Search Results for author: Heiko Paulheim

Found 35 papers, 14 papers with code

KERMIT - A Transformer-Based Approach for Knowledge Graph Matching

no code implementations29 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.

Graph Matching Knowledge Graphs

Walk this Way! Entity Walks and Property Walks for RDF2vec

no code implementations5 Apr 2022 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 word2vec for computing vector representations for entities.

Knowledge Graph Embedding Knowledge Graphs

Order Matters: Matching Multiple Knowledge Graphs

no code implementations3 Nov 2021 Sven Hertling, Heiko Paulheim

Knowledge graphs (KGs) provide information in machine interpretable form.

Knowledge Graphs

The CaLiGraph Ontology as a Challenge for OWL Reasoners

1 code implementation11 Oct 2021 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 more than 15 million typed entities and around 10 million relation assertions.

Knowledge Graphs

Matching with Transformers in MELT

no code implementations15 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.

Graph Matching Knowledge Graphs

Putting RDF2vec in Order

1 code implementation11 Aug 2021 Jan Portisch, Heiko Paulheim

The RDF2vec method for creating node embeddings on knowledge graphs is based on word2vec, which, in turn, is agnostic towards the position of context words.

Knowledge Graphs

Winning at Any Cost -- Infringing the Cartel Prohibition With Reinforcement Learning

no code implementations5 Jul 2021 Michael Schlechtinger, Damaris Kosack, Heiko Paulheim, Thomas Fetzer

Thanks to their ability to train with live market data while making decisions on the fly, deep reinforcement learning algorithms are especially effective in taking such pricing decisions.

reinforcement-learning

On-Demand and Lightweight Knowledge Graph Generation -- a Demonstration with DBpedia

1 code implementation2 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.

Graph Generation Knowledge Graphs

Background Knowledge in Schema Matching: Strategy vs. Data

1 code implementation29 Jun 2021 Jan Portisch, Michael Hladik, Heiko Paulheim

The use of external background knowledge can be beneficial for the task of matching schemas or ontologies automatically.

Knowledge Graphs

GraphConfRec: A Graph Neural Network-Based Conference Recommender System

1 code implementation23 Jun 2021 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 their respective fields.

Graph Attention Recommendation Systems

Large-scale Taxonomy Induction Using Entity and Word Embeddings

no code implementations4 May 2021 Petar Ristoski, Stefano Faralli, Simone Paolo Ponzetto, Heiko Paulheim

Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies.

Word Embeddings

Bias in Knowledge Graphs -- an Empirical Study with Movie Recommendation and Different Language Editions of DBpedia

1 code implementation3 May 2021 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.

Knowledge Graphs Recommendation Systems

FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs

no code implementations2 Mar 2021 Jan Portisch, Michael Hladik, Heiko Paulheim

This paper presents the FinMatcher system and its results for the FinSim 2021 shared task which is co-located with the Workshop on Financial Technology on the Web (FinWeb) in conjunction with The Web Conference.

Knowledge Graphs

Web Table Classification based on Visual Features

no code implementations25 Feb 2021 Babette Bühler, Heiko Paulheim

Tables on the web constitute a valuable data source for many applications, like factual search and knowledge base augmentation.

Classification General Classification +3

Information Extraction From Co-Occurring Similar Entities

no code implementations10 Feb 2021 Nicolas Heist, Heiko Paulheim

In this paper, we explore how information extracted from similar entities that co-occur in structures like tables or lists can help to increase the coverage of such knowledge graphs.

