SimonBinPhD Student

Agile Knowledge Engineering and Semantic Web (AKSW)
University of Leipzig

Profiles: DBLP

Hainstraße 11, 04109 Leipzig
sbin@informatik.uni-leipzig.de
Phone: +49-341-9732292

 

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
  • 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


2017

  • J. Lehmann, G. Sejdiu, L. Bühmann, P. Westphal, C. Stadler, I. Ermilov, S. Bin, N. Chakraborty, M. Saleem, A. N. Ngonga, and H. Jabeen, “Distributed Semantic Analytics using the SANSA Stack,” in Proceedings of 16th International Semantic Web Conference – Resources Track (ISWC’2017), 2017.
    [BibTeX] [Abstract] [Download PDF]
    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.

    @InProceedings{lehmann-2017-sansa-iswc,
    Title = {Distributed {S}emantic {A}nalytics using the {SANSA} {S}tack},
    Author = {Lehmann, Jens and Sejdiu, Gezim and B\"uhmann, Lorenz and Westphal, Patrick and Stadler, Claus and Ermilov, Ivan and Bin, Simon and Chakraborty, Nilesh and Saleem, Muhammad and Ngonga, Axel-Cyrille Ngomo and Jabeen, Hajira},
    Booktitle = {Proceedings of 16th International Semantic Web Conference - Resources Track (ISWC'2017)},
    Year = {2017},
    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.},
    Added-at = {2017-07-17T14:46:26.000+0200},
    Biburl = {https://www.bibsonomy.org/bibtex/21ae18ac13750f9cf74227fe0a7c50104/aksw},
    Interhash = {eb99dff0ce6a9cdbce2c4cbea115fbee},
    Intrahash = {1ae18ac13750f9cf74227fe0a7c50104},
    Keywords = {2017 bde buehmann chakraborty group_aksw iermilov lehmann ngonga saleem sbin sejdiu stadler westphal},
    Owner = {iermilov},
    Timestamp = {2017-07-17T14:46:26.000+0200},
    Url = {http://svn.aksw.org/papers/2017/ISWC_SANSA_SoftwareFramework/public.pdf}
    }

  • I. Ermilov, J. Lehmann, G. Sejdiu, L. Bühmann, P. Westphal, C. Stadler, S. Bin, N. Chakraborty, H. Petzka, M. Saleem, A. N. Ngonga, and H. Jabeen, “The Tale of Sansa Spark,” in Proceedings of 16th International Semantic Web Conference, Poster & Demos, 2017.
    [BibTeX] [Download PDF]
    @InProceedings{iermilov-2017-sansa-iswc-demo,
    Title = {The {T}ale of {S}ansa {S}park},
    Author = {Ermilov, Ivan and Lehmann, Jens and Sejdiu, Gezim and B\"uhmann, Lorenz and Westphal, Patrick and Stadler, Claus and Bin, Simon and Chakraborty, Nilesh and Petzka, Henning and Saleem, Muhammad and Ngonga, Axel-Cyrille Ngomo and Jabeen, Hajira},
    Booktitle = {Proceedings of 16th International Semantic Web Conference, Poster \& Demos},
    Year = {2017},
    Added-at = {2017-08-31T16:24:45.000+0200},
    Biburl = {https://www.bibsonomy.org/bibtex/2f9b5a69afa4755944984ae63f59ad146/aksw},
    Interhash = {ebabfe08f697304b399c9b6b89f2829e},
    Intrahash = {f9b5a69afa4755944984ae63f59ad146},
    Keywords = {2017 bde buehmann chakraborty group_aksw iermilov lehmann mole ngonga saleem sbin sejdiu stadler westphal},
    Owner = {iermilov},
    Timestamp = {2017-08-31T16:24:45.000+0200},
    Url = {http://jens-lehmann.org/files/2017/iswc_pd_sansa.pdf}
    }

