Head Concepts Selection for Verbose Medical Queries Expansion

Mohammed Maree,Mohammed Belkhatir,Israa Noor,Khaled S Rabayah,Saadat M Alhashmi

doi:10.1109/access.2020.2987568

Abstract

Semantic concepts and relations encoded in domain-specific ontologies and other medical semantic resources play a crucial role in deciphering terms in medical queries and documents. The exploitation of these resources for tackling the semantic gap issue has been widely studied in the literature. However, there are challenges that hinder their widespread use in real-world applications. Among these challenges is the insufficient knowledge individually encoded in existing medical ontologies, which is magnified when users express their information needs using long-winded natural language queries. In this context, many of the users query terms are either unrecognized by the used ontologies, or cause retrieving false positives that degrade the quality of current medical information search approaches. In this article, we explore the combination of multiple extrinsic semantic resources in the development of a full-fledged medical information search framework to: i) highlight and expand head medical concepts in verbose medical queries (i.e. concepts among query terms that significantly contribute to the informativeness and intent of a given query), ii) build semantically enhanced inverted index documents, iii) contribute to a heuristical weighting technique in the query document matching process. To demonstrate the effectiveness of the proposed approach, we conducted several experiments over the CLEF eHealth 2014 dataset. Findings indicate that the proposed method combining several extrinsic semantic resources proved to be more effective than related approaches in terms of precision measure.

Highlights

To generic web-based search queries that tend to be short [1], medical queries are long-winded with a reported average length of five terms when examining the query log of an Electronic Health Record search engine.1 their processing through statistical techniques alone appears insufficient since they encompass several domain-specific medical concepts [2], [3] that require making use of extrinsic knowledge for their deciphering [4]
Medical Information Retrieval (MIR) systems have shifted to exploiting medical semantic resources and ontologies in an attempt to capture knowledge in this domain through formally and explicitly defining medical concepts, instances, as well as semantic and taxonomic relations that link related concepts
This challenge is manifested by the diversity of users, their information needs and their background knowledge in the medical domain [6], [13], [14]. Addressing each of these challenges plays a crucial role in the way medical query processing and expansion techniques are developed, and has a direct impact on the quality of the retrieved results by MIR systems [15]–[18]. Starting from this position, we propose a semantics-based MIR system that aims to improve the quality of the returned results through incorporating multiple medical semantic resources and query expansion techniques

Summary

Introduction

To generic web-based search queries that tend to be short [1], medical queries are long-winded with a reported average length of five terms when examining the query log of an Electronic Health Record search engine. their processing through statistical techniques alone appears insufficient since they encompass several domain-specific medical concepts [2], [3] that require making use of extrinsic knowledge for their deciphering [4]. To generic web-based search queries that tend to be short [1], medical queries are long-winded with a reported average length of five terms when examining the query log of an Electronic Health Record search engine.1 Their processing through statistical techniques alone appears insufficient since they encompass several domain-specific medical concepts [2], [3] that require making use of extrinsic knowledge for their deciphering [4]. MIR systems have shifted to exploiting medical semantic resources and ontologies in an attempt to capture knowledge in this domain through formally and explicitly defining medical concepts, instances, as well as semantic and taxonomic relations that link related concepts.

Objectives

Methods

Conclusion