Simon Bin left SDA. The profile below reflects the status at the point of his departure and is no longer updated.

PhD Student
Agile Knowledge Engineering and Semantic Web (AKSW)
University of Leipzig
Profiles: DBLP
Goerdellering 9, Room 24, 04109 Leipzig
Short CV
Simon Bin is a PhD Student at the University of Leipzig. Simon’s research interests are in the area of Structured Machine Learning.
Research Interests
- Ontology Learning
- Ontology Debugging
- Reasoning
- Big Structured Machine Learning
Current Projects
- DL-Learner – a tool for supervised Machine Learning in OWL and Description Logics
- QROWD – The power of the Qrowd combines with RDF
- SAKE – With RDF and Machine Learning Getting Results Faster
- SANSA-Stack – Open source platform for distributed data processing for RDF large-scale datasets
- SML-Bench – A Benchmark for Symbolic Supervised Machine Learning from Expressive Structured Data
Publications
2019
SML-Bench - A benchmarking framework for structured machine learning Journal Article
In: Semantic Web, vol. 10, no. 2, pp. 231–245, 2019.
2018
DL-Learner Structured Machine Learning on Semantic Web Data Inproceedings
In: Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27, 2018, pp. 467–471, ACM, 2018.
2017
The Tale of Sansa Spark Inproceedings
In: 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, CEUR-WS.org, 2017.
Implementing scalable structured machine learning for big data in the SAKE project Inproceedings
In: 2017 IEEE International Conference on Big Data, BigData 2017, Boston, MA, USA, December 11-14, 2017, pp. 1400–1407, IEEE Computer Society, 2017.
Distributed Semantic Analytics Using the SANSA Stack Inproceedings
In: The Semantic Web - ISWC 2017 - 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II, pp. 147–155, Springer, 2017.
2016
Towards SPARQL-Based Induction for Large-Scale RDF Data Sets Inproceedings
In: 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), pp. 1551–1552, IOS Press, 2016.
2014
Visual Analysis of Discrete Particle Swarm Optimization Using Fitness Landscapes Incollection
In: Recent Advances in the Theory and Application of Fitness Landscapes, vol. 6, pp. 487-507, Springer Berlin Heidelberg, 2014, ISBN: 978-3-642-41887-7.
Comparing the Optimization Behaviour of Heuristics with Topology Based Visualization Inproceedings
In: Theory and Practice of Natural Computing - Third International Conference, TPNC 2014, Granada, Spain, December 9-11, 2014. Proceedings, pp. 47–58, Springer, 2014.