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KMi Seminars
Topic Models for Data Mining
This event took place on Friday 13 July 2012 at 12:00

 
Dr. Tomoharu Iwata NTT/University of Cambridge


A topic model is a probabilistic generative model for extracting a latent structure from discrete data such as text document. Topic models are successfully used in a wide variety of applications including information retrieval, collaborative filtering and image recognition. In this talk, first I will present basics of topic modelling, such as relations with other probabilistic models, and inference. Then, I will present three applications of topic modelling: social annotation data analysis, multi-scale dynamics analysis, and fashion coordinates recommendation, which I worked on recent years.



 
KMi Seminars Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

Knowledge Management is...


Knowledge Management
Creating learning organisations hinges on managing knowledge at many levels. Knowledge can be provided by individuals or it can be created as a collective effort of a group working together towards a common goal, it can be situated as "war stories" or it can be generalised as guidelines, it can be described informally as comments in a natural language, pictures and technical drawings or it can be formalised as mathematical formulae and rules, it can be expressed explicitly or it can be tacit, embedded in the work product. The recipient of knowledge - the learner - can be an individual or a work group, professionals, university students, schoolchildren or informal communities of interest.
Our aim is to capture, analyse and organise knowledge, regardless of its origin and form and make it available to the learner when needed presented with the necessary context and in a form supporting the learning processes.