Computer program for the analysis of filament-reinforced metal-shell pressure vessels

Fortran IV computer program for design and structural analysis calculations of filament- reinforced metal shell pressure vessel

Improving document retrieval with a clustering based relevance feedback system

Relevance feedback for document retrieval systems is a technique where user feedback is used to improve a query response. In this work we propose a system that uses multiple clusterings and a semi-supervised heuristic to improve a query response. The heuristic creates an optimal cluster w.r.t. the relevance feedback based on multiple clusterings. We justify the explicit separation of the optimization process and the clustering process by time and space constrains. The evaluation of the heuristic on a corpus containing 1.660 documents from MEDLINE showed promising results. We were able to obtain better results as a single clustering after a few iterations

What was the Question? A Systematization of Information Retrieval and NLP Problems

In this paper we suggest a novel systematization of Information Retrieval and Natural Language Processing problems. Using this rather general description of problems we are able to discuss and proof the equivalence of some problems. We provide reformulations of well-known problems like Named Entity Recognition using our novel description and discuss further research and the expected outcome. We will discuss the relation of two problems, cluster labeling and search query finding. With these results we are able to provide a novel optimization approach to both problems. This novel systematization approach provides a yet unknown view generating new classes of problems in NLP. It brings application and algorithmic approaches together and offers a better description with concepts of theoretical computer science

SCAIView - A semantic search engine for biomedical research utilizing a microservice architecture

Biological and medical researchers explore the mechanisms of living organisms and tend to gain a better understanding of underlying fundamental biological processes of life. To tackle such complex tasks they constantly need to gather and accumulate new knowledge by performing experiments and studying scientific literature. We will present the novel semantic search engine "SCAIView" for knowledge discovery and retrieval and, additionally, discuss the most recent paradigm shifts in communication technologies, which leads to a completely new architecture that improves scalability, achieves better interoperability, and also increases fault-tolerance