1. Tantan Liu, Fan Wang, Gagan Agrawal The Ohio State University
Applying Data Mining Techniques for Schema Matching across Biological Deep Web Data Sources
Tantan Liu, Fan Wang, Gagan Agrawal The Ohio State University
Scientific deep web data sources
Querying Interface: submitting query
Input schema: describing input attributes
Output Web page: querying results
output schema: describing output attributes
Inter-dependence
A data source provides input for another data source
Input-output attributes matching across multiple data sources
Exploring inter-dependence between data
sources
Automatically generating query plans given users’ queries
Integrating multiple data sources
Motivation
Model for Schemas
Input Schema
Input Attributes -- label and instances
Output Schema
Hierarchical model
Siblings: Related attributes in a table or a separate block
Output Attributes -- label, instances, parent and siblings
Similarity Function
Similarity between corresponding properties
Linguistic similarity is utilized to compute the similarity between strings
System Design
Aim: identifying semantic matching between input attributes and output attributes across multiple data sources
Approaches
Discovering instances for input attributes
Help web pages -- querying interfaces and their linked web pages
Output web pages of other data sources
Schema matching via clustering
Schema Matching
Discovering Instances for Input Attributes
Discover semantic correspondence between attributes
Mapping attributes are grouped together
Hierarchical clustering
Similarity between attributes are calculated
At each step, groups with largest similarity are merged into one group
Input and output attributes in the same group: inter-dependence between their data sources
Bridge effect -- two attributes are similar if they are both similar to a third attribute.
From output web pages
Discovering instances from output web pages
Iteratively borrowing instances from related output attributes
More output attributes and instances provide more instances for input attributes
From help web pages
Potential web pages linked from query interface
Identifying help web pages by anchor text of links, e.g. ‘search hint’
Locating potential instances by meaningful keywords, e.g. ‘for instance’
Discovering potential domain-specific instances, less frequently used in other domains
Validating potential instances through querying interface
We applied a series of data mining techniques for schema matching.
We show that the instances for deep web data sources can be discovered from the query interfaces themselves.
We also show the instances are obtained from output pages of related data sources
Our approach has been effective on a number of biological data sources.
Conclusion
Experiment Results
Data Sources
11 data sources with 24 query interfaces
Data sources provide SNP, Gene, Protein and related information
Instances discovered from Interface
Accuracy on different subsets
Impact of size of input instance sets
Accuracy of all types of schema matching
Tantan Liu
Fan Wang
Gagan Agrawal