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Priv.Doz. Mag. Dr. Gerhard Wohlgenannt

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Gerhard Wohlgenannt

Assist­ant Pro­fessor

WU (Vi­enna Uni­versity of Eco­nom­ics and Busi­ness)
De­part­ment of In­form­a­tion Sys­tems and Op­er­a­tions
In­sti­tute for In­form­a­tion Busi­ness
Build­ing D2, 3rd Floor
Welthan­delsplatz 1, 1020 Vi­enna, Aus­tria

Email: ger­hard.wohl­genan­nt@wu.ac.at
Con­sulta­tion on Ap­point­ment

Ger­hard Wohl­genannt is an assist­ant pro­fessor at the In­sti­tute of In­form­a­tion Busi­ness of the Vi­enna Uni­versity of Eco­nom­ics and Busi­ness. He com­pleted his PhD thesis in the field of on­to­logy learn­ing on a method for learn­ing on­to­logy re­la­tions by com­bin­ing cor­pus-­based tech­niques and reas­on­ing on data from Se­mantic Web sources in 2010. In July 2016, he re­ceived his hab­il­it­a­tion de­gree (ve­nia do­cendi) in Busi­ness In­form­at­ics ("Wirtschaftsin­form­atik").  

Ger­hard Wohl­genannt par­ti­cip­ated in a num­ber of re­search pro­jects at the Vi­enna Uni­versity of Eco­nom­ics and Busi­ness such as DI­VINE, RAVEN, IDIOM and AVALON, where he ex­ten­ded the web­Lyz­ard ar­chi­tec­ture and se­mantic ser­vices and used his ex­pert­ise in tech­no­lo­gies such as the Py­thon pro­gram­ming lan­guage and the Post­gr­eSQL DBMS.
He re­cently served as pro­ject co­ordin­ator in the DI­VINE pro­ject (fun­ded by FFG), and as Co-In­vestig­ator in the uComp pro­ject (fun­ded by EU FP7/CHIST-ERA). 

Re­search In­terests

  • On­to­logy En­gin­eer­ing and On­to­logy Evolu­tion

    • On­to­logy Learn­ing/Ex­ten­sion from text, so­cial sources and struc­tured data

  • Se­mantic (Web) Tech­no­lo­gies / Linked Data

  • In­form­a­tion Ex­trac­tion / Data Min­ing

  • Nat­ural Lan­guage Pro­cessing, Text Min­ing

  • Data­base Sys­tems

  • Hu­man Com­pu­ta­tion (Crowd­sourcing, Games with a Pur­pose) 

Thesis Top­ics

see Re­search In­terests, here you find more in­fos on thesis top­ics and com­pleted thesis

Lec­tures at WU

Pub­lic­a­tions

Pro­jects

Soft­ware

Py­thon-im­ple­ment­a­tion of "spec­tral as­so­ci­ation", a method for solv­ing spread­ing activ­a­tion net­works in one go.