供语义。在概念格中，节点是概念，被解释为应用于现象（实例）的谓词（例如，Male(John)）。有向弧线表示两个节点之间的“优先”关系；也就是说，拥有一种财产意味着拥有另一种财产（例如，选民（约翰）- b>成年人（约翰））。在这个框架中，节点的语义完全由进入和离开节点的弧线的模式（以及弧线之间的关系）决定。与依赖于“重量级”模型（全局模式或中介模式）的传统方法相比，概念格是一个领域的“轻量级”概念模型。节点既可以被视为类，也可以被视为属性，这取决于传入和传出的弧线的模式。这种语义相对主义有利于概念格的整合，一个概念在一个格中可能是类，但在另一个格中可能是属性。此外，来自异构源的两个语义等效节点可能具有不同的表现形式，表明对属性值的不同语义解释。***拟议的研究计划将建立在我早期工作的基础上，重点关注四个目标，与研究生和学术同事合作。首先，我们将正式定义概念晶格结构的核心元素，并开发导航晶格的推理机制。其次，我们将设计和实现一个原型来存储和处理概念格。第三，我们将使用概念格作为基础，通过定义“合并节点”来整合跨异构源的信息——这些节点来自具有相同含义的独立格，可用于组合格。第四，我们将评估概念格在改进异构数据源信息检索方面的有效性

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. ***The proposed research introduces the notion of "concept lattice" as a mechanism to provide semantics for data that can be used to integrate heterogeneous independent sources. In a concept lattice, nodes are concepts, interpreted as predicates applied to phenomena (instances) (e.g., Male(John)). A directed arc indicates a "precedence" relationship between two nodes; that is, possessing one property implies possessing another (e.g., Voter(John) -> Adult(John)). In this framework, the semantics of a node is entirely determined by the pattern of (and relationships among) arcs entering and leaving it.***A concept lattice is a "lightweight" conceptual model of a domain, in contrast with traditional approaches that rely on "heavyweight" models (global or mediated schemas). Nodes may be considered either classes or properties, depending only on the pattern of incoming and outgoing arcs. This semantic relativism facilitates integration of concept lattices, where a concept may be a class in one lattice but a property in another. Moreover, two semantically equivalent nodes from heterogeneous sources can have different manifestations, indicating different semantic interpretations of the values of a property.***The proposed research program will build on my earlier work by focusing on four objectives, working with graduate students and academic colleagues. First, we will formally define the core elements of a concept lattice structure, and develop reasoning mechanisms to navigate lattices. Second, we will design and implement a prototype to store and process concept lattices. Third, we will use concept lattices as a foundation to integrate information across heterogeneous sources by defining "merge nodes" - nodes from independent lattices that carry the same meaning and can be used to combine lattices. Fourth, we will evaluate the effectiveness of concept lattices in improving information retrieval from heterogeneous data sources.**