Knowledge Representation and Management: Towards Interoperable Medical Terminologies

Rassinoux, Anne-Marie

doi:10.1055/s-0038-1638646

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…The above development and research trends were already highlighted throughout the topics of the KRM section of the previous IMIA Yearbooks whose titles were: "Structuring knowledge for better access" [6], "Towards interoperable medical terminologies" [7] or "Trans IMIA Yearbook of Medical Informatics 2011…”

Section: Best Paper Selectionmentioning

confidence: 98%

Knowledge Representation and Management: Benefits and Challenges of the Semantic Web for the Fields of KRM and NLP

Rassinoux

2011

Yearb Med Inform

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SummaryTo summarize excellent current research in the field of knowledge representation and management (KRM).A synopsis of the articles selected for the IMIA Yearbook 2011 is provided and an attempt to highlight the current trends in the field is sketched.This last decade, with the extension of the text-based web towards a semantic-structured web, NLP techniques have experienced a renewed interest in knowledge extraction. This trend is corroborated through the five papers selected for the KRM section of the Yearbook 2011. They all depict outstanding studies that exploit NLP technologies whenever possible in order to accurately extract meaningful information from various biomedical textual sources.Bringing semantic structure to the meaningful content of textual web pages affords the user with cooperative sharing and intelligent finding of electronic data. As exemplified by the best paper selection, more and more advanced biomedical applications aim at exploiting the meaningful richness of free-text documents in order to generate semantic metadata and recently to learn and populate domain ontologies. These later are becoming a key piece as they allow portraying the semantics of the Semantic Web content. Maintaining their consistency with documents and semantic annotations that refer to them is a crucial challenge of the Semantic Web for the coming years.

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Section: Best Paper Selectionmentioning

confidence: 98%

Knowledge Representation and Management: Benefits and Challenges of the Semantic Web for the Fields of KRM and NLP

Rassinoux

2011

Yearb Med Inform

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“…Minimal proto-ontologies in OWL are designed for capturing different aspects of the text mining experiment thus allowing the semantic interoperability between them. Harmonizing concept representation, languages and methodologies was already brought to light in the last year IMIA Yearbook [13]. Moreover, using a workflow as the knowledge extraction procedure makes possible to repeatedly run the same workflow or adaptations of it in order to perform posterior analyses over the results from several experiments.…”

Section: Best Paper Selectionmentioning

confidence: 99%

“…This paper outlines that the structure of modern terminologies must evolve in order to formally integrate concept meanings and relationships. This is of paramount interest as more and more often ontologies enhance information discovery (see [12]), modeling, and semantic interoperability [13]. Moreover, an attempt to reformulate the SNOMED's logical formalism into the OWL standard has already been scrutinized [16,13], in order to improve its accessibility and internationalization.…”

Section: Best Paper Selectionmentioning

confidence: 99%

Knowledge Representation and Management

2010

Yearb Med Inform

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SummaryObjectives: To summarize current outstanding research in the field of knowledge representation and management. Method: Synopsis of the articles selected for the IMIA Yearbook 2010. Results: Four interesting papers, dealing with structured knowledge, have been selected for the section knowledge representation and management. Combining the newest techniques in computational linguistics and natural language processing with the latest methods in statistical data analysis, machine learning and text mining has proved to be efficient for turning unstructured textual information into meaningful knowledge. Three of the four selected papers for the section knowledge representation and management corroborate this approach and depict various experiments conducted to .extract meaningful knowledge from unstructured free texts such as extracting cancer disease characteristics from pathology reports, or extracting protein-protein interactions from biomedical papers, as well as extracting knowledge for the support of hypothesis generation in molecular biology from the Medline literature. Finally, the last paper addresses the level of formally representing and structuring information within clinical terminologies in order to render such information easily available and shareable among the health informatics community. Conclusions: Delivering common powerful tools able to automatically extract meaningful information from the huge amount of electronically unstructured free texts is an essential step towards promoting sharing and reusability across applications, domains, and institutions thus contributing to building capacities worldwide.

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“…For example, having access to data from outpatient record systems may not be available to physicians caring for the patient in an emergency room or a hospital. Rassinoux addresses many of these problems in this Yearbook [49].…”

Section: Major Problems With Signal Acquisition Data Integration Andmentioning

confidence: 99%

Clinical Decision Support Systems: The Fascination with Closed-Loop Control

Gardner

2009

Yearb Med Inform

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Knowledge Representation and Management: Towards Interoperable Medical Terminologies

Cited by 3 publications

References 13 publications

Knowledge Representation and Management: Benefits and Challenges of the Semantic Web for the Fields of KRM and NLP

Knowledge Representation and Management: Benefits and Challenges of the Semantic Web for the Fields of KRM and NLP

Knowledge Representation and Management

Clinical Decision Support Systems: The Fascination with Closed-Loop Control

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