Dr. Sahar Vahdati2021-04-15T19:12:21+02:00

Dr. Sahar Vahdati


Nature, and all that it encompasses, has influenced computer science, in particular Artificial Intelligence, from its inception. Many effective tools, mechanisms, processes, algorithms, methods, and systems have been proposed inspired by nature. For example, Neural Networks are roughly inspired by the cognitive brain function, Genetic Algorithms are inspired by evolution and the survival of the fittest, and Artificial Immune Systems are inspired by their biological equivalents. Further examples include swarm or collective approaches, that are inspired by colonies of insects and birds. Current AI methods have the following weaknesses:


Link Prediction using Numerical Weights for Knowledge Graph Completion within the Scholarly Domain

By , , , , , , , , , |December 11th, 2020|

Abstract Knowledge graphs (KGs) are widely used for modeling scholarly communication, informing scientometric analysis, and supporting a variety of intelligent services to explore the literature and predict research dynamics. However, they often suffer from incompleteness (eg, missing affiliations, references, research topics), which ends up reducing the scope and quality of the resulting analysis. This issue is usually tackled by computing knowledge graph embeddings (KGEs) [...]

5* Knowledge Graph Embeddings with Projective Transformations

By , , , |June 8th, 2020|

Abstract Performing link prediction using knowledge graph embedding (KGE) models is a popular approach for knowledge graph completion. Such link predictions are performed by measuring the likelihood of links in the graph via a transformation function that maps nodes via edges into a vector space. Since the complex structure of the real world is reflected in multi-relational knowledge graphs, the transformation functions need to [...]

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