1. Inferring XML Schema Definitions from XML Data
Eert Jan Bex, Frank Neven, Stijn Vansummeren
Hasselt Univ. and transnational Univ. of Limburg, Belgium
VLDB 2007
Summarized by Chulki Lee, IDS Lab., Seoul National University
Presented by Chulki Lee, IDS Lab., Seoul National University
Many parts from author’s slide (

2. Inferring XML Schema Why schemas? Why infer schemas?
automation & optimization of search
integration of XML data sources …
Why infer schemas?
50% of XML on the web have none
33% of schemas are not valid
Why infer XSD? (XML Schema Definition)
DTD (Document Type Definitions) has limitations
element type only depend on the element’s name (not consider path)

3. Example: DTD vs. XSD
name
type

4. Theorem Inferring XSD from XML corpus is
impossible to learn from positive data only
Content model of an element is
uniquely determined by the path from the root to that element

5. Observation: local context
XSD is k-local
its content models depend only on labels up to the k-th ancestor
98% of XSD, k = 2

6. Observation: SORE Single Occurrence Regular Expression (SORE)
What’s SORE
title, (author, affiliation?)+, abstract
What’s not SORE
title, ((author, affiliation)++(editor, affiliation)+), abstract
99 % of regular expressions is single occurrence
duplicated element names

7. SOA: Single Occurrence Automaton
Proposed Algorithms
Theorem
XSDs with local context and SORE content models are learnable from positive examples only (need ‘sufficiently large’)
iLocal = iSOA + TOSORE + MINIMIZE
infer k-local and single occurrence target XSD Schema
iXSD = iLocal & REDUCE
REDUCE = (unify sufficiently similar types)
SOA: Single Occurrence Automaton

8. Algorithm: iLocal (1/4)

9. Algorithm: iLocal (2/4)

10. Algorithm: iLocal (3/4) iSOA: make SOA from strings
ToSORE: translate SOA → SORE

11. Algorithm: iLocal (4/4)

12. Algorithm: iXSD incomplete data REDUCE: practical heuristics
iLocal derives too many types
REDUCE: practical heuristics
define distance between types
for type s and t
if distance(s, t) < ε then unify s and t

13. Experiments 8 schemas & 200 generated documents for each schema
schema: 12~23 types with unbounded depth and width
local with k = 2, 3
types of iXSD imprecisions:
content model for target and inferred type can differ
based on positive examples, can’t be avoided
type in target XSD can corresponds to multiple types in inferred XSD: false positives
type in inferred XSD can corresponds to multiple types in target XSD: false negatives
type in target XSD is not derived
incomplete corpus, can't be avoided

14. Experiments k = 3, parsing 697 XSDs (40Mb), PentiumM1.73 → 17 seconds
k = 2, without REDUCE → 29 false positive
power of REDUCE
Sensitivity to parameters
context size k ↑
⇒ false positives ↑
⇒ false negatives ↓
ε ↑
⇒ false positives ↓
⇒ false negatives ↑

15. Experiments
iXSD derives good XSDs from small training sets (50~)

16. Conclusions Propose two algorithms Future work
iLocal – sound & k-complete
iXSD – deal with poor data
good performance on real world
good runtime performance
Future work
determine best locality k