Knowledge Graphs Question Answering +1

Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019

no code implementations22 Dec 2020 Nacira Abbas, Kholoud Alghamdi, Mortaza Alinam, Francesca Alloatti, Glenda Amaral, Claudia d'Amato, Luigi Asprino, Martin Beno, Felix Bensmann, Russa Biswas, Ling Cai, Riley Capshaw, Valentina Anita Carriero, Irene Celino, Amine Dadoun, Stefano De Giorgis, Harm Delva, John Domingue, Michel Dumontier, Vincent Emonet, Marieke van Erp, Paola Espinoza Arias, Omaima Fallatah, Sebastián Ferrada, Marc Gallofré Ocaña, Michalis Georgiou, Genet Asefa Gesese, Frances Gillis-Webber, Francesca Giovannetti, Marìa Granados Buey, Ismail Harrando, Ivan Heibi, Vitor Horta, Laurine Huber, Federico Igne, Mohamad Yaser Jaradeh, Neha Keshan, Aneta Koleva, Bilal Koteich, Kabul Kurniawan, Mengya Liu, Chuangtao Ma, Lientje Maas, Martin Mansfield, Fabio Mariani, Eleonora Marzi, Sepideh Mesbah, Maheshkumar Mistry, Alba Catalina Morales Tirado, Anna Nguyen, Viet Bach Nguyen, Allard Oelen, Valentina Pasqual, Heiko Paulheim, Axel Polleres, Margherita Porena, Jan Portisch, Valentina Presutti, Kader Pustu-Iren, Ariam Rivas Mendez, Soheil Roshankish, Sebastian Rudolph, Harald Sack, Ahmad Sakor, Jaime Salas, Thomas Schleider, Meilin Shi, Gianmarco Spinaci, Chang Sun, Tabea Tietz, Molka Tounsi Dhouib, Alessandro Umbrico, Wouter van den Berg, Weiqin Xu

Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything.

Common Sense Reasoning Knowledge Graphs

Supervised Ontology and Instance Matching with MELT

no code implementations20 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.

A Knowledge Graph for Assessing Aggressive Tax Planning Strategies

1 code implementation12 Aug 2020 Niklas Lüdemann, Ageda Shiba, Nikolaos Thymianis, Nicolas Heist, Christopher Ludwig, Heiko Paulheim

The taxation of multi-national companies is a complex field, since it is influenced by the legislation of several states.

Visual Analysis of Ontology Matching Results with the MELT Dashboard

no code implementations27 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).

Towards Exploiting Implicit Human Feedback for Improving RDF2vec Embeddings

no code implementations9 Apr 2020 Ahmad Al Taweel, Heiko Paulheim

In a second step, those sequences are processed by the word2vec algorithm for creating the actual embeddings.

Entity Extraction from Wikipedia List Pages

no code implementations11 Mar 2020 Nicolas Heist, Heiko Paulheim

In this paper, we present a two-phased approach for the extraction of entities from Wikipedia's list pages, which have proven to serve as a valuable source of information.

Entity Extraction using GAN Knowledge Graphs

KGvec2go -- Knowledge Graph Embeddings as a Service

no code implementations LREC 2020 Jan Portisch, Michael Hladik, Heiko Paulheim

In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications.

Knowledge Graph Embeddings Knowledge Graphs

The Knowledge Graph Track at OAEI -- Gold Standards, Baselines, and the Golden Hammer Bias

no code implementations24 Feb 2020 Sven Hertling, Heiko Paulheim

The Ontology Alignment Evaluation Initiative (OAEI) is an annual evaluation of ontology matching tools.

Knowledge Graphs

Uncovering the Semantics of Wikipedia Categories

1 code implementation28 Jun 2019 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 disambiguation or semantic similarity estimation.

Entity Disambiguation Knowledge Graphs +2

On Cognitive Preferences and the Plausibility of Rule-based Models

1 code implementation4 Mar 2018 Johannes Fürnkranz, Tomáš Kliegr, Heiko Paulheim

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones.

A Large DataBase of Hypernymy Relations Extracted from the Web.

no code implementations LREC 2016 Julian Seitner, Christian Bizer, Kai Eckert, Stefano Faralli, Robert Meusel, Heiko Paulheim, Simone Paolo Ponzetto

Hypernymy relations (those where an hyponym term shares a {``}isa{''} relationship with his hypernym) play a key role for many Natural Language Processing (NLP) tasks, e. g. ontology learning, automatically building or extending knowledge bases, or word sense disambiguation and induction.

Word Sense Disambiguation

Cannot find the paper you are looking for? You can Submit a new open access paper.