Schema Mapping: Experiences and Lessons Learned

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Schema Mapping Experiences and Lessons Learned

• Yihong Ding
• Data Extraction Group
• Brigham Young University
• Sponsored by NSF

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Schema Mapping

• Semantic correspondence between two schemas
• Significance
• data integration
• data warehouses
• ontology merging
• message translation in e-commerce
• semantic query processing
• etc.

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Schema Representation
Phone_evening
MLS
MLS
Bedrooms
Basic_features
location
House
Agent
beds
SQFT
Name
location_ description
agent
Phone_day
Golf course
Water front
Location
Address
name
cell phone
Street
City
State
home phone
office phone
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11 Mapping Cardinality
Phone_evening
MLS
MLS
Bedrooms
Basic_features
location
House
Agent
beds
SQFT
Name
location_ description
agent
Phone_day
Golf course
Water front
Location
Address
name
cell phone
Street
City
State
home phone
office phone
5
n1 Mapping Cardinality
Phone_evening
MLS
MLS
Bedrooms
Basic_features
location
House
Agent
beds
SQFT
Name
location_ description
agent
Phone_day
Golf course
Water front
Location
Address
name
cell phone
Street
City
State
home phone
office phone
6
nm Mapping Cardinality
Phone_evening
MLS
MLS
Bedrooms
Basic_features
location
House
Agent
beds
SQFT
Name
location_ description
agent
Phone_day
Golf course
Water front
Location
Address
name
cell phone
Street
City
State
home phone
office phone
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Object-Set Matcher (schema-level)

• Name-based matcher
• string and substring comparison
• linguistic methods stemming, stop words,
  removing ignorable characters, etc.
• thesaurus WordNet, etc.
• 11 mapping cardinality

Agent
agent
Name
name
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Object-Set Matcher (instance-level)

• Data Frame
• multiple regular expressions in Perl style
• as simple as a list of data values
• Data-frame matcher
• use compare recognized data values
• benefit able to recognize disjunctive data value
  sets
• bias data frame may not correspond 100 with the
  semantics
• limitation a needed data frame might not exist
• 11 mapping cardinality

Car Model Ford, Honda, Chevy, Toyota
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Extended Data-Frame Matcher (instance-level)

• n1 mapping cardinality
• Add a STRICT_SUBSTRING operation
• With the help of structural analysis

Schema 1
location
Schema 2
Address
Street
City
State
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Direct Structure Matcher

• Comparing structure similarity between two
  candidate schemas
• 11 mapping cardinality

Name
agent
Agent
Fax
Location
name
phone_day
fax
phone
Address
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Reference Structure Matcher

• If A and B match C, then A matches B.
• Able to solve nm mapping cardinality
• 11, n1, and nm mapping cardinalities

Phone
Day Phone
Cell Phone
Evening Phone
Office Phone
Home Phone
Schema 2
Schema 1
Home Phone
Evening Phone
Cell Phone
Day Phone
Office Phone
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Experiments
Application (Number of Schemes) Precision () Recall () F () Number Matches Number Correct Number Incorrect
Faculty Member (5) 100 100 100 540 540 0
Course Schedule (5) 99 93 96 490 454 6
Real Estate (5) 90 94 92 876 820 92
Indirect Matches (precision 87, recall 94,
F-measure 90)
Data borrowed from Univ. of Washington DDH,
SIGMOD01

• Rough Comparison with U of W Results
• Faculty Member Accuracy, 92
• Course Schedule Accuracy 71
• Real Estate (2 tests) Accuracy 75

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Lessons Learned

• n1 and nm matches occur frequently.
• 22 97/437 DMD03 (Course Catalog, Company
  Profile)
• 45 287/638 (Car Ads, Cell Phones, Real Estate)
• Reference structures provides a way to solve the
  long-lasting hard cluster mapping (nm
  cardinality) problem.
• Data frames improve the instance-level matchers.
• The combination of schema-level and
  instance-level matchers improve the results.