Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
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1Monireh, E. ; Sarker, M.K. ; Bianchi, F. ; Hitzler, P. ; Doran, D. ; Xie, N.: Reasoning over RDF knowledge bases using deep learning.Preprint.
Abstract: Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.
Inhalt: Vgl.: arXiv:1811.04132v1 [cs.LG] 9 Nov 2018.
Themenfeld: Wissensrepräsentation ; Semantic Web
2Derek Doran, D. ; Gokhale, S.S.: ¬A classification framework for web robots.
In: Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2549-2554,.
Abstract: The behavior of modern web robots varies widely when they crawl for different purposes. In this article, we present a framework to classify these web robots from two orthogonal perspectives, namely, their functionality and the types of resources they consume. Applying the classification framework to a year-long access log from the UConn SoE web server, we present trends that point to significant differences in their crawling behavior.
Themenfeld: Internet ; Data Mining