Semantic Web Analysis over the Nova Scotia Open Data

新斯科舍省开放数据的语义网络分析

• 批准号: RGPIN-2020-05869
• 负责人: Rajabi, Enayat
• 金额: $ 1.75万
• 依托单位: Cape Breton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746457
• 关键词: Semantic; Web; Analysis; over; Nova

Several governmental and provincial organizations have published data in different domains to the hope that such data are used and analyzed by a wide range of people, from professional statisticians to the lay public. Despite the emerging public attention on publishing open government data, the re-use of open data - from a semantic web point of view- is still in its infancy. Based on the Linked Data standard, open data should follow a 5-star development scheme and each entity in an open dataset should be discoverable. Existing provincial open data platforms in Canada, however, follow a 3-star development scheme. They publish data in raw formats without underlying precise semantics, while they are semantically related to each other. Lack of semantics in datasets leads to dataset isolation, hinders data enrichment, and causes poor search results. The long-term objective of this research program is designing a semantic web analytics platform over the Nova Scotia open data to a) achieve a 5-star development scheme for the open datasets and b) develop a query answering system. The 5-start scheme allows all the entities in an open dataset to be discoverable by the other datasets on the Web of Data. I will achieve this long¬-term objective with my HQP and through the following short¬-term objectives: a) collecting data from the Nova Scotia open data b) analyzing the datasets and their relationships c) transforming the collected data to the semantic web format by designing an ontology, and d) creating a knowledgebase and developing a semantic web search over the knowledgebase. To target each objective, HQP will be trained and supervised to acquire the knowledge of open data analytics, develop their research and professional skills within the program, and eventually implement a system that is defined by each objective. The research program is divided into four phases which include data collection, data analysis, data transformation and building a semantic web analytics application. A set of case studies (e.g., linking to the other provincial open data) will be implemented within the research program as a proof of concept. Providing a semantic web analytics platform over the Nova Scotia open data significantly impacts on the existing provincial open data in Canada, as it proposes a referenceable method that can be applied by all the open data websites. Provincial open data websites in Canada can follow the approach that will be presented in this research program, to make their data available in the semantic web format and link their datasets. Both the public sector and private companies will be highly motivated to utilize the outcome of this research program in order to search and discover new facts over the linked Nova Scotia datasets. Enriching an open data platform with internal and external datasets is an important step towards unifying all the open data in Canada.

一些政府和省级组织公布了不同领域的数据，希望这些数据能被从专业统计人员到普通公众的广泛人群使用和分析。尽管公众开始关注公开政府数据，但从语义网的角度来看，公开数据的再利用仍处于起步阶段。基于关联数据标准，开放数据应该遵循5-星星开发方案，并且开放数据集中的每个实体都应该是可扩展的。然而，加拿大现有的省级开放数据平台遵循三星级星星开发计划。它们以原始格式发布数据，而没有底层的精确语义，尽管它们在语义上彼此相关。数据集缺乏语义会导致数据集隔离，阻碍数据丰富，并导致搜索结果不佳。本研究计划的长期目标是在新斯科舍省的开放数据上设计一个语义Web分析平台，以a）实现开放数据集的五星级星星开发计划和B）开发一个查询应答系统。5-start方案允许开放数据集中的所有实体都可以被Web of Data上的其他数据集所替代。我将通过我的HQP和以下短期目标实现这一长期目标：a）从新斯科舍省的开放数据中收集数据B）分析数据集及其关系c）通过设计本体将收集的数据转换为语义网格式，d）创建知识库并在知识库上开发语义网搜索。为了实现每个目标，HQP将接受培训和监督，以获得开放数据分析的知识，在计划中发展他们的研究和专业技能，并最终实施由每个目标定义的系统。研究计划分为四个阶段，包括数据收集，数据分析，数据转换和建立一个语义Web分析应用程序。一组案例研究（例如，链接到其他省级开放数据）将在研究方案中实施，作为概念验证。在新斯科舍省开放数据上提供语义网络分析平台对加拿大现有的省级开放数据产生了重大影响，因为它提出了一种可供所有开放数据网站应用的可参考方法。加拿大的省级开放数据网站可以遵循本研究计划中提出的方法，以语义网格式提供数据并链接其数据集。公共部门和私营公司都将非常积极地利用这一研究方案的成果，以便在相关的新斯科舍省数据集上搜索和发现新的事实。利用内部和外部数据集丰富开放数据平台是统一加拿大所有开放数据的重要一步。