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Dr. Stefan Heindorf

Dr. Stefan Heindorf
12/2019 - today

Universität Paderborn


10/2013 - 12/2019

Universität Paderborn

PhD in Computer Science

04/2011 - 09/2013

Universität Paderborn

Master of Science (M.Sc.) in Computer Science

10/2007 - 03/2011

Universität Paderborn

Bachelor of Science (B.Sc.) in Computer Science

Open list in Research Information System


COVIDPUBGRAPH: A FAIR Knowledge Graph of COVID-19 Publications

S.. Pestryakova, D. Vollmers, M. Sherif, S. Heindorf, M.. Saleem, D. Moussallem, A. Ngonga Ngomo, Scientific Data (2022)

EvoLearner: Learning Description Logics with Evolutionary Algorithms

S. Heindorf, L. Blübaum, N. Düsterhus, T. Werner, V.N. Golani, C. Demir, A. Ngonga Ngomo, in: WWW, ACM, 2022, pp. 818-828

Classifying nodes in knowledge graphs is an important task, e.g., predicting missing types of entities, predicting which molecules cause cancer, or predicting which drugs are promising treatment candidates. While black-box models often achieve high predictive performance, they are only post-hoc and locally explainable and do not allow the learned model to be easily enriched with domain knowledge. Towards this end, learning description logic concepts from positive and negative examples has been proposed. However, learning such concepts often takes a long time and state-of-the-art approaches provide limited support for literal data values, although they are crucial for many applications. In this paper, we propose EvoLearner - an evolutionary approach to learn ALCQ(D), which is the attributive language with complement (ALC) paired with qualified cardinality restrictions (Q) and data properties (D). We contribute a novel initialization method for the initial population: starting from positive examples (nodes in the knowledge graph), we perform biased random walks and translate them to description logic concepts. Moreover, we improve support for data properties by maximizing information gain when deciding where to split the data. We show that our approach significantly outperforms the state of the art on the benchmarking framework SML-Bench for structured machine learning. Our ablation study confirms that this is due to our novel initialization method and support for data properties.


Convolutional Hypercomplex Embeddings for Link Prediction

C. Demir, D. Moussallem, S. Heindorf, A. Ngonga Ngomo, CoRR (2021), abs/2106.15230

Prediction of concept lengths for fast concept learning in description logics

N. Jean Kouagou, S. Heindorf, C. Demir, A. Ngonga Ngomo, CoRR (2021), abs/2107.04911

Convolutional Hypercomplex Embeddings for Link Prediction

C. Demir, D. Moussallem, S. Heindorf, A. Ngonga Ngomo, in: The 13th Asian Conference on Machine Learning, ACML 2021, 2021

Knowledge graph embedding research has mainly focused on the two smallest normed division algebras, $\mathbb{R}$ and $\mathbb{C}$. Recent results suggest that trilinear products of quaternion-valued embeddings can be a more effective means to tackle link prediction. In addition, models based on convolutions on real-valued embeddings often yield state-of-the-art results for link prediction. In this paper, we investigate a composition of convolution operations with hypercomplex multiplications. We propose the four approaches QMult, OMult, ConvQ and ConvO to tackle the link prediction problem. QMult and OMult can be considered as quaternion and octonion extensions of previous state-of-the-art approaches, including DistMult and ComplEx. ConvQ and ConvO build upon QMult and OMult by including convolution operations in a way inspired by the residual learning framework. We evaluated our approaches on seven link prediction datasets including WN18RR, FB15K-237 and YAGO3-10. Experimental results suggest that the benefits of learning hypercomplex-valued vector representations become more apparent as the size and complexity of the knowledge graph grows. ConvO outperforms state-of-the-art approaches on FB15K-237 in MRR, Hit@1 and Hit@3, while QMult, OMult, ConvQ and ConvO outperform state-of-the-approaches on YAGO3-10 in all metrics. Results also suggest that link prediction performances can be further improved via prediction averaging. To foster reproducible research, we provide an open-source implementation of approaches, including training and evaluation scripts as well as pretrained models.


