SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases

The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources of structured knowledge and answer complex queries. However, the efficient alignment of large-scale knowledge bases still poses a considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts. SiGMa is an iterative propagation algorithm which leverages both the structural information from the relationship graph as well as flexible similarity measures between entity properties in a greedy local search, thus making it scalable. Despite its greedy nature, our experiments indicate that SiGMa can efficiently match some of the world's largest knowledge bases with high precision. We provide additional experiments on benchmark datasets which demonstrate that SiGMa can outperform state-of-the-art approaches both in accuracy and efficiency.Comment: 10 pages + 2 pages appendix; 5 figures -- initial preprin

Austrian Research and Technology Report 2023

Der Forschungs- und Technologiebericht ist der Lagebericht über die aus Bundesmitteln geförderte Forschung, Technologie und Innovation in Österreich und wird im Auftrag des Bundesministeriums für Bildung, Wissenschaft und Forschung (BMBWF) in Einvernehmen mit dem Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie (BMK) sowie dem Bundesministerium für Arbeit und Wirtschaft (BMAW) erstellt. Der vorliegende Bericht steht im Zeichen eines komplexen Wandels auf unterschiedlichen Ebenen, einerseits getrieben durch multiple Krisen, die nicht nur das Innovationsverhalten von Unternehmen und wissenschaftlichen Akteurinnen und Akteuren verändern, sondern auch veränderte Rahmenbedingungen mit sich bringen. Die Twin Transition ist allgegenwärtig. Im vorliegenden Bericht wird mit dem Schwerpunktthema der Fokus auf die Grüne Transformation in Forschung und Wirtschaft gelegt. Abstrac

Österreichischer Forschungs- und Technologiebericht 2023

Der Forschungs- und Technologiebericht ist der Lagebericht über die aus Bundesmitteln geförderte Forschung, Technologie und Innovation in Österreich und wird im Auftrag des Bundesministeriums für Bildung, Wissenschaft und Forschung (BMBWF) in Einvernehmen mit dem Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie (BMK) sowie dem Bundesministerium für Arbeit und Wirtschaft (BMAW) erstellt. Der vorliegende Bericht steht im Zeichen eines komplexen Wandels auf unterschiedlichen Ebenen, einerseits getrieben durch multiple Krisen, die nicht nur das Innovationsverhalten von Unternehmen und wissenschaftlichen Akteurinnen und Akteuren verändern, sondern auch veränderte Rahmenbedingungen mit sich bringen. Die Twin Transition ist allgegenwärtig. Im vorliegenden Bericht wird mit dem Schwerpunktthema der Fokus auf die Grüne Transformation in Forschung und Wirtschaft gelegt

Austrian Research and Technology Report 2022

Der Forschungs-und Technologiebericht 2022 gibt einen Überblick über die aus Bundesmitteln geförderte Forschung, Technologie und Innovation (FTI) in Österreich. Neben der Darstellung aktueller forschungspolitischer Entwicklungen, die den Stand der Umsetzung der mit Ende 2020 verabschiedeten FTI-Strategie 2030, forschungsrelevante Teilstrategien und neueste Entwicklungen im Hochschulbereich behandelt, werden auf Grundlage rezenter Daten aus diversen internationalen Rankings, aus der F&E (Forschung & Entwicklung)-Erhebung 2019 und der Globalschätzung 2022 Analysen zur nationalen und internationalen FTI-Performance Österreichs erstellt

Eye tracking: empirical foundations for a minimal reporting guideline

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "empirically based minimal reporting guideline")

Eye-Tracking as a Tool to Evaluate Functional Ability in Everyday Tasks in Glaucoma

To date, few studies have investigated the eye movement patterns of individuals with glaucoma while they undertake everyday tasks in real-world settings. While some of these studies have reported possible compensatory gaze patterns in those with glaucoma who demonstrated good task performance despite their visual field loss, little is known about the complex interaction between field loss and visual scanning strategies and the impact on task performance and, consequently, on quality of life. We review existing approaches that have quantified the effect of glaucomatous visual field defects on the ability to undertake everyday activities through the use of eye movement analysis. Furthermore, we discuss current developments in eye-tracking technology and the potential for combining eye-tracking with virtual reality and advanced analytical approaches. Recent technological developments suggest that systems based on eye-tracking have the potential to assist individuals with glaucomatous loss to maintain or even improve their performance on everyday tasks and hence enhance their long-term quality of life. We discuss novel approaches for studying the visual search behavior of individuals with glaucoma that have the potential to assist individuals with glaucoma, through the use of personalized programs that take into consideration the individual characteristics of their remaining visual field and visual search behavior

Modeling cognitive processes from multimodal signals

Multimodal signals allow us to gain insights into internal cognitive processes of a person, for example: speech and gesture analysis yields cues about hesitations, knowledgeability, or alertness, eye tracking yields information about a person's focus of attention, task, or cognitive state, EEG yields information about a person's cognitive load or information appraisal. Capturing cognitive processes is an important research tool to understand human behavior as well as a crucial part of a user model to an adaptive interactive system such as a robot or a tutoring system. As cognitive processes are often multifaceted, a comprehensive model requires the combination of multiple complementary signals. In this workshop at the ACM International Conference on Multimodal Interfaces (ICMI) conference in Boulder, Colorado, USA, we discussed the state-of-the-art in monitoring and modeling cognitive processes from multi-modal signals

Bayesian Knowledge Corroboration with Logical Rules and User Feedback

Abstract. Current knowledge bases suffer from either low coverage or low accuracy. The underlying hypothesis of this work is that user feedback can greatly improve the quality of automatically extracted knowledge bases. The feedback could help quantify the uncertainty associated with the stored facts and would enable mechanisms for searching, ranking and reasoning at entity-relationship level. Most importantly, a principled model for exploiting user feedback to learn the truth values of facts in the knowledge base would be a major step forward in addressing the issue of knowledge base curation. We present a family of probabilistic graphical models that builds on user feedback and logical inference rules derived from the popular Semantic Web formalism of RDFS [1]. Through internal inference and belief propagation, these models are capable of learning both, the truth values of the facts in the knowledge base and the reliabilities of the users who give feedback. We demonstrate the viability of our approach in extensive experiments on real-world datasets, with feedback collected from Amazon Mechanical Turk