A SEMANTIC FRAMEWORK FOR SEQUENTIAL DECISION MAKING FOR JOURNAL OF WEB ENGINEERING

Authors

  • PATRICK PHILIPP Institute AIFB, Karlsruhe Institute of Technology Karlsruhe, Germany
  • MARIA MALESHKOVA Institute AIFB, Karlsruhe Institute of Technology Karlsruhe, Germany
  • ACHIM RETTINGER Institute AIFB, Karlsruhe Institute of Technology Karlsruhe, Germany
  • DARKO KATIC HIS, Karlsruhe Institute of Technology Karlsruhe, Germany

Keywords:

Sequential Decision Making, Linked APIs, Meta Learning, Medical Assistance, Entity Linking

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.

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