@inproceedings{DBLP:conf/www/BuhmannLWB18,
title = {DL-Learner Structured Machine Learning on Semantic Web Data},
author = {Lorenz Bühmann and
Jens Lehmann and
Patrick Westphal and
Simon Bin},
url = {https://doi.org/10.1145/3184558.3186235},
doi = {10.1145/3184558.3186235},
year = {2018},
date = {2018-01-01},
booktitle = {Companion of the The Web Conference 2018 on The Web Conference 2018,
WWW 2018, Lyon , France, April 23-27, 2018},
pages = {467--471},
publisher = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{DBLP:conf/semweb/ErmilovLSBWSBCP17,
title = {The Tale of Sansa Spark},
author = {Ivan Ermilov and
Jens Lehmann and
Gezim Sejdiu and
Lorenz Bühmann and
Patrick Westphal and
Claus Stadler and
Simon Bin and
Nilesh Chakraborty and
Henning Petzka and
Muhammad Saleem and
Axel-Cyrille Ngonga Ngomo and
Hajira Jabeen},
url = {http://ceur-ws.org/Vol-1963/paper552.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the ISWC 2017 Posters & Demonstrations and Industry
Tracks co-located with 16th International Semantic Web Conference
(ISWC 2017), Vienna, Austria, October 23rd - to - 25th, 2017},
volume = {1963},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{DBLP:conf/bigdataconf/BinWLN17,
title = {Implementing scalable structured machine learning for big data in
the SAKE project},
author = {Simon Bin and
Patrick Westphal and
Jens Lehmann and
Axel Ngonga},
url = {http://jens-lehmann.org/files/2017/ieee_bigdata_sake.pdf},
doi = {10.1109/BigData.2017.8258073},
year = {2017},
date = {2017-01-01},
booktitle = {2017 IEEE International Conference on Big Data, BigData 2017, Boston,
MA, USA, December 11-14, 2017},
pages = {1400--1407},
publisher = {IEEE Computer Society},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{DBLP:conf/semweb/LehmannSBWSEBCS17,
title = {Distributed Semantic Analytics Using the SANSA Stack},
author = {Jens Lehmann and
Gezim Sejdiu and
Lorenz Bühmann and
Patrick Westphal and
Claus Stadler and
Ivan Ermilov and
Simon Bin and
Nilesh Chakraborty and
Muhammad Saleem and
Axel-Cyrille Ngonga Ngomo and
Hajira Jabeen},
url = {http://svn.aksw.org/papers/2017/ISWC_SANSA_SoftwareFramework/public.pdf},
doi = {10.1007/978-3-319-68204-4_15},
year = {2017},
date = {2017-01-01},
booktitle = {The Semantic Web - ISWC 2017 - 16th International Semantic Web Conference,
Vienna, Austria, October 21-25, 2017, Proceedings, Part II},
volume = {10588},
pages = {147--155},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
abstract = {Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies. A major research challenge today is to perform scalable analysis of large-scale knowledge graphs to facilitate applications like link prediction, knowledge base completion and question answering. Most analytics approaches, which scale horizontally (i.e., can be executed in a distributed environment) work on simple feature-vector-based input rather than more expressive knowledge structures. On the other hand, analytics methods which exploit expressive structures usually do not scale well to very large knowledge bases. This software framework paper describes the ongoing project Semantic Analytics Stack (SANSA) which supports expressive and scalable semantic analytics by providing functionality for distributed in-memory computing for RDF data. The library provides APIs for RDF storage, querying using SPARQL and forward chaining inference. It includes several machine learning algorithms for RDF knowledge graphs. The article describes the vision, architecture and use cases of SANSA.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{DBLP:conf/ecai/BinBLN16,
title = {Towards SPARQL-Based Induction for Large-Scale RDF Data Sets},
author = {Simon Bin and
Lorenz Bühmann and
Jens Lehmann and
Axel-Cyrille Ngonga Ngomo},
url = {http://svn.aksw.org/papers/2016/ECAI_SPARQL_Learner/public.pdf},
doi = {10.3233/978-1-61499-672-9-1551},
year = {2016},
date = {2016-01-01},
booktitle = {ECAI 2016 - 22nd European Conference on Artificial Intelligence,
29 August-2 September 2016, The Hague, The Netherlands - Including
Prestigious Applications of Artificial Intelligence (PAIS 2016)},
volume = {285},
pages = {1551--1552},
publisher = {IOS Press},
series = {Frontiers in Artificial Intelligence and Applications},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{DBLP:conf/tpnc/BinVSM14,
title = {Comparing the Optimization Behaviour of Heuristics with Topology Based
Visualization},
author = {Simon Bin and
Sebastian Volke and
Gerik Scheuermann and
Martin Middendorf},
url = {https://doi.org/10.1007/978-3-319-13749-0_5},
doi = {10.1007/978-3-319-13749-0_5},
year = {2014},
date = {2014-01-01},
booktitle = {Theory and Practice of Natural Computing - Third International Conference,
TPNC 2014, Granada, Spain, December 9-11, 2014. Proceedings},
volume = {8890},
pages = {47--58},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}