  • S. Bin, P. Westphal, J. Lehmann, and A. N. Ngonga, “Implementing Scalable Structured Machine Learning for Big Data in the SAKE Project,” in IEEE Big Data Conference 2017, 2017.
    [BibTeX] [Download PDF]
    @inproceedings{bin-2017-sake,
    added-at = {2017-11-17T14:26:26.000+0100},
    author = {Bin, Simon and Westphal, Patrick and Lehmann, Jens and Ngonga, Axel-Cyrille Ngomo},
    biburl = {https://www.bibsonomy.org/bibtex/224f107297aa2a27c82b875e63c9b9055/aksw},
    booktitle = {IEEE Big Data Conference 2017},
    interhash = {8ff7e69474050557c9f872c41433cc04},
    intrahash = {24f107297aa2a27c82b875e63c9b9055},
    keywords = {2017 bin group_aksw lehmann mole ngonga sake westphal},
    timestamp = {2017-11-17T14:26:26.000+0100},
    title = {Implementing Scalable Structured Machine Learning for Big Data in the SAKE Project},
    url = {http://jens-lehmann.org/files/2017/ieee_bigdata_sake.pdf},
    year = 2017
    }

2016

  • S. Bin, L. Bühmann, J. Lehmann, and A. {Ngonga Ngomo}, “Towards SPARQL-Based Induction for Large-Scale RDF Data sets,” in ECAI 2016 – Proceedings of the 22nd European Conference on Artificial Intelligence, 2016, pp. 1551-1552. doi:10.3233/978-1-61499-672-9-1551
    [BibTeX] [Download PDF]
    @InProceedings{sparqllearner,
    Title = {Towards {SPARQL}-Based Induction for Large-Scale {RDF} Data sets},
    Author = {Bin, Simon and B{\"u}hmann, Lorenz and Lehmann, Jens and {Ngonga Ngomo}, Axel-Cyrille},
    Booktitle = {ECAI 2016 - Proceedings of the 22nd European Conference on Artificial Intelligence},
    Year = {2016},
    Editor = {Kaminka, Gal A. and Fox, Maria and Bouquet, Paolo and H{\"u}llermeier, Eyke and Dignum, Virginia and Dignum, Frank and van Harmelen, Frank},
    Pages = {1551--1552},
    Publisher = {IOS Press},
    Series = {Frontiers in Artificial Intelligence and Applications},
    Volume = {285},
    Doi = {10.3233/978-1-61499-672-9-1551},
    ISBN = {978-1-61499-672-9},
    Keywords = {2016 sbin buehmann lehmann ngonga sake group_aksw dllearner},
    Language = {English},
    Url = {http://svn.aksw.org/papers/2016/ECAI_SPARQL_Learner/public.pdf}
    }

2014

  • S. Volke, S. Bin, D. Zeckzer, M. Middendorf, and G. Scheuermann, “Visual Analysis of Discrete Particle Swarm Optimization Using Fitness Landscapes,” in Recent Advances in the Theory and Application of Fitness Landscapes, H. Richter and A. Engelbrecht, Eds., Springer Berlin Heidelberg, 2014, vol. 6, pp. 487-507. doi:10.1007/978-3-642-41888-4_17
    [BibTeX] [Download PDF]
    @InCollection{volke2014visual,
    Title = {Visual Analysis of Discrete Particle Swarm Optimization Using Fitness Landscapes},
    Author = {Volke, Sebastian and Bin, Simon and Zeckzer, Dirk and Middendorf, Martin and Scheuermann, Gerik},
    Booktitle = {Recent Advances in the Theory and Application of Fitness Landscapes},
    Publisher = {Springer Berlin Heidelberg},
    Year = {2014},
    Editor = {Richter, Hendrik and Engelbrecht, Andries},
    Pages = {487-507},
    Series = {Emergence, Complexity and Computation},
    Volume = {6},
    Bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-642-41888-4_17},
    Doi = {10.1007/978-3-642-41888-4_17},
    ISBN = {978-3-642-41887-7},
    Keywords = {2014 sbin visualization optimization landscape},
    Language = {English},
    Url = {http://dx.doi.org/10.1007/978-3-642-41888-4_17}
    }