Using Concept Lattices to Reconcile Semantic Heterogeneity in Data

使用概念格来协调数据中的语义异质性

• 批准号: RGPIN-2015-03929
• 负责人: Parsons, Jeffrey
• 金额: $ 1.31万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=612663
• 关键词: Using; Concept; Lattices; Reconcile; Semantic

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.

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。 拟议的研究引入了“概念格”的概念，作为一种机制，以提供语义的数据，可用于集成异构的独立源。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，男（约翰））。有向弧表示两个节点之间的“优先”关系;也就是说，拥有一个属性意味着拥有另一个属性（例如，选民（约翰）->成人（约翰））。在这个框架中，节点的语义完全由进入和离开它的弧的模式（以及弧之间的关系）决定。 概念格是领域的“轻量级”概念模型，与依赖于“重量级”模型（全局或中介模式）的传统方法相反。节点可以被认为是类或属性，这仅取决于传入和传出弧的模式。这种语义相对主义促进了概念格的整合，其中概念可以是一个格中的类，但在另一个格中是属性。此外，来自异构源的两个语义上等同的节点可以具有不同的表现形式，指示对属性的值的不同语义解释。 拟议的研究计划将建立在我早期的工作，重点是四个目标，与研究生和学术界的同事。首先，我们将正式定义概念格结构的核心元素，并开发推理机制来导航格。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”--来自独立格的节点，这些节点具有相同的含义，可以用于联合收割机格，来整合跨异构源的信息。第四，我们将评估概念格在改善异构数据源信息检索方面的有效性。