MatchIT 1.1: Data Integration with Semantic Mapping Technologies

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MatchIT 1.1 Data Integration with Semantic
Mapping Technologies

• Michael Schidlowsky
• Sr. Software Architect

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Data Integration

• Motivated by
• Organizational Changes
• Mergers and Acquisitions
• Internal reorganizations (e.g., DHS)
• Data Mining
• Standards Conformance
• Migration Efforts
• Legacy Systems
• Decouple data sources from application code

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Data Integration

• Challenges for integration specialist include
• Domain-specific terms
• Unfamiliarity with source schemas
• Large size of schema set
• Semantics often not captured
• Captured semantics
• Stored in ad-hoc formats
• Cannot be reused to facilitate future data
  integration efforts

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Data Integration Example

• Background
• Acme Inc., merges with CompuGlobalHyperMeganet.
• Technical Challenge
• Need Virtual Database of all sales for all
  stores in real-time.

• Which fields represent customers?
• CUSTOMERID
• CUST_ID
• SSN
• Which fields represent Price?
• Sale_Amt
• Total_Sale
• What if your database has 10,000 columns?

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Data Integration Example

• Background
• HR needs to use employee information for new
  company portal.
• Technical Challenge
• Data must be in XML and conform to standard HR
  schema.

• Find all fields related to Address?
• RESIDENCE
• PREV_RESIDENCE
• What if your database has 10,000 columns?

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Ideal Matching Solution

• Finds lexical relationships
• Captures semantic information
• Finds semantic relationships
• Provides programmatic access to results (API)
• Fast
• Scalable
• Human Involvement

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MatchIT Philosophy

• Best Matching tool already exists!
• What is meant by ID?

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MatchIT Philosophy

• Best Matching tool already exists!
• What is meant by ID?
• PLEASE PRESENT ID

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MatchIT Philosophy

• Best Matching tool already exists!
• What is meant by ID?
• PLEASE PRESENT ID
• NY, NJ, ID

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MatchIT Philosophy

• Best Matching tool already exists!
• What is meant by ID?
• PLEASE PRESENT ID
• NY, NJ, ID
• SUPEREGO, EGO, ID

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MatchIT 1.1

• - MatchIT is a semantic and lexical matching
  tool.
• Session Outline
• Import and process schemas
• Perform lexical matching
• Create and manage a semantic vocabulary
• Perform semantic matching
• Demonstrate 3rd Party integration with Data
  Integration tool (MetaMatrix)

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Import Process Schemas

• Revelytix Models are RDF/OWL
• Flexible model architecture
• Extensible
• Interoperable
• Current Importers
• JDBC
• XML Schema
• MetaMatrix XMI Models

Importer Demo
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Lexical Matching

• Uses lexical distance measures to determine
  lexical similarity.
• Fastest matching technique
• Requires no work other than importing schemas
• Often yields interesting results

Lexical Matching Demo
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Create Vocabulary from Schemas

• A Vocabulary is
• A set of symbols
• Occurrences of those symbols in your schemas
• Binding of each symbol to one or more semantic
  concepts
• Created by MatchIT from schemas using
  tokenization algorithms.
• Reusable

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Tokenization Algorithms

• Different schemas require different tokenization
  techniques.
• Tokenization algorithms determine how symbols are
  extracted from schemas
• Capitalization
• Delimiters
• English Language

Vocabulary Demo
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Matching Techniques

• MatchIT currently uses two types of matching
  techniques
• Lexical Matching
• Attempts to determine similarity based on the
  lexical distance between them.
• Semantic Matching
• Attempts to determine similarity based on the
  ontological distance between them within a
  semantic knowledge base.

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Parts Supplier Schema(as seen by a person)
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Parts Supplier Schema (as seen by a computer)
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Semantic Matching

• How semantically similar are two concepts?

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Semantic Matching

• Uses knowledge base distance measures to
  determine semantic similarity.
• Presents ranked candidate matches
• Based on semantics captured in Vocabularies
• The only way to effectively find relationships
  between lexically dissimilar symbols
• GenderCode SexCode
• Provider Supplier
• Amount Quantity

Semantic Matching Demo
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3rd Party Integration

• MatchIT Integration
• MatchIT Java API
• Stand-alone application
• Embeddable application (as Eclipse plug-ins).
• Hides unapproved matches
• Useful for various 3rd Party applications
• Data Integration
• Data Discovery
• Ontology Mediation
• Search
• Metadata Management
• Data Cleansing

MetaMatrix Demo
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Questions?

• MatchIT 30-day trial available at
  http//www.revelytix.com
• Michael Schidlowsky
• michaels_at_revelytix.com