A semantic framework for sequential decision making for journal of web engineering
Publication date
2017-03-01
Document type
Forschungsartikel
Author
Organisational unit
Karlsruhe Institute of Technology
Scopus ID
Publisher
Rinton Press
Series or journal
Journal of Web Engineering (JWE)
ISSN
Periodical volume
16
Periodical issue
5-6
First page
471
Last page
504
Part of the university bibliography
Nein
Language
English
Keyword
Entity linking
Linked apis
Medical assistance
Meta learning
Sequential decision making
Abstract
Current developments in the medical domain, not unlike many other sectors, are marked by the growing digitalization of data, including patient records, study results, clinical guidelines or imagery. This trend creates the opportunity for the development of innovative decision support systems to assist physicians in making a diagnosis or preparing a treatment plan. Similar conditions hold for the Web, where massive amounts of raw text are to be processed and interpreted automatically, e.g. to eventually add new information to a knowledge base. To this end, complex tasks need to be solved, requiring one or more interpretation algorithms (e.g. image- or natural language processors) to be chosen and executed based on heterogeneous data. We, therefore, propose the first approach to a semantic framework for sequential decision making and develop the foundations of a Linked agent who executes interpretation algorithms available as Linked APIs [43] on a data-driven, declarative basis [45] by integrating structured knowledge formalized with the Resource Description Framework (RDF), and having access to meta components for planning and learning from experience. We evaluate our framework based on automatically processing brain images, the ad-hoc combination of surgical phase recognition algorithms and experiential learning to optimally pipeline entity linking approaches. © Rinton Press.
Version
Published version
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