Robert Jäschke

Simon Hachmeier, Robert Jäschke

Detecting music entities such as song titles or artist names is a useful application to help use cases like processing music search queries or analyzing music consumption on the web. Recent approaches incorporate smaller language models (SLMs) like BERT and achieve high results. However, further research indicates a high influence of entity exposure during pre-training on the performance of the models. With the advent of large language models (LLMs), these outperform SLMs in a variety of downstream tasks. However, researchers are still divided if this is applicable to tasks like entity detection in texts due to issues like hallucination. In this paper, we provide a novel dataset of user-generated metadata and conduct a benchmark and a robustness study using recent LLMs with in-context-learning (ICL). Our results indicate that LLMs in the ICL setting yield higher performance than SLMs. We further uncover the large impact of entity exposure on the best performing LLM in our study.

Tom Hanika, Robert Jäschke

Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of data sets are repeatedly used in scholarly articles and software tools, acting as de facto standard data sets. However, the distribution of the data sets poses a problem for the sustainable development of the research field. There is a lack of a central location that provides and describes FCA data sets and links them to already known analysis results. This article analyses the current state of the dissemination of FCA data sets, presents the requirements for a central FCA repository, and highlights the challenges for this.

Frank Fischer, Robert Jäschke

Vossian Antonomasia is a prolific stylistic device, in use since antiquity. It can compress the introduction or description of a person or another named entity into a terse, poignant formulation and can best be explained by an example: When Norwegian world champion Magnus Carlsen is described as "the Mozart of chess", it is Vossian Antonomasia we are dealing with. The pattern is simple: A source (Mozart) is used to describe a target (Magnus Carlsen), the transfer of meaning is reached via a modifier ("of chess"). This phenomenon has been discussed before (as 'metaphorical antonomasia' or, with special focus on the source object, as 'paragons'), but no corpus-based approach has been undertaken as yet to explore its breadth and variety. We are looking into a full-text newspaper corpus (The New York Times, 1987-2007) and describe a new method for the automatic extraction of Vossian Antonomasia based on Wikidata entities. Our analysis offers new insights into the occurrence of popular paragons and their distribution.

Tuan Tran, Nam Khanh Tran, Teka Hadgu Asmelash et al.

Trending topics in microblogs such as Twitter are valuable resources to understand social aspects of real-world events. To enable deep analyses of such trends, semantic annotation is an effective approach; yet the problem of annotating microblog trending topics is largely unexplored by the research community. In this work, we tackle the problem of mapping trending Twitter topics to entities from Wikipedia. We propose a novel model that complements traditional text-based approaches by rewarding entities that exhibit a high temporal correlation with topics during their burst time period. By exploiting temporal information from the Wikipedia edit history and page view logs, we have improved the annotation performance by 17-28\%, as compared to the competitive baselines.

Christoph Hube, Frank Fischer, Robert Jäschke et al.

Among the manifold takes on world literature, it is our goal to contribute to the discussion from a digital point of view by analyzing the representation of world literature in Wikipedia with its millions of articles in hundreds of languages. As a preliminary, we introduce and compare three different approaches to identify writers on Wikipedia using data from DBpedia, a community project with the goal of extracting and providing structured information from Wikipedia. Equipped with our basic set of writers, we analyze how they are represented throughout the 15 biggest Wikipedia language versions. We combine intrinsic measures (mostly examining the connectedness of articles) with extrinsic ones (analyzing how often articles are frequented by readers) and develop methods to evaluate our results. The better part of our findings seems to convey a rather conservative, old-fashioned version of world literature, but a version derived from reproducible facts revealing an implicit literary canon based on the editing and reading behavior of millions of people. While still having to solve some known issues, the introduced methods will help us build an observatory of world literature to further investigate its representativeness and biases.

Nikolas Landia, Stephan Doerfel, Robert Jäschke et al.

The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.