70 citations · 273 across the 44 of their papers we have counts for
22 papers · 1 filter
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…
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…
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.…
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…
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…
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…
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…
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…
The CaLiGraph Ontology as a Challenge for OWL Reasoners
Nicolas Heist, Heiko Paulheim
CaLiGraph is a large-scale cross-domain knowledge graph generated from Wikipedia by exploiting the category system, list pages, and other list structures in Wikipedia, containing m…
Winning at Any Cost -- Infringing the Cartel Prohibition With Reinforcement Learning
Michael Schlechtinger, Damaris Kosack, Heiko Paulheim +1
Pricing decisions are increasingly made by AI. Thanks to their ability to train with live market data while making decisions on the fly, deep reinforcement learning algorithms are…
Large-scale Taxonomy Induction Using Entity and Word Embeddings
Petar Ristoski, Stefano Faralli, Simone Paolo Ponzetto +1
Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal on…
Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019
Nacira Abbas, Kholoud Alghamdi, Mortaza Alinam +71
One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is…
Supervised Ontology and Instance Matching with MELT
Sven Hertling, Jan Portisch, Heiko Paulheim
In this paper, we present MELT-ML, a machine learning extension to the Matching and EvaLuation Toolkit (MELT) which facilitates the application of supervised learning for ontology…
RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings
Jan Portisch, Michael Hladik, Heiko Paulheim
Knowledge graph embedding approaches represent nodes and edges of graphs as mathematical vectors. Current approaches focus on embedding complete knowledge graphs, i.e. all nodes an…
More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings
Andreea Iana, Heiko Paulheim
RDF2vec is an embedding technique for representing knowledge graph entities in a continuous vector space. In this paper, we investigate the effect of materializing implicit A-box a…
A Knowledge Graph for Assessing Aggressive Tax Planning Strategies
Niklas Lüdemann, Ageda Shiba, Nikolaos Thymianis +3
The taxation of multi-national companies is a complex field, since it is influenced by the legislation of several states. Laws in different states may have unforeseen interaction e…
Towards Exploiting Implicit Human Feedback for Improving RDF2vec Embeddings
Ahmad Al Taweel, Heiko Paulheim
RDF2vec is a technique for creating vector space embeddings from an RDF knowledge graph, i.e., representing each entity in the graph as a vector. It first creates sequences of node…
Knowledge Graphs on the Web -- an Overview
Nicolas Heist, Sven Hertling, Daniel Ringler +1
Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, th…