an article by Nassim Abdeldjallal Otmani and Malik Si-Mohammed (Mouloud Mammeri University of Tizi Ouzou, Algeria) and Catherine Comparot and Pierre-Jean Charrel (University Toulouse – Jean Jaurès, Toulouse, France) International Journal of Web Information Systems Volume 15 Issue 3 (2019)
Abstract
Purpose
The purpose of this study is to propose a framework for extracting medical information from the Web using domain ontologies. Patient–Doctor conversations have become prevalent on the Web. For instance, solutions like HealthTap or AskTheDoctors allow patients to ask doctors health-related questions. However, most online health-care consumers still struggle to express their questions efficiently due mainly to the expert/layman language and knowledge discrepancy. Extracting information from these layman descriptions, which typically lack expert terminology, is challenging. This hinders the efficiency of the underlying applications such as information retrieval. Herein, an ontology-driven approach is proposed, which aims at extracting information from such sparse descriptions using a meta-model.
Design/methodology/approach
A meta-model is designed to bridge the gap between the vocabulary of the medical experts and the consumers of the health services. The meta-model is mapped with SNOMED-CT to access the comprehensive medical vocabulary, as well as with WordNet to improve the coverage of layman terms during information extraction. To assess the potential of the approach, an information extraction prototype based on syntactical patterns is implemented.
Findings
The evaluation of the approach on the gold standard corpus defined in Task1 of ShARe CLEF 2013 showed promising results, an F-score of 0.79 for recognizing medical concepts in real-life medical documents.
Originality/value
The originality of the proposed approach lies in the way information is extracted. The context defined through a meta-model proved to be efficient for the task of information extraction, especially from layman descriptions.
Showing posts with label web_searching. Show all posts
Showing posts with label web_searching. Show all posts
Friday, 30 August 2019
Monday, 20 August 2012
A social inverted index for social-tagging-based information retrieval
an article by Kang-Pyo Lee, Hong-Gee Kim and Hyoung-Joo Kim (Seoul National University, South Korea) published in Journal of Information Science Volume 38 Number 4 (August 2012)
Abstract
Keywords have played an important role not only for searchers who formulate a query, but also for search engines that index documents and evaluate the query.
Recently, tags chosen by users to annotate web resources are gaining significance for improving information retrieval (IR) tasks, in that they can act as meaningful keywords bridging the gap between humans and machines.
One critical aspect of tagging (besides the tag and the resource) is the user (or tagger); there exists a ternary relationship among the tag, resource, and user. The traditional inverted index, however, does not consider the user aspect, and is based on the binary relationship between term and document.
In this paper we propose a social inverted index – a novel inverted index extended for social-tagging-based IR – that maintains a separate user sublist for each resource in a resource-posting list to contain each user’s various features as weights.
The social inverted index is different from the normal inverted index in that it regards each user as a unique person, rather than simply count the number of users, and highlights the value of a user who has participated in tagging. This extended structure facilitates the use of dynamic resource weights, which are expected to be more meaningful than simple user-frequency-based weights.
It also allows a flexible response to the conditional queries that are increasingly required in tag-based IR. Our experiments have shown that this user-considering indexing performs better in IR tasks than a normal inverted index with no user sublists.
The time and space overhead required for index construction and maintenance was also acceptable.
Abstract
Keywords have played an important role not only for searchers who formulate a query, but also for search engines that index documents and evaluate the query.
Recently, tags chosen by users to annotate web resources are gaining significance for improving information retrieval (IR) tasks, in that they can act as meaningful keywords bridging the gap between humans and machines.
One critical aspect of tagging (besides the tag and the resource) is the user (or tagger); there exists a ternary relationship among the tag, resource, and user. The traditional inverted index, however, does not consider the user aspect, and is based on the binary relationship between term and document.
In this paper we propose a social inverted index – a novel inverted index extended for social-tagging-based IR – that maintains a separate user sublist for each resource in a resource-posting list to contain each user’s various features as weights.
The social inverted index is different from the normal inverted index in that it regards each user as a unique person, rather than simply count the number of users, and highlights the value of a user who has participated in tagging. This extended structure facilitates the use of dynamic resource weights, which are expected to be more meaningful than simple user-frequency-based weights.
It also allows a flexible response to the conditional queries that are increasingly required in tag-based IR. Our experiments have shown that this user-considering indexing performs better in IR tasks than a normal inverted index with no user sublists.
The time and space overhead required for index construction and maintenance was also acceptable.
Saturday, 19 December 2009
Web searching by the “general public”: ...
an individual differences perspective
an article by Nigel Ford, Barry Eaglestone, Andrew Madden and Martin Whittle published in Journal of Documentation Volume 65 Issue 4 (2009)
Abstract
Purpose
The purpose of this paper is to explore the impact of a number of human individual differences on the web searching of a sample of the general public.
Design/methodology/approach
In total, 91 members of the general public performed 195 controlled searches. Search activity and ratings of search difficulty and success were recorded and statistically analysed. The study was exploratory, and sought to establish whether there is a prima facie case for further systematic investigation of the selection and combination of variables studied here.
Findings
Results revealed a number of interactions between individual differences, the use of different search strategies, and levels of perceived search difficulty and success. The findings also suggest that the open and closed nature of searches may affect these interactions. A conceptual model of these relationships is presented.
Practical implications
Better understanding of factors affecting searching may help one to develop more effective search support, whether in the form of personalised search interfaces and mechanisms, adaptive systems, training or help systems. However, the findings reveal a complexity and variability suggesting that there is little immediate prospect of developing any simple model capable of driving such systems.
Originality/value
There are several areas of this research that make it unique: the study’s focus on a sample of the general public; its use of search logs linked to personal data; its development of a novel search strategy classifier; its temporal modelling of how searches are transformed over time; and its illumination of four different types of experienced searcher, linked to different search behaviours and outcomes.
an article by Nigel Ford, Barry Eaglestone, Andrew Madden and Martin Whittle published in Journal of Documentation Volume 65 Issue 4 (2009)
Abstract
Purpose
The purpose of this paper is to explore the impact of a number of human individual differences on the web searching of a sample of the general public.
Design/methodology/approach
In total, 91 members of the general public performed 195 controlled searches. Search activity and ratings of search difficulty and success were recorded and statistically analysed. The study was exploratory, and sought to establish whether there is a prima facie case for further systematic investigation of the selection and combination of variables studied here.
Findings
Results revealed a number of interactions between individual differences, the use of different search strategies, and levels of perceived search difficulty and success. The findings also suggest that the open and closed nature of searches may affect these interactions. A conceptual model of these relationships is presented.
Practical implications
Better understanding of factors affecting searching may help one to develop more effective search support, whether in the form of personalised search interfaces and mechanisms, adaptive systems, training or help systems. However, the findings reveal a complexity and variability suggesting that there is little immediate prospect of developing any simple model capable of driving such systems.
Originality/value
There are several areas of this research that make it unique: the study’s focus on a sample of the general public; its use of search logs linked to personal data; its development of a novel search strategy classifier; its temporal modelling of how searches are transformed over time; and its illumination of four different types of experienced searcher, linked to different search behaviours and outcomes.
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