Context-aware information retrieval on a ubiquitous medical learning environment

Martins, Diogo S.; Santana, Luiz Henrique Zambom; Biajiz, Mauro; Prado, Antonio Francisco do; Souza, Wanderley Lopes de

doi:10.1145/1363686.1364243

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…In the past several years, tremendous efforts have been made to develop context‐aware systems for health professionals, but disproportionally less attention has been paid to consumer‐oriented systems (Lopes & Ribeiro, ). The existing systems focusing on consumers rely mainly on three sources for contextualization: explicit feedback from users' interactions with a system, such as browsing results (Martins, Santana, Biajiz, do Prado, & de Souza, ), individuals' interests or personal health information (Luo & Tang, ), and medical thesauri, such as UMLS and MeSH (Zeng et al., ). The contextualization often takes place in two IR processes: indexing, and query operation and recommendation (Lopes & Ribeiro, ).…”

Section: Discussionmentioning

confidence: 99%

Toward a layered model of context for health information searching: An analysis of consumer‐generated questions

Zhang

2013

J Am Soc Inf Sci Tec

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Designing effective consumer health information systems requires deep understanding of the context in which the systems are being used. However, due to the elusive nature of the concept of context, few studies have made it a focus of examination. To fill this gap, we studied the context of consumer health information searching by analyzing questions posted on a social question and answer site: Yahoo! Answers. Based on the analysis, a model of context was developed. The model consists of 5 layers: demographic, cognitive, affective, situational, and social and environmental. The demographic layer contains demographic factors of the person of concern; the cognitive layer contains factors related to the current search task (specifically, topics of interest and information goals) and users' cognitive ability to articulate their needs. The affective layer contains different affective motivations and intentions behind the search. The situational layer contains users' perceptions of the current health condition and where the person is in the illness trajectory. The social and environmental layer contains users' social roles, social norms, and various information channels. Several novel system functions, including faceted search and layered presentation of results, are proposed based on the model to help contextualize and improve users' interactions with health information systems.

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Section: Discussionmentioning

confidence: 99%

Toward a layered model of context for health information searching: An analysis of consumer‐generated questions

Zhang

2013

J Am Soc Inf Sci Tec

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“…This integrates subject-keywords with domain-keywords to provide greater performance, but the specific sense in WordNet has to be chosen by the user. Martins et al (2008) used a domain-specific ontology for information retrieval on a ubiquitous medical learning environment. They employed an implicit relevance feedback approach using contextual information to build semantic indexes.…”

Section: Related Workmentioning

confidence: 99%

OGIR: an ontology‐based grid information retrieval framework

Hung

Tsai²,

Hung

et al. 2012

Online Information Review

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Purpose -A grid information retrieval model has benefits for sharing resources and processing mass information, but cannot handle conceptual heterogeneity without integration of semantic information. The purpose of this research is to propose a concept-based retrieval mechanism to catch the user's query intentions in a grid environment. This research re-ranks documents over distributed data sources and evaluates performance based on the user judgment and processing time. Design/methodology/approach -This research uses the ontology lookup service to build the concept set in the ontology and captures the user's query intentions as a means of query expansion for searching. The Globus toolkit is used to implement the grid service. The modification of the collection retrieval inference (CORI) algorithm is used for re-ranking documents over distributed data sources. Findings -The experiments demonstrate that this proposed approach successfully describes the user's query intentions evaluated by user judgment. For processing time, building a grid information retrieval model is a suitable strategy for the ontology-based retrieval model. Originality/value -Most current semantic grid models focus on construction of the semantic grid, and do not consider re-ranking search results from distributed data sources. The significance of evaluation from the user's viewpoint is also ignored. This research proposes a method that captures the user's query intentions and re-ranks documents in a grid based on the CORI algorithm. This proposed ontology-based retrieval mechanism calculates the global relevance score of all documents in a grid and displays those documents with higher relevance to users.

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“…According to [15], we refer to context as any information that can be used to characterise the situation of an entity, where an entity is a person, a place, or an object that is considered as relevant for the interaction between a user and an application. Context-awareness is a very common tool in medical information systems especially when combined with IR techniques: [22] enriches a standard IR engine for medical documents with the UMLS ontology 1 and a context model. The ontology is used as a reference index; the terms in the documents are linked to the resources of the ontology to increase the recall of the retrieval process, while the context-model is used to annotate the documents with meta-data about the context of the document's author.…”

Section: Related Workmentioning

confidence: 99%

“…In this system, the original query is forwarded to Google and the contextual data are used to further filter and rank the search results. In both [22] and [28], the context meta-data are biased toward either the document author's or the patient's perspective and they fail to represent other contexts of fruition of the information (e.g., the situation or the device capabilities). SAFE uses the information stored in the SAFE Card in a similar way, however its context-model, based on the one presented in [10], is much more expressive than those adopted in the previous two systems and can model all the necessary perspectives (i.e., situations) involving users, systems and applications.…”

Section: Related Workmentioning

confidence: 99%

Keyword-based, context-aware selection of natural language query patterns

Orsi

Tanca

Zimeo

2011

Proceedings of the 14th International Conference on Extending Database Technology

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Pervasive access to distributed data sources by means of mobile devices is becoming a frequent realistic operational context in many application domains. In these scenarios data access may be thwarted by the scarce knowledge that users have of the application and of the underlying data schemas and complicated by limited query interfaces, due to the small size of the devices.\ud \ud A viable solution to this problem could be expressing the queries in natural language; however, in applications like medical emergencies, data management systems must obey requirements such as very fast and precise data access which make this solution infeasible.\ud \ud To reduce the time needed to get answers to user queries, the paper proposes a lightweight, context-aware approach based on the combination of keywords with natural language queries. The method employs ontologies and query patterns to support the users in formulating the most appropriate query for retrieving the desired data. Precision and query efficiency are further improved by focusing searches only to the data which are meaningful w.r.t. the current context, thus supporting the users' situation awareness.\ud The approach has been integrated in the SAFE system, developed for mobile and Web, and has been applied in cardiology to support medical personnel in emergency interventions on patients affected by chronic cerebro-vascular diseases. Experimental results have shown that the proposed solution significantly reduces the time to get useful data w.r.t. traditional form-based approaches

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Context-aware information retrieval on a ubiquitous medical learning environment

Cited by 5 publications

References 2 publications

Toward a layered model of context for health information searching: An analysis of consumer‐generated questions

Toward a layered model of context for health information searching: An analysis of consumer‐generated questions

OGIR: an ontology‐based grid information retrieval framework

Keyword-based, context-aware selection of natural language query patterns

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