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Machine Learning Techniques for Knowledge Graphs and Natural Language Processing

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Machine Learning Techniques for Knowledge Graphs and Natural Language Processing

Veranstaltungsart:
Kolloquium Angewandte Informatik




This talk will focus on using data mining and machine learning techniques applied to Knowledge Graphs and Natural Language Processing. First part of the talk gives an in-sight into various methods and visualization tools allowing interactive knowledge discovery over the web of data for data analytics. A method based on association rule mining for knowledge base completion will also be discussed.

The second part of this talk dives into a combination of Knowledge Graphs and Deep Learning methods for NLP tasks, i.e., it discusses the details of Framester, a linguistic linked data hub with a recent addition of MetaNet (a resource for Metaphors). This resource is further used for generating Frame/Role Embeddings for knowledge reconciliation over the knowledge graphs generated from text.

The third part of this talk gives a vision on other Natural Language Processing tasks that can be performed using the previously defined resources with Deep Learning Techniques such as semantic textual similarity, refining MetaNet metaphors, metaphor generation, metaphor detection/interpretation etc.

(Mehwish Alam)




Start: 25. Januar 2019 um 14:00
Ende: 25. Januar 2019 um 15:30


Im Gebäude 05.20, Raum: 3A-11.2

Veranstaltung vormerken: (iCal)


Veranstalter: Forschungsgruppe(n) Information Service Engineering
Information: Media:Alam 25-01-2019.pdf