This document presents a methodology for refactoring metadata by systematically comparing metadata structures. It discusses identifying points of comparison between different metadata standards, using various techniques like tree matching, regular expression matching, and graph matching to analyze syntactic and semantic similarities and differences. The goal is to determine architectural compatibility between metadata schemas to facilitate efficient refactoring.

1. Refactoring Metadata:
Finding architectural
compatibility through
structural comparisons
Baden Hughes
Department of Computer Science and Software Engineering
The University of Melbourne
May 2-6, 2004 Copyright © 2004 Baden Hughes 1

2. Agenda
• Motivation for Refactoring Metadata
• Setting the Context
• Identifying Points of Comparison
• Goals for Structural Comparison
• Methods for Structural Comparison
• Refactoring in Practice
• Principles for Robust Instances
• Conclusion
May 2-6, 2004 Copyright © 2004 Baden Hughes 2

3. Motivations for Refactoring
Metadata
• The need to addressing the problem of metadata volatility is
conceptually juxtaposed with the motivation for metadata
creation
• XML technologies have become pervasive within the
metadata domain
• Different communities = different standards = different
degrees of maturity in metadata adoption resulting in
metadata being (highly?) variable even within an organization
• Systematically determining similarity and difference is the key
to effective refactoring of metadata
• Automatically determining similarity and difference is the key
to efficient refactoring of metadata
May 2-6, 2004 Copyright © 2004 Baden Hughes 3

4. Setting the Context
• XML-based metadata from natural language engineering
and digital libraries
• Wide variety of
– traditions of metadata development
– technologies for metadata implementation
– objects described by metadata
– granularity of metadata descriptions
• Motivated by interoperability analysis
• Seeking to leverage processes not dissimilar to
database schema comparisons
May 2-6, 2004 Copyright © 2004 Baden Hughes 4

5. Identifying Points of Comparison
• Robust instances require both syntactic and semantic
analysis
• Points of comparison
– XML Document Instance
– DTDs
– Schemata
– Namespaces
– RDF Instances
– Ontologies
• Likely that different methods are required for each
different input
May 2-6, 2004 Copyright © 2004 Baden Hughes 5

6. Goals of Structural Comparison
• While validation of XML based metadata does contribute
to the quality of metadata, it does not necessarily assist
in determining architectural compatibility
• Systematic, iterative evaluation of metadata
architectures can contribute to maturity of XML based
metadata
• Quantifying the degree of syntactic and semantic
similarity is an important first step in the refactoring
process – it may in fact demonstrate viability
May 2-6, 2004 Copyright © 2004 Baden Hughes 6

7. Methods for Structural Comparison
• Different methods for structural comparison
depending on the input
– XML documents: trees
– DTDs: regexps and feature structures
– XML Namespaces: feature structures
– XML Schemas: regexps and graph matching
– RDF Instances: graph matching
– Ontologies: feature structures and graph matching
May 2-6, 2004 Copyright © 2004 Baden Hughes 7

8. Tree Matching
• Common conception of an XML document as a tree
structure
• Tree matching is a widely used IE/IR technique for
structured data, and is applicable to XML based
metadata
• Tree matching is largely derivative from pattern
matching, and is largely independent of syntactic or
semantic constraints
• While tree matching can provide basic information about
the similarity of two documents, for architectural
compatibility a deeper analysis is required
May 2-6, 2004 Copyright © 2004 Baden Hughes 8

9. Regular Expression Matching
• DTD syntax is derived from regular expressions
• Well known evaluation methodologies for regexps are
applicable to DTDs
• In contrast to pure syntactic comparison, regexp
matching allows the discovery of the legal constituents of
syntactic structures
• Regexp evaluation is is a highly efficient exercise even
on large metadata collections, and widely implemented
in common programming languages
May 2-6, 2004 Copyright © 2004 Baden Hughes 9

10. Feature Structure Matching
• Typed feature structures are widely used for
deriving controlled vocabularies – XML attribute
instances are typically able to be reduced to
typed feature structures for comparison
• Evaluation of the semantic content of feature
structures is well grounded in formal logic
• Feature structure comparisons can also reveal
syntactic constraints expressed as
dimensionality of feature matrices
May 2-6, 2004 Copyright © 2004 Baden Hughes 10

11. Graph Matching
• Rich XML representations such as RDF can be
construed as a series of arcs and nodes, allowing the
adoption of graph theory techniques for the
determination of isomorphism
• Finding the minimum and maximum common subgraphs
is a technique which can be used to determine
architectural compatibility in the syntactic domain
• Graph matching is primarily syntactic, although it can
also be applied to semantic analysis on sources such as
ontologies
May 2-6, 2004 Copyright © 2004 Baden Hughes 11

12. Refactoring in Practice
• XML Documents
• DTDs
• Namespaces
• Schemata
• RDF instances
• Ontologies
• See http://www.cs.mu.oz.au/~badenh/projects/metadata-comparison
for demo materials
May 2-6, 2004 Copyright © 2004 Baden Hughes 12

13. Principles for Robust Instances
• Both syntactic and semantic analysis are required
• Initiate comparisons at the highest level, and proceed
downwards – higher level incompatibilities are more
complex to resolve
• Quantifying degree of similarity is extremely important as
it impacts directly on the complexity of refactoring
processes
• Accurately identified commonalities at both syntactic and
semantic levels can be leveraged efficiently
May 2-6, 2004 Copyright © 2004 Baden Hughes 13

14. Conclusion
• Adopting and permuting a range of techniques
for structural comparison from a variety of other
disciplines can lead to efficient methods for
metadata structural analysis and consequently
refactoring
• Large scale metadata management requires an
automated approach to both syntactic and
semantic evaluation in order to contribute to ROI
May 2-6, 2004 Copyright © 2004 Baden Hughes 14

15. Acknowledgements
• National Science Foundation Grant
Number 9910603 (International Standards
in Language Engineering)
• National Science Foundation Grant
Number 0317826 (Querying Linguistic
Databases)
May 2-6, 2004 Copyright © 2004 Baden Hughes 15