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13.1 - Semantic Information Extraction, Integration, and Visualization for Big Data Analytics

Project - Summary


Free text documents such as scientific literature contain abundant knowledge about relationships among concepts or entities. Unfortunately, this type of knowledge is expressed in natural language, where different types of relationships are not explicitly categorized. In this project, we developed techniques for extracting structured knowledge from unstructured data through weak supervision over existing sources of knowledge.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Yuan An PI Not available Not available Drexel University
Mengwen Liu PhD Student Not available Not available Drexel University
Yuan Ling PhD Student Not available Not available Drexel University
Yizhou Zang PhD Student Not available Not available Drexel University

Project - Impact and Uses/Benefits

Working with the IAB member Elsevier, we developed the proposed distant supervision approach for gene expression relation extraction. The development was primarily led by Elsevier’s particular interests. However, the approach can be easily generalized to other IAB members’ data sets. The outcomes of the project provide techniques for easily processing big data in analytic environments. The results of the study improve productivity for extracting greater value from big unstructured data.

The modules of the system are implemented in Python and Java languages. The entire system consists of three subsystems:

  • Data Preparation
  • Entity Annotation
  • Relation Extraction

Project - Deep Dive

Project - Documents

FilenameFilesizeLast modified
13.1_year_2_presentation.pptx1.5 MiB2019/08/22 11:13
13.1_year_2_ip_disclosure_letter.pdf1004.5 KiB2019/08/22 11:13
13.1_year2_final_project_report_combined.pdf5.1 MiB2019/08/22 10:12
projects/year2/13.1.txt · Last modified: 2019/08/22 10:13 by sally.johnson