CauseNet: Towards a Causality Graph Extracted from the Web

S. Heindorf, Y. Scholten, H. Wachsmuth, A. Ngonga Ngomo, M. Potthast, in: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020), 2020, pp. 3023-3030



Vandalism Detection in Crowdsourced Knowledge Bases

S. Heindorf, Universität Paderborn, 2019


Semantic Data Mediator: Linking Services to Websites

D. Wolters, S. Heindorf, J. Kirchhoff, G. Engels, in: Service-Oriented Computing -- ICSOC 2017 Workshops, Springer International Publishing, 2018, pp. 388-392

Many websites offer links to social media sites for convenient content sharing. Unfortunately, those sharing capabilities are quite restricted and it is seldom possible to share content with other services, like those provided by a user's favorite applications or smart devices. In this paper, we present Semantic Data Mediator (SDM) --- a flexible middleware linking a vast number of services to millions of websites. Based on reusable repositories of service descriptions defined by the crowd, users can easily fill a personal registry with their favorite services, which can then be linked to websites by SDM. For this, SDM leverages semantic data, which is already available on millions of websites due to search engine optimization. Further support for our approach from website or service developers is not required. To enable the use of a broad range of services, data conversion services are automatically composed by SDM to transform data according to the needs of the different services. In addition to linking web services, various service adapters allow services of applications and smart devices to be linked as well. We have fully implemented our approach and present a real-world case study demonstrating its feasibility and usefulness.


Linking Services to Websites by Leveraging Semantic Data

D. Wolters, S. Heindorf, J. Kirchhoff, G. Engels, in: 2017 IEEE International Conference on Web Services (ICWS), IEEE, 2017

Websites increasingly embed semantic data for search engine optimization. The most common ontology for semantic data,, is supported by all major search engines and describes over 500 data types, including calendar events, recipes, products, and TV shows. As of today, users wishing to pass this data to their favorite applications, e.g., their calendars, cookbooks, price comparison applications or even smart devices such as TV receivers, rely on cumbersome and error-prone workarounds such as reentering the data or a series of copy and paste operations. In this paper, we present Semantic Data Mediator (SDM), an approach that allows the easy transfer of semantic data to a multitude of services, ranging from web services to applications installed on different devices. SDM extracts semantic data from the currently displayed web page on the client-side, offers suitable services to the user, and by the press of a button, forwards this data to the desired service while doing all the necessary data conversion and service interface adaptation in between. To realize this, we built a reusable repository of service descriptions, data converters, and service adapters, which can be extended by the crowd. Our approach for linking services to websites relies solely on semantic data and does not require any additional support by either website or service developers. We have fully implemented our approach and present a real-world case study demonstrating its feasibility and usefulness.

Overview of the Wikidata Vandalism Detection Task at WSDM Cup 2017

S. Heindorf, M. Potthast, G. Engels, B. Stein, in: WSDM Cup 2017 Notebook Papers, 2017


Vandalism Detection in Wikidata

S. Heindorf, M. Potthast, B. Stein, G. Engels, in: Proceedings of the 25th International Conference on Information and Knowledge Management (CIKM 2016), 2016, pp. 327--336

Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. Its knowledge is increasingly used within Wikipedia itself and various other kinds of information systems, imposing high demands on its integrity.Wikidata can be edited by anyone and, unfortunately, it frequently gets vandalized, exposing all information systems using it to the risk of spreading vandalized and falsified information. In this paper, we present a new machine learning-based approach to detect vandalism in Wikidata.We propose a set of 47 features that exploit both content and context information, and we report on 4 classifiers of increasing effectiveness tailored to this learning task. Our approach is evaluated on the recently published Wikidata Vandalism Corpus WDVC-2015 and it achieves an area under curve value of the receiver operating characteristic, ROC-AUC, of 0.991. It significantly outperforms the state of the art represented by the rule-based Wikidata Abuse Filter (0.865 ROC-AUC) and a prototypical vandalism detector recently introduced by Wikimedia within the Objective Revision Evaluation Service (0.859 ROC-AUC).



Optimized XPath evaluation for Schema-compressed XML data

S. Böttcher, R. Hartel, S. Heindorf, in: ADC, Australian Computer Society, 2012, pp. 137-144

Open list in Research Information System

The University for the Information Society