Semantics-driven Information Retrieval

本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，用于对网络文档和查询的自然语言内容进行语义分析。语义感知检索方法将系统地研究如何解决基于关键字的检索系统中普遍存在的词汇缺口问题。通过这个研究项目，我们将开发将查询和文档链接到知识图子图的技术，从而最大限度地保留它们的语义并最小化主题漂移。我们将开发基于KG子图估计查询文档相关性的方法。我们还将探索查询性能预测方法，该方法根据知识图信息的利用或缺乏来估计查询的性能，以便在每个查询的基础上决定知识图的效用。

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.