Am Anfang einer jeden Forschungsarbeit steht das Literaturreview, das heißt die systematische Aufarbeitung des Stands der Wissenschaft, Forschung und Technik. Im wissenschaftlichen Umfeld wohnt diesem Teil der wissenschaftlichen Arbeit aufgrund der rapide ansteigenden Anzahl von Publikationen weltweit eine größer werdende Herausforderung inne. Um diesen Schritt so effektiv und effizient wie möglich zu gestalten, ist der Einsatz eines strukturierten, nachvollziehbaren und methodischen Vorgehens wichtiger. Vor allem im medizinischen und sozialwissenschaftlichen Forschungsbereich hat sich die Durchführung von systematischen Literaturreviews etabliert. Während in den genannten Forschungsdisziplinen diese Methode im Rahmen verschiedener Publikation diskutiert und determiniert ist, existiert im ingenieurwissenschaftlichen Bereich eine hohe Heterogenität in angewendeten Methoden und deren Qualität. Im folgenden Artikel wird das methodische Vorgehen des systematischen Literaturreviews auf den ingenieurwissenschaftlichen Bereich übertragen und als praktischer Handlungsleitfaden dargestellt. Dafür wird zunächst der wissenschaftliche Stand der methodischen Vorgehensweise des systematischen Literaturreviews aufgezeigt. Die überblickende Beschreibung des Gesamtprozesses schließt sich an, gefolgt von der Detailierung der zugehörigen Subprozesse. Jede Prozessbeschreibung wird zum besseren Verständnis durch eine Prozessdarstellung unterstützt. Finalisiert wird der Artikel durch eine Betrachtung von Grenzen der beschriebenen Methodik sowie durch ein Fazit.:Abstract 1 Einleitung 2 Stand von Wissenschaft und Forschung 3 Beschreibung des Gesamtprozesses 4 Dokumentation (A) 5 Konzeptionierung (B) 6 Literatursuche (C) 7 Literaturanalyse und -auswahl (D) 8 Erweiterte Literatursuche (E) 9 Kritik und Limitierung 10 Fazit Literaturverzeichnis / At the beginning of any research work you have to do a literature review, the systematic review of the state of the art in science, research and technology. In the scientific environment, this part of scientific work is increasingly challenging due to the rapidly growing number of publications worldwide. To make this step as effective and efficient as possible, the use of a structured, comprehensible and methodical approach is crucial. Especially in the medical and social science research fields, the implementation of systematic literature reviews has become established. In the mentioned research disciplines this method is discussed and determined in the context of different publications, whereas in the engineering field there is a high heterogeneity in applied methods and their quality. In the following article, the methodological procedure of systematic literature reviews is transferred to the field of engineering and presented as a practical guideline. For this purpose, the scientific state-of-the-art of the methodological approach of systematic literature reviews is first presented. The overview description of the overall process follows, after which the details of the associated sub-processes are presented. Each process description is supported by a process chart for better understanding. The article is finalized by a consideration of limitations of the described methodology as well as by a conclusion.:Abstract 1 Einleitung 2 Stand von Wissenschaft und Forschung 3 Beschreibung des Gesamtprozesses 4 Dokumentation (A) 5 Konzeptionierung (B) 6 Literatursuche (C) 7 Literaturanalyse und -auswahl (D) 8 Erweiterte Literatursuche (E) 9 Kritik und Limitierung 10 Fazit Literaturverzeichnis

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org.

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org.