Digging for knowledge with information extraction: a case study on human gene-disease associations

Authors:
Markus Bundschus;Anna Bauer-Mehren;Volker Tresp;Laura Furlong;Hans-Peter Kriegel
Affiliations:
LMU München, Munich, Germany;IMIM, Barcelona, Spain;Siemens AG, Munich, Germany;IMIM, Barcelona, Spain;LMU München, Munich, Germany
Venue:
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Year:
2010

Citing 3
Cited 1

Autonomously semantifying wikipedia

Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Cytoscape ESP

Bioinformatics
Information Extraction

Foundations and Trends in Databases

Knowing where and how criminal organizations operate using web content

Proceedings of the 21st ACM international conference on Information and knowledge management

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present the information extraction system Text2SemRel. The system (semi-) automatically constructs knowledge bases from textual data consisting of facts about entities using semantic relations. An integral part of the system is a graph-based interactive visualization and search layer. The second contribution in this paper is the presentation of a case study on the (semi-) automatic construction of a knowledge base consisting of gene-disease associations. The resulting knowledge base, the Literature-derived Human Gene-Disease Network (LHGDN), is now an integral part of the Linked Life Data initiative and represents currently the largest publicly available gene-disease repository. The LHGDN is compared against several curated state of the art databases. A unique feature of the LHGDN is that the semantics of the associations constitute a wide variety of biomolecular conditions.