Connecting the dots for contextual information retrieval - LO, Pei-Chi (Institutional Knowledge at Singapore Management University, 2023)
There are many information retrieval tasks that depend on knowledge graphs to return contextually relevant result of the query. We call them Knowledgeenriched Contextual Information Retrieval (KCIR) tasks and these tasks come in many different forms including query-based document retrieval, query answering and others. These KCIR tasks often require the input query to contextualized by additional facts from a knowledge graph, and using the context representation to perform document or knowledge graph retrieval and prediction. In this dissertation, we present a meta-framework that identifies Contextual Representation Learning (CRL) and Contextual Information Retrieval (CIR) to be the two key components in KCIR tasks. We then address three research tasks related to the two KCIR components. In the first research task, we propose a VAE-based contextual representation learning method using a co-embedding attributed network structure that co-embeds knowledge and query context in the same vector space. The model shows superior downstream prediction accuracy compared to other baseline models using VAE with or without using external knowledge graph. Next, we address the research task of solving a novel IR problem known as Contextual Path Retrieval (CPR). In this task, a knowledge graph path relevant to a given query and a pair of head and tail entities is to be retrieved from the background knowledge graph. We develop a transformer-based model consisting of context encoder and path encoder to solve the CPR task. Our proposed models which include the proposed two encoders show promising ability to retrieve contextual paths. Finally, we address the Contextual Path Generation (CPG) task which issimilar to CPR except that the knowledge graph path to be returned may require inferred relation edges since most knowledge graphs are incomplete in their coverage. For the CPG task, we propose both monotonic and non-monotonic approaches to generate contextual paths. Our experiment results demonstrate that the non-monotonic approach yields better-quality resultant knowledge graph paths.