Knowledge Graphs in Information Retrieval

Abstract

This paper introduces an information retrieval model that leverages knowledge graphs, specifically tailored for Clinical Trials. In these scenarios, the document in question takes the form of a semi-structured clinical trial, containing details about enrolled patients, descriptions of experiments and procedures conducted during the trial, relevant diseases, and specific enrollment criteria. While the document retains a semi-structured format, the majority of the information is expressed in natural language. Queries in this context consist of specific patient characteristics, such as disease type, genetic information, and demographic data. The primary aim of this paper is to develop and utilize a knowledge graph capable of storing this information, including links to external resources like the Disease Ontology. We propose an Object-Relational model, which is then transformed into a knowledge graph. This graph is subsequently employed to identify semantic connections between concepts present in the clinical trials and those in the queries. These connections are then utilized to formulate a retrieval model for each aspect of the query. To achieve this, we design a relevance formula that incorporates weights to account for ontological relationships between concepts. We evaluate the effectiveness of our model by comparing the results with manual annotations.