70 citations · 273 across the 44 of their papers we have counts for
15 papers · 1 filter
OLaLa: Ontology Matching with Large Language Models
Sven Hertling, Heiko Paulheim
Ontology (and more generally: Knowledge Graph) Matching is a challenging task where information in natural language is one of the most important signals to process. With the rise o…
NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation
Andreea Iana, Goran Glavaš, Heiko Paulheim
NewsRecLib is an open-source library based on Pytorch-Lightning and Hydra developed for training and evaluating neural news recommendation models. The foremost goals of NewsRecLib…
NeMig -- A Bilingual News Collection and Knowledge Graph about Migration
Andreea Iana, Mehwish Alam, Alexander Grote +5
News recommendation plays a critical role in shaping the public's worldviews through the way in which it filters and disseminates information about different topics. Given the cruc…
Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation
Andreea Iana, Goran Glavaš, Heiko Paulheim
Recent neural news recommenders (NNRs) extend content-based recommendation (1) by aligning additional aspects (e.g., topic, sentiment) between candidate news and user history or (2…
Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives
Andreea Iana, Goran Glavaš, Heiko Paulheim
The advent of personalized news recommendation has given rise to increasingly complex recommender architectures. Most neural news recommenders rely on user click behavior and typic…
Transformer-based Subject Entity Detection in Wikipedia Listings
Nicolas Heist, Heiko Paulheim
In tasks like question answering or text summarisation, it is essential to have background knowledge about the relevant entities. The information about entities - in particular, ab…
Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection
Mehwish Alam, Andreea Iana, Alexander Grote +3
News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has be…
Order Matters: Matching Multiple Knowledge Graphs
Sven Hertling, Heiko Paulheim
Knowledge graphs (KGs) provide information in machine interpretable form. In cases where multiple KGs are used in the same system, that information needs to be integrated. This is…
On-Demand and Lightweight Knowledge Graph Generation -- a Demonstration with DBpedia
Malte Brockmeier, Yawen Liu, Sunita Pateer +2
Modern large-scale knowledge graphs, such as DBpedia, are datasets which require large computational resources to serve and process. Moreover, they often have longer release cycles…
GraphConfRec: A Graph Neural Network-Based Conference Recommender System
Andreea Iana, Heiko Paulheim
In today's academic publishing model, especially in Computer Science, conferences commonly constitute the main platforms for releasing the latest peer-reviewed advancements in thei…
Bias in Knowledge Graphs -- an Empirical Study with Movie Recommendation and Different Language Editions of DBpedia
Michael Matthias Voit, Heiko Paulheim
Public knowledge graphs such as DBpedia and Wikidata have been recognized as interesting sources of background knowledge to build content-based recommender systems. They can be use…
Information Extraction From Co-Occurring Similar Entities
Nicolas Heist, Heiko Paulheim
Knowledge about entities and their interrelations is a crucial factor of success for tasks like question answering or text summarization. Publicly available knowledge graphs like W…
Visual Analysis of Ontology Matching Results with the MELT Dashboard
Jan Portisch, Sven Hertling, Heiko Paulheim
In this demo, we introduce MELT Dashboard, an interactive Web user interface for ontology alignment evaluation which is created with the existing Matching EvaLuation Toolkit (MELT)…
Entity Extraction from Wikipedia List Pages
Nicolas Heist, Heiko Paulheim
When it comes to factual knowledge about a wide range of domains, Wikipedia is often the prime source of information on the web. DBpedia and YAGO, as large cross-domain knowledge g…
Uncovering the Semantics of Wikipedia Categories
Nicolas Heist, Heiko Paulheim
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like YAGO or Probase, and has been used extensively for tasks like entity disambiguat…