Dr. Sebastian Tramp

Dr. Sebastian Tramp
Depiction of Dr. Sebastian Tramp
Address Work
eccenca GmbH
Hainstraße 8, 04109 Leipzig
Email Office
Phone Work
Fax Work

@DBLP @github @Google Scholar @Google+ @LinkedIn @Microsoft Academic Search @ResearchGate @XING

I am an AKSW founding member, former head of the Emergent Semantics research group and now associated AKSW member and Head of Development at eccenca GmbH.

I am interested in students who want to work in the Semantic Web in business context.

I provide supervision of bachelor and master thesis, practical work courses as well as student working contracts.

Feel free to drop me a note if you are interested working with me and eccenca GmbH.

My research interests are:

  • Distributed Semantic Social as well as Business Networks
  • Security / trust / privacy in the Semantic Web
  • Basic Web information technologies (Web data integration, personalization techniques, architecture of Web information systems)
  • Semantic Web infrastructure and languages (query, rule and update languages)
  • Semantic Web applications

Current Projects

  • LDAP 2 SPARQLAccessing RDF Knowledge Bases via LDAP Clients
  • LEDSLinked Enterprise Data Services
  • Semantic PingbackAdding a social dimension to the Linked Data Web
  • Xturtlean eclipse / Xtext2 based editor for RDF/Turtle files

Past Projects

  • 5. Leipziger Semantic Web Tag (LSWT2013)Von Big Data zu Smart Data
  • aksw.orga linked data driven web page rendered by OntoWiki site extension
  • DSSNtowards a global Distributed Semantic Social Network
  • ErfurtPHP5 / Zend based Semantic Web API for Social Semantic Software
  • KeyNode.jsNext level web presentations
  • LE4SWRegional Technology Platform of Social Semantic Collaboration
  • LOD2Creating Knowledge out of Interlinked Data
  • Mobile Social Semantic Webweaving a distributed, semantic social network for mobile users
  • OntoWikia tool providing support for agile, distributed knowledge engineering scenarios
  • OntoWiki MobileKnowledge Management in your Pocket
  • OntoWiki.euSocial Semantic Collaboration for EKM, E-Learning & E-Tourism
  • RDFAPI-JSUse JavaScript RDFa Widgets for Model/View Separation inside Read/Write Websites
  • RDFauthoris an editing solution for distributed and syndicated structured content on the World Wide Web
  • Semantic LDAPBringing together LDAP and the Semantic Web
  • SoftWikiSemantics- and Community-Based Requirements Engineering
  • Triplifyprovides a building block for the 'semantification' of Web applications
  • XodxA basic DSSN node implementation
  • xOperatorcombines advantages of social network websites with instant messaging



by (Editors: ) [BibTex of ]


seebi pushed to master at vocol/scor ( 2015-06-03T20:38:59+02:00 )


Jun 3, 2015 seebi pushed to master at vocol/scor 3241d5e fix subsection headings Read more about "seebi pushed to master at vocol/scor"

seebi pushed to master at vocol/scor ( 2015-06-03T20:38:02+02:00 )


Jun 3, 2015 seebi pushed to master at vocol/scor 8b92c79 fix markdown Read more about "seebi pushed to master at vocol/scor"

seebi pushed to master at vocol/odette ( 2015-05-29T13:40:00+02:00 )


May 29, 2015 seebi pushed to master at vocol/odette fd84c04 add some new props, upgrade example Read more about "seebi pushed to master at vocol/odette"

Graph Engine ( 2015-05-20T15:04:01+02:00 )


Graph Engine (GE) is a distributed, in-memory, large graph processing engine, underpinned by a strongly-typed RAM store and a general computation engine. The distributed RAM store provides a globally addressable high-performance key-value store over a cluster of machines. Through the RAM store, GE enables the fast random data access power over a large distributed data set. Read more about "Graph Engine"

DeepDive ( 2015-05-20T13:49:30+02:00 )


DeepDive helps create structured data (SQL tables) from unstructured information (text documents) and integrate such data with an existing structured database. DeepDive is used to extract sophisticated relationships between entities and make inferences about facts involving those entities. DeepDive can process structured, unstructured, clean, or noisy data and put the results into a database. Once ina database, one can use a variety of standard tools that consume structured data, e.g. Read more about "DeepDive"