NDLI: Application of the spreading activation technique for recommending concepts of well-known ontologies in medical systems

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Application of the spreading activation technique for recommending concepts of well-known ontologies in medical systems

Content Provider	ACM Digital Library
Author	Polo, Luis Abella, Pablo Jimenez, Weena Labra, Jose Emilio Alvarez, Jose María
Abstract	The aim of this paper is to present the application of the Spreading Activation Technique in the scope of medical systems. This technique is implemented through the ONTOSPREAD framework for the development, configuration, customization and execution of the Spreading Activation technique over graph-based structures, more specifically over RDF graphs and ontologies arising from the Semantic Web area. It has been used to the efficient exploration and querying of large and heterogeneous knowledge bases based on semantic networks in the Information and Document Retrieval domains. ONTOSPREAD implements the double process of activation and spreading of concepts in ontologies applying different restrictions of the original model like weight degradation according to the distance or others coming from the extension of this technique like the converging paths reward. It is considered to be relevant to support the recommendation of concepts for tagging clinical records and to provide a tool for decision-support in clinical diagnosis. Finally an evaluation methodology and two examples using the well-known ontologies Galen and SNOMED CT are presented to validate the goodness, the improvement and the capabilities of this technique applied to medical systems.
Starting Page	626
Ending Page	635
Page Count	10
File Format	PDF
ISBN	9781450307963
DOI	10.1145/2147805.2147913
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-08-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Algorithms Recommending system Clinical decision-support Information retrieval Recommending and tagging systems Spreading activation
Content Type	Text
Resource Type	Article

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