no code implementations • EMNLP (Eval4NLP) 2020 • Kiril Gashteovski, Rainer Gemulla, Bhushan Kotnis, Sven Hertling, Christian Meilicke
First, we investigate OPIEC triples and DBpedia facts having the same arguments by comparing the information on the OIE surface relation with the KB rela- tion.
1 code implementation • 29 Apr 2024 • Sven Hertling, Ebrahim Norouzi, Harald Sack
In this paper, we introduce a dataset that contains training, validation, and test sets for most of the OAEI tracks.
no code implementations • 7 Nov 2023 • 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.
no code implementations • 21 Aug 2023 • 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 correct, or more diverse.
no code implementations • 6 Oct 2022 • Sven Hertling, Heiko Paulheim
In this paper, we present the approach and analysis of DBkWik++, a fused Knowledge Graph from thousands of wikis.
no code implementations • 15 Sep 2022 • Sven Hertling, Heiko Paulheim
The number of Knowledge Graphs (KGs) generated with automatic and manual approaches is constantly growing.
no code implementations • 29 Apr 2022 • Sven Hertling, Jan Portisch, Heiko Paulheim
One of the strongest signals for automated matching of knowledge graphs and ontologies are textual concept descriptions.
no code implementations • 3 Nov 2021 • Sven Hertling, Heiko Paulheim
Knowledge graphs (KGs) provide information in machine interpretable form.
no code implementations • 15 Sep 2021 • Sven Hertling, Jan Portisch, Heiko Paulheim
One of the strongest signals for automated matching of ontologies and knowledge graphs are the textual descriptions of the concepts.
1 code implementation • 2 Jul 2021 • Malte Brockmeier, Yawen Liu, Sunita Pateer, Sven Hertling, Heiko Paulheim
Modern large-scale knowledge graphs, such as DBpedia, are datasets which require large computational resources to serve and process.
no code implementations • 20 Sep 2020 • 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 and instance matching.
no code implementations • 27 Apr 2020 • 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).
no code implementations • 2 Mar 2020 • Nicolas Heist, Sven Hertling, Daniel Ringler, Heiko Paulheim
Knowledge Graphs are an emerging form of knowledge representation.
no code implementations • 24 Feb 2020 • Sven Hertling, Heiko Paulheim
The Ontology Alignment Evaluation Initiative (OAEI) is an annual evaluation of ontology matching tools.
3 code implementations • AKBC 2019 • Kiril Gashteovski, Sebastian Wanner, Sven Hertling, Samuel Broscheit, Rainer Gemulla
In this paper, we release, describe, and analyze an OIE corpus called OPIEC, which was extracted from the text of English Wikipedia.
Open-Ended Question Answering Open Information Extraction +1