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Wie weit kann ein christlicher Denker Avicenna folgen, wenn er dessen Ontologie zur Erklärung des Verhältnisses von Gott und Welt heranzieht? Dieser zentralen Frage der Avicenna-Rezeption widmet sich die vorliegende Arbeit.
Avicenna (Ibn Sīnā, 980–1037) entwickelt in der Metaphysik (al-Ilāhiyyāt) – dem vierten Teil seiner philosophischen Summe Buch der Heilung (Kitāb al-Šifāʾ) – den Grundgedanken seiner Ontologie: die Distinktion von Sein und Wesen, die zu einem seiner bekanntesten und einflussreichsten Lehrstücke wurde. Nach der lateinischen Übersetzung von Avicennas Metaphysik im zwölften Jahrhundert fand die darin entworfene Ontologie rasche Verbreitung unter den lateinisch-christlichen Gelehrten. Für deren monotheistische Weltanschauung war diese Lehre insofern attraktiv, als sich aus der Sein-Wesen-Distinktion die wichtigsten ontologischen Aspekte der Beziehung von Gott und Welt rein rational ableiten lassen. Vor diesem Hintergrund stellt sich die genannte Frage, wie weit ein christlicher Denker mit Avicenna gehen kann, wenn er dessen Ontologie heranzieht, um das Verhältnis von Gott und Welt zu erklären. Diese Frage untersucht die Autorin für die drei Gelehrten Dominicus Gundisalvi († nach 1190), Wilhelm von Auvergne († 1249) und Heinrich von Gent († 1293). Die Verschränkung von Ontologie, Theologie und Kosmogonie gibt der Autorin die Möglichkeit, für diese drei Bereiche jeweils herauszuarbeiten, an welchen Stellen und aus welchen Motiven Modifikationen an der avicennischen Theorie vorgenommen wurden, um sie eigenen Zwecken oder neuen Kontexten wie der Trinitätstheologie anzupassen. Zugleich zeigt sie auf, an welchen Punkten mit Avicennas Theorie gänzlich gebrochen wurde. Was bedeuten diese Änderungen und Brüche inhaltlich? Und insbesondere: Wie werden sie rational gerechtfertigt?
An important but very time consuming part of the research process is literature review. An already large and nevertheless growing ground set of publications as well as a steadily increasing publication rate continue to worsen the situation. Consequently, automating this task as far as possible is desirable. Experimental results of systems are key-insights of high importance during literature review and usually represented in form of tables. Our pipeline KIETA exploits these tables to contribute to the endeavor of automation by extracting them and their contained knowledge from scientific publications. The pipeline is split into multiple steps to guarantee modularity as well as analyzability, and agnosticim regarding the specific scientific domain up until the knowledge extraction step, which is based upon an ontology. Additionally, a dataset of corresponding articles has been manually annotated with information regarding table and knowledge extraction. Experiments show promising results that signal the possibility of an automated system, while also indicating limits of extracting knowledge from tables without any context.
How are fictions given? Conjoining the ‘artifactual theory’ and the ‘imaginary-object theory’
(2021)
According to the so-called ‘artifactual theory’ of fiction, fictional objects are to be considered as abstract artifacts. Within this framework, fictional objects are defined on the basis of their complex dependence on literary works, authors, and readership. This theory is explicitly distinguished from other approaches to fictions, notably from the imaginary-object theory. In this article, I argue that the two approaches are not mutually exclusive but can and should be integrated. In particular, the ontology of fiction can be fruitfully supplemented by a phenomenological analysis, which allows us to clarify the defining modes of givenness of fictional objects. Likewise, based on the results of the artifactual theory, some assumptions in the imaginary-object theory, which are liable to be interpreted as laying the ground to phenomenalism, can be corrected.
Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.