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Der Campus im Frühling. Bildinformationen anzeigen

Der Campus im Frühling.

Foto: Universität Paderborn, Kamil Glabica.

Dr. Stefan Heindorf

Kontakt
Vita
Publikationen
Dr. Stefan Heindorf
12/2019 - heute

Universität Paderborn

Postdoc

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


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2020

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


2019


Vandalism Detection in Crowdsourced Knowledge Bases

S. Heindorf, Universität Paderborn, 2019



2018

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.


2017

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, schema.org, 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


2016


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).


2015


2012

Optimized XPath evaluation for Schema-compressed XML data

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


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