Complex Matching of RDF Datatype Properties

Complex Matching of RDF Datatype Properties

Bernardo Pereira Nunes1,2, Alexander Mera1, Marco Antonio Casanova1, Besnik Fetahu2, Luiz André P. Paes Leme3, Stefan Dietze2

1) Department of Informatics-PUC-Rio, 2) L3S Research Center, Leibniz University Hannover,

3) Computer Science Institute, Fluminese Federal University

DEXA 2013 – Prague, Czech Republic

Outline

• Introduction

• Motivation

• Related Work

• Schema Matching Principles

• Our approach:

• Phase 1) Estimated Mutual Information – EMI

• Phase 2) Genetic Programing - GP

• Evaluation

• Results

• Conclusions

Besnik Fetahu 2DEXA 2013 – Prague, Czech Republic

Introduction

• Data Integration

• Combine different data sources into an unified view of data

• Originally fomented by large organizations:

• Merge companies databases due to acquisitions

• Currently, driven by new Web trends such as:

• Improvement of Web-based search

• Proliferation of Web applications

• e-business

• Examples: momondo.de, semantic search, price watchers sites, etc.


Introduction

• Challenges

• Heterogeneous data

• Different data formats

• Data quality (data impurities, corrupted information)

• Scalability

• Adaptability

• Costly


Introduction

• Initiatives to address data integration problems

• Linked Data Principles

• Ontology Alignment Initiatives (OAI)

• Schema Matching tools


Motivation

• Given two schemas S and T a matching from S to T is characterized if an element e from S is mapped to an element e’ from T by some expression that relates both elements.


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Related Work

• Methods

• RiMOM, iMAP, S-Match, DSSim, ATOM, etc.

• Schema-based approach

• Instance-based approach

• Hybrid approach

• Cardinality

• 1:1

• 1:n

• n:m


Rahm, E. and Bernstein, P. 2001. A survey of approaches to automatic schema matching. The VLDB Journal 10, 4 (Dec. 2001), 334-350.

Cardinality

• Simple match

• 1:1 – direct matching

• Complex match

• 1:1 / n:1 (mapping functions)


ISBN

0-671-72287-5

ISBN

0-671-72287-5

Fullname

William Shakespeare

Firstname Last name

William Shakespeare

split(fullname)

concatenate(f,l)

Our approach

• Two-phase approach:

• Estimated Mutual Information

• Suggest 1:1 and 1:n mappings

• Serve as a filtering step (filter out data properties that have no mutual information)

• Reduce search space for the next phase (speed up the process)

• Genetic Programming

• Automatic process for creating mapping functions

• Reduces the cost of traversing the search space


Estimated Mutual Information (EMI)

• EMI Matrix

• p=(p1,…,pu), q=(q1,…,qv) two lists of sets (i.e. sets of data type properties)


Cosine SimilarityJaccard Index…..

Estimated Mutual Information (EMI)

• Computing the mutual information:

• Cosine Similarity

• Simple matches: William Shakespeare → William Shakespeare

• Jaccard Similarity

• Simple and Complex matches: William → William Shakespeare


Genetic Programming (GP)

• Genetic programming refers to an automated method to create and evolve programs to solve a problem.

• A solution is represented by a tree, whose nodes are labeled with functions (concatenate, split, sum) or with values (strings, numbers, etc).

• New individuals are generated by applying genetic operations to the current population of individuals.

• Selects individuals that should breed by an evolutionary process.



• GP Functions:

• Crossover

• The act of swapping gene values between two potential solutions,

simulating the "mating" of the two solutions.

• Mutation

• The act of randomly altering the value of a gene in a potential solution.

• Reproduction

• The act of making a copy of a potential solution



• Fitness function

• Levenshtein similarity function for string values

• KL-divergence measure for numeric values

• Different measures are applied since data properties values can have multiple common values (such as 0) and it can lead to a wrong match. Thus, we use measure the probability of two sets being the same with KL.


An Example of Implementation



Phase 1 – Co-occurrence matrix

1. Difference between Cosine/Jaccard similarity metrics.



Phase 1 – EMI matrix

2. Possible matchings:






Complement

+

NumberAddress

+

Number

Crossover

NeighborhoodComplementNumber

Address

+

+ mutation

Complement

+

Number

reproduction



Correct

Repetitive and Incorrect mutation

Evaluation

• Datasets

• “Personal Information” dataset lists information about people

• “Real Estate” dataset lists information about houses for sale

• “Inventory” dataset describes product inventories

With exception of the “Personal Information” dataset due to privacy reasons, other datasets are available at:

http://pages.cs.wisc.edu/ anhai/wisc-si-archive/domains/


Results


Results


10/04/23Ricardo Kawase 24

Conclusions

• Complex schema matching approach

• Simple + Complex matching:

• Estimated Mutual Information + Genetic Programing

• Reduced search space for matching properties

• Adaptive to variations of 1:1 and n:1 matching instances

• High accuracy on generated matches and coverage

Questions?

Thank you!


Education

Complex Matching of RDF Datatype Properties