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Data Mining and Semantic Technology Talks in California

2013-07-12
Photo of Dr. Ding

ILS faculty member, Dr. Ying Ding recently made presentations in California on her research in data mining and semantic technology. The talk at the Semantic Technology and Business Conference was given with her colleague Dr. Jie Tang, Associate Professor at Tsinghua University, Beijing, China. Excerpts from the talk abstracts are included below.

In addition to serving as Director of the Ph.D. in Information Science, Dr. Ding will teach ILS courses this fall in both Database Design (Z511) and the Semantic Web (Z636.)


Invited Talk: Semantic Link Prediction for Drug Discovery
Semantic Mining in Translational Medicine, IBM Almaden Research Center,
June 4, 2013, San Jose, CA, USA.

A critical barrier in current drug discovery is the inability to utilize public datasets in an integrated fashion to fully understand the actions of drugs and chemical compounds on biological systems. There is a need to intelligently integrate heterogeneous datasets pertaining to compounds, drugs, targets, genes, diseases, and drug side effects now available to enable effective network data mining algorithms to extract important biological relationships. In this talk, we demonstrate the semantic integration of 25 different databases and develop various mining and predication methods to identify hidden associations that could provide valuable directions for further exploration at the experimental level.

Plenary Talk: Mining Data Semantics in Heterogeneous Networks,
Semantic Technology & Business Conference (SemTech2013),
June 3, 2013, San Francisco, CA, USA.

This presentation will discuss key issues and practices of mining semantics in heterogeneous information networks. Social, information and biological systems usually consist of a large number of interacting, multi-typed components connected via various types of links, which makes heterogeneous networks ubiquitous. Mining semantics from the heterogeneous networks can address several important questions regarding data integration, data analytics and knowledge discovery. Three use cases and their related tools utilizing the developed algorithms to solve real-life problems will be demonstrated.

Posted July 12, 2013