Search Results for author:
Found 9 papers, 4 papers with code
A larger-scale evaluation resource of terms and their shift direction for diachronic lexical semantics
Determining how words have changed their meaning is an important topic in Natural Language Processing.
Analysing and Organising Human Communications for AI Fairness-Related Decisions: Use Cases from the Public Sector
End-users and policy-makers often lack the technical skills to interpret a system's limitations, and rely on developer roles for making decisions concerning fairness issues.
Text classification of column headers with a controlled vocabulary: leveraging LLMs for metadata enrichment
This work proposes a novel approach that leverages LLMs for text classification using a controlled topic vocabulary, which has the potential to facilitate automated metadata enrichment, thereby enhancing dataset retrieval and the Findability, Accessibility, Interoperability and Reusability (FAIR) of research data on the Web.
How Contentious Terms About People and Cultures are Used in Linked Open Data
In some cases, LOD contributors mark contentious terms with words and phrases in literals (implicit markers) or properties linked to resources (explicit markers).
Hidden Author Bias in Book Recommendation
We find that popular books are mainly written by US citizens in the dataset, and that these books tend to be recommended disproportionally by popular collaborative filtering algorithms compared to the users' profiles.
Nanopublication-Based Semantic Publishing and Reviewing: A Field Study with Formalization Papers
With the rapidly increasing amount of scientific literature, it is getting continuously more difficult for researchers in different disciplines to be updated with the recent findings in their field of study. Processing scientific articles in an automated fashion has been proposed as a solution to this problem, but the accuracy of such processing remains very poor for extraction tasks beyond the basic ones. Few approaches have tried to change how we publish scientific results in the first place, by making articles machine-interpretable by expressing them with formal semantics from the start. In the work presented here, we set out to demonstrate that we can formally publish high-level scientific claims in formal logic, and publish the results in a special issue of an existing journal. We use the concept and technology of nanopublications for this endeavor, and represent not just the submissions and final papers in this RDF-based format, but also the whole process in between, including reviews, responses, and decisions. We do this by performing a field study with what we call formalization papers, which contribute a novel formalization of a previously published claim. We received 15 submissions from 18 authors, who then went through the whole publication process leading to the publication of their contributions in the special issue. Our evaluation shows the technical and practical feasibility of our approach. The participating authors mostly showed high levels of interest and confidence, and mostly experienced the process as not very difficult, despite the technical nature of the current user interfaces. We believe that these results indicate that it is possible to publish scientific results from different fields with machine-interpretable semantics from the start, which in turn opens countless possibilities to radically improve in the future the effectiveness and efficiency of the scientific endeavor as a whole.
Expressing High-Level Scientific Claims with Formal Semantics
Analyzing the main claims from a sample of scientific articles from all disciplines, we find that their semantics are more complex than what a straight-forward application of formalisms like RDF or OWL account for, but we managed to elicit a clear semantic pattern which we call the 'super-pattern'.
Is it a Fruit, an Apple or a Granny Smith? Predicting the Basic Level in a Concept Hierarchy
The "basic level", according to experiments in cognitive psychology, is the level of abstraction in a hierarchy of concepts at which humans perform tasks quicker and with greater accuracy than at other levels.
Utilizing a Transparency-driven Environment toward Trusted Automatic Genre Classification: A Case Study in Journalism History
With the growing abundance of unlabeled data in real-world tasks, researchers have to rely on the predictions given by black-boxed computational models.
