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: 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=752547
• 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.

该研究计划的目标是开发语义感知的信息检索机制，该机制将受益于知识图中表示的结构化知识，用于Web文档和查询的自然语言内容的语义分析。语义感知的检索方法将系统地研究如何解决基于关键字的检索系统中普遍存在的词汇缺口问题等挑战。通过这个研究计划，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子图的查询-文档相关性估计方法。我们还将探索查询性能预测方法，这些方法在使用或缺少知识图信息的情况下估计查询的性能，以便在逐个查询的基础上决定知识图的效用。这一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，这些方法将被日益增长的语义搜索行业采用。这些结果可用于特定领域的搜索引擎和推荐系统，如求职推荐和在线营销解决方案，以及那些专注于医疗保健中采用的大规模文档语料库的元分析的领域，以及法律文档分析领域。拟议的研究计划还将为培养高素质的人才奠定坚实的基础，其中包括3名博士和6名硕士研究生，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且不断增长的数据科学和分析行业所吸收。