Semantics-driven Information Retrieval

语义驱动的信息检索

• 批准号: RGPIN-2020-05429
• 负责人: Ensan, Faezeh
• 金额: $ 1.75万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740600
• 关键词: Semantics; driven; Information; Retrieval

The objective of this research program is to develop semantics-aware information retrieval mechanisms that would benefit from the structured knowledge represented in knowledge graphs for the semantic analysis of natural language content of web documents and queries. A semantics-aware retrieval approach would systematically study how challenges such as the vocabulary gap problem prevalent in keyword-based retrieval systems, can be addressed. Through this research program, we will develop techniques for linking queries and documents to knowledge graph subgraphs such that their semantics of are maximally preserved and topic drift is minimized. We will develop methods for estimating query-document relevance based on KG subgraphs. We will also explore query performance prediction methods that estimate the performance of a query subject to the utilization of knowledge graph information, or lack thereof, for deciding on the utility of knowledge graphs on a query by query basis. The primary outcome of this research program will be to develop next generation semantics-aware information retrieval methods that will be adopted by the growing semantic search industry. The outcomes can be utilized by domain-specific search engines and recommendation systems such as job recommendation and online marketing solutions, as well as those domains which focus on meta-analysis of large-scale document corpora as adopted in the medical and healthcare, as well as legal document analysis domains. The proposed research program will also provide a solid foundation for the training of highly qualified personnel including three PhD and 6 MASc students who will gain expertise in information retrieval, knowledge engineering, and semantic technologies and can be immediately absorbed the lively and growing data science and analytics industry in Canada.

这项研究计划的目标是开发语义感知的信息检索机制，将受益于结构化的知识表示在知识图的Web文档和查询的自然语言内容的语义分析。语义感知检索方法将系统地研究如何解决基于关键词的检索系统中普遍存在的词汇缺口问题等挑战。通过这项研究计划，我们将开发将查询和文档链接到知识图子图的技术，以便最大限度地保留它们的语义，并最大限度地减少主题漂移。我们将开发基于KG子图估计查询文档相关性的方法。我们还将探讨查询性能预测方法，估计性能的查询知识图信息的利用率，或缺乏，以决定对查询的基础上的查询知识图的效用。 这项研究计划的主要成果将是开发下一代语义感知的信息检索方法，将通过不断增长的语义搜索行业。这些结果可以被特定领域的搜索引擎和推荐系统（如工作推荐和在线营销解决方案）以及那些专注于医疗和保健中采用的大型文档语料库的元分析的领域以及法律的文档分析领域所利用。拟议的研究计划还将为培养高素质人才提供坚实的基础，包括三名博士和六名MASc学生，他们将获得信息检索，知识工程和语义技术方面的专业知识，并可以立即被加拿大活跃和不断增长的数据科学和分析行业所吸收。