70 citations · 273 across the 44 of their papers we have counts for
51 papers
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…
The Time Traveler's Guide to Semantic Web Research: Analyzing Fictitious Research Themes in the ESWC "Next 20 Years" Track
Irene Celino, Heiko Paulheim
What will Semantic Web research focus on in 20 years from now? We asked this question to the community and collected their visions in the "Next 20 years" track of ESWC 2023. We cha…
Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals
Patryk Preisner, Heiko Paulheim
Knowledge graph embeddings are dense numerical representations of entities in a knowledge graph (KG). While the majority of approaches concentrate only on relational information, i…
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…
KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks
Nicolas Heist, Sven Hertling, Heiko Paulheim
In recent years, countless research papers have addressed the topics of knowledge graph creation, extension, or completion in order to create knowledge graphs that are larger, more…
Biomedical Knowledge Graph Embeddings with Negative Statements
Rita T. Sousa, Sara Silva, Heiko Paulheim +1
A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importan…
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…
Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
Nicolas Hubert, Heiko Paulheim, Pierre Monnin +2
Knowledge graph embedding models (KGEMs) have gained considerable traction in recent years. These models learn a vector representation of knowledge graph entities and relations, a.…
ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics
Antonis Klironomos, Baifan Zhou, Zhipeng Tan +4
Many machine learning (ML) libraries are accessible online for ML practitioners. Typical ML pipelines are complex and consist of a series of steps, each of them invoking several ML…
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…
Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG
Fajar J. Ekaputra, Majlinda Llugiqi, Marta Sabou +7
In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focu…
NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker
Nicolas Heist, Heiko Paulheim
Entity Linking (EL) is the task of detecting mentions of entities in text and disambiguating them to a reference knowledge base. Most prevalent EL approaches assume that the refere…
Causal Inference for Chatting Handoff
Shanshan Zhong, Jinghui Qin, Zhongzhan Huang +1
Aiming to ensure chatbot quality by predicting chatbot failure and enabling human-agent collaboration, Machine-Human Chatting Handoff (MHCH) has attracted lots of attention from bo…
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…
Gollum: A Gold Standard for Large Scale Multi Source Knowledge Graph Matching
Sven Hertling, Heiko Paulheim
The number of Knowledge Graphs (KGs) generated with automatic and manual approaches is constantly growing. For an integrated view and usage, an alignment between these KGs is neces…
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions
Russa Biswas, Jan Portisch, Heiko Paulheim +2
The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task o…
On a Generalized Framework for Time-Aware Knowledge Graphs
Franz Krause, Tobias Weller, Heiko Paulheim
Knowledge graphs have emerged as an effective tool for managing and standardizing semistructured domain knowledge in a human- and machine-interpretable way. In terms of graph-based…
The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings
Jan Portisch, Heiko Paulheim
Knowledge graph embedding is a representation learning technique that projects entities and relations in a knowledge graph to continuous vector spaces. Embeddings have gained a lot…
KERMIT -- A Transformer-Based Approach for Knowledge Graph Matching
Sven Hertling, Jan Portisch, Heiko Paulheim
One of the strongest signals for automated matching of knowledge graphs and ontologies are textual concept descriptions. With the rise of transformer-based language models, text co…
Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph
Niclas Heilig, Jan Kirchhoff, Florian Stumpe +3
Medical diagnosis is the process of making a prediction of the disease a patient is likely to have, given a set of symptoms and observations. This requires extensive expert knowled…
Ontology Matching Through Absolute Orientation of Embedding Spaces
Jan Portisch, Guilherme Costa, Karolin Stefani +3
Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledg…
Walk this Way! Entity Walks and Property Walks for RDF2vec
Jan Portisch, Heiko Paulheim
RDF2vec is a knowledge graph embedding mechanism which first extracts sequences from knowledge graphs by performing random walks, then feeds those into the word embedding algorithm…
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…