NDLI: Graph and Topological Structure Mining on Scientific Articles

Content Provider	IEEE Xplore Digital Library
Author	Fan Wang Ruoming Jin Agrawal, G. Piontkivska, H.
Copyright Year	2007
Description	Author affiliation: Ohio State Univ., Columbus (Fan Wang)
Abstract	In this paper, we investigate a new approach for literature mining. We use frequent subgraph mining, and its generalization topological structure mining, for finding interesting relationships between gene names and other key biological terms from the text of scientific articles. We show how we can find keywords of interest and represent them as nodes of the graphs. We also propose several methods for inserting edges between these nodes. Our study initially focused on comparing: 1) different methods for constructing edges, and 2) patterns found from sub-graph mining and topological structure mining. Subsequently, we analyzed several frequent topological minors reported by our experiments, and explained their scientific significance. Overall, our study shows the following. First, a simple method of constructing edges, which is based on sliding windows, seems to provide the best results. Second, we are able to find much larger number of well-known and meaningful topological patterns with high support values, as compared to sub-graphs. Overall, the frequent topological minors our algorithm found correspond well to known relationships between genes and biological terms. Thus, we believe that topological structure mining can be a very valuable tool for researchers who are not deeply familiar with the existing literature, and want to obtain a quick summary about known relationships among key scientific names or terms.
Starting Page	1318
Ending Page	1322
File Size	3555433
Page Count	5
File Format	PDF
ISBN	9781424415090
DOI	10.1109/BIBE.2007.4375739
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Data mining Biology Social network services Pattern matching Diseases Proteins Sequences
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Multi-Constrained Graph Pattern Matching in large-scale contextual social graphs

ORIGAMI: Mining Representative Orthogonal Graph Patterns

MuGRAM: An approach for multi-labelled graph matching

Pattern Mining in Frequent Dynamic Subgraphs

Social Graph Generation & Forecasting Using Social Network Mining

Data Mining Through Fuzzy Social Network Analysis

Emerging Graph Queries in Linked Data

GRAPHITE: A Visual Query System for Large Graphs

Anti-monotonic Overlap-Graph Support Measures

Graph and Topological Structure Mining on Scientific Articles

Similar Documents

Multi-Constrained Graph Pattern Matching in large-scale contextual social graphs

ORIGAMI: Mining Representative Orthogonal Graph Patterns

MuGRAM: An approach for multi-labelled graph matching

Pattern Mining in Frequent Dynamic Subgraphs

Social Graph Generation & Forecasting Using Social Network Mining

Data Mining Through Fuzzy Social Network Analysis

Emerging Graph Queries in Linked Data

GRAPHITE: A Visual Query System for Large Graphs

Anti-monotonic Overlap-Graph Support Measures

Graph and Topological Structure Mining on Scientific Articles