This document discusses using Schematron for validating XML documents at multiple layers. It notes that while DTDs are useful, Schematron is needed to enforce all business rules and validate semantics. Schematron can validate documents across genres and lifecycle phases. The American Geophysical Union (AGU) uses Schematron extensively to validate over 350 rules across various document types. Building good Schematron involves eliciting requirements, ensuring rules don't conflict, testing extensively, and gaining in-house expertise in Schematron and XSLT.

1. Using Schematron for Appropriate Layer
Validation: a Case Study
Alexander (‘Sasha’) Schwarzman, AGU (sschwarzman@agu.org)
Balisage 2011: The Markup Conference, Montréal, Canada
August 2 – 5, 2011
Appropriate layer validation—advantages
 Even the most “Prussian” DTD cannot enforce all business rules, data types, and house style
 Rules-based checking needed anyway
 May use a “Californian” DTD, such as JATS: de facto industry standard adopted by publishers,
conversion and composition vendors, archives, etc.
 Can use tools developed for JATS: Preview XSLT stylesheets, EPUB conversion processes, etc.
Why Schematron?
 Multiple genres (document types)
 Journal article
 Book chapter
 Book
 Newspaper article
 Different lifecycle phases
 Papers in press (journal article)
 Initial validation (journal article, book chapter)
 Final validation (all genres)
Journal article
Alexander (‘Sasha’) Schwarzman, AGU Balisage 2011: The Markup Conference, Montréal, Canada Page 1 of 3
(sschwarzman@agu.org) August 2–5, 2011

2. Book chaper and book
Newspaper article
Alexander (‘Sasha’) Schwarzman, AGU Balisage 2011: The Markup Conference, Montréal, Canada Page 2 of 3
(sschwarzman@agu.org) August 2–5, 2011

3. AGU Schematrons
FBA IJA FJA SWN FBK EOM EOS PIP IBA
AGUcontribs.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
bibr-adhoc.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
bibr-ids.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
bibr-italics.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
bibr-structures.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
book-bookarticle.sch ✓
book-meta.sch ✓
bookarticle-meta-final.sch ✓ ✓
bookarticle-meta.sch ✓ ✓ ✓
common-back.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
common-final.sch ✓ ✓ ✓ ✓ ✓ ✓
common-meta.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
common.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
dates.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓
eos-only.sch ✓ ✓
filetypes.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
global.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
index-codes.sch ✓ ✓ ✓
index-terms.sch ✓ ✓ ✓ ✓ ✓ ✓
journalarticle-meta-final.sch ✓ ✓ ✓ ✓
journalarticle-meta.sch ✓ ✓ ✓ ✓ ✓ ✓
journalarticle-tech.sch ✓ ✓
mddb-ws.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
names.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
print-final.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
ref-misc.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
setup.sch ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
 9 top-level Schematrons, 27 modules
 350+ requirements checked
 Perform Web Services-based verifications against relational metadata database
 Check data quality, markup integrity, business rules, data types, and house style
 Provide control over production processes
 Work best in oXygen (context-sensitive), can be compiled and integrated into pipeline scripts
Paradigm shift: validation focus moves from XML parser to Schematron engine
☛ Content may be valid to the DTD but make no sense
☛ Semantic integrity now depends on Schematron
☛ Should each Schematron release be preserved and the version info added to metadata?
☛ Constraints on business partners: they must be Schematron-capable and have tools
☛ Schematron does not “fix” problems—people do! Processes & procedures must be defined
How to build a good Schematron
 Elicit, document, convey, and clarify the Requirements
 Ensure Schematron fits into your workflow
 Modularize Schematron
 Ensure that individual Schematron rules aren’t in conflict
 Optimize Schematron performance
 Employ XSLT 2.0
 Test, test, test
 Cultivate Schematron & XSLT 2.0 expertise in-house
Alexander (‘Sasha’) Schwarzman, AGU Balisage 2011: The Markup Conference, Montréal, Canada Page 3 of 3
(sschwarzman@agu.org) August 2–5, 2011