OntologyBased Semantic Integration Method for Domain Specific Scientific Data

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Ontology-Based Semantic Integration Method for
Domain Specific Scientific Data

• AUTHORS
• Hu Changjun
• Zhang Xiaoming
• Zhao Qian
• Zhao Chongchong
• Presented By Mayank Gupta

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Outline

• Introduction
• Heterogeneity
• Need for Ontology based solution
• Ontology based solution
• Ontology based semantic integration method
• Semantic integration method steps
• Semantic integration method architecture
• Future work
• References

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Introduction

• What is the motive of this paper?
• Sharing huge amount of domain specific data
  which is of great significance for researches.
• What is the problem in achieving such motive?
• Semantic heterogeneity between various sources
  of data in specific area of science (research
  area).
• What solution does author proposes?
• Author proposes an Ontology based solution which
  uses TBox reasoning (i.e., reasoning about the
  concepts in an ontology) for resolving this
  semantic heterogeneity.

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Heterogeneity

• What do you mean by heterogeneity?
• Data heterogeneity can be divide into following
  levels
• System
• Syntactic
• Structural
• Semantic

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Need for Ontology based solution

• Domain specific markup languages such as MatML,
  CML and MathML are used to exchange data among
  different sources in specific domain.
• They do solve syntactic and structural problems
  and to little extend semantic problem too.
• Still data lacks high level abstraction of
  concept semantics.

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Ontology based solution

• Why only Ontology based solution?
• An ontology is formal, explicit specification of
  a shared conceptualization.
• It can be used to capture shared domain
  knowledge.
• It also serves a lot for logic reasoning of
  information content in a specific domain (TBox
  reasoning).

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Ontology based solution

• What are disadvantages of non-ontology based
  solution to ontology based solution?
• Non-ontology solution has no means of checking
  the consistence and discovering domain
  terminology conflicts.
• It cant implement inheritance mechanism.
• Implicit knowledge cant be discovered since no
  reasoning mechanism supported.

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Ontology based solution

• In ontology based solution
• We use OWL ontology to represent global semantics
  of domain and local semantics of heterogeneous
  sources .
• A mapping ontology for mapping between global and
  local ontology.
• Semantic heterogeneity is resolved using TBox
  reasoning and there is no need to migrate data
  from sources to ontology instances.
• Domain specific markup languages are used as
  uniform format for query results for data
  exchange.

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Ontology based semantic integration method

• Ontology is defined as a 4-tuple
• OltC, R, I, Aogt
• Here
• C is a finite set of concepts
• R is a finite set of relations
• I is a set of instances
• Ao is a set of axioms, which is expressed in an
  appropriate logical language. Given SC?R?I, Ao
  is logic axiom set over S

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Ontology based semantic integration method

• Semantic data integration system can be defined
  as 5- tuple SIltG, S, D, MGS, MSDgt
• Here
• G is global conceptual schema . It gives global
  view for users.
• S is local conceptual schema. It gives local
  semantics for data source.
• D is the data source.
• MGS is the mapping between G and S.
• MSD is the mapping between S and D.

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Ontology based semantic integration method steps

• Step 1
• First deeply analyze the data requirements
  together with the domain experts, and construct
  the global ontology OG to provide the formal and
  explicit shared knowledge in this domain. OG is
  considered as global conceptual schema of SI,
  i.e. G OG and AG SOG .
• Step 2
• Assuming there are p data sources expressed as
  D1, D2, , Dp, we construct local ontology OSi
  for each Di, where i?0, p. OS is viewed as
  local conceptual schema, namely S OS and AS
  SOS .

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Ontology based semantic integration method steps
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Ontology based semantic integration method steps

• Step 3
• Build MSD ?(D, OS ) is axiom set defined over
  SD?SOS. In this step, we should build a set of
  mapping axioms f between each Di and OSi, where
  f??i and i?0, p.
• Step 4
• Build MGS?(OS ,OG ) is axiom set defined over
  SOS?SOG. In this step we should build a set of
  mapping axioms ? between each OSi and OG, where
  ???i and i?0, p. A mapping ontology OM is built
  to express ? in ontology language.

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Ontology based semantic integration method steps

• Case study in material science domain

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Ontology based semantic integration method steps

• Step 5
• Data user submits a qG over G
• MGS and the imported ontology are loaded into DL
  (Description Logic) reasoner.
• By TBox reasoning, global concepts and their
  subconcpets in qG will be converted into
  corresponding local concepts in local ontology
• Then qG will be decomposed into subqueries qS1,
  qS2,, qSn, where n?0, p.
• Both qG and its subqueries are expressed as
  SPARQL.

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Ontology based semantic integration method steps

• Step 6
• Using mappings in MSD, the local concepts in qSj
  can be translated into symbols from SDj, where
  j?0, n
• qSj will be rewritten into native query qDj.
• After accessing the data from data sources, the
  query results aD1, aD2,, aDn will be returned in
  a uniform way.
• In this method, domain markup language is used to
  express the query results

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Ontology based semantic integration method steps

• Step 7
• aD1, aD2, , aDn are composed as final result
  aG, which will be returned back to data user.

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Ontology based semantic integration method steps

• How ontology helped in this above process?
• It provides formal description for specific
  domain. Domain concepts and knowledge structure
  are formally defined by OG.
• It provides semantic extension for data sources.
  Data sources are enhanced semantically by OS
  which can make inner structure of data sources
  explicit.
• It provides semantic interoperability. OM,
  considered as foundation for query reformulation,
  contains mappings between global semantic and
  local semantic.
• It provides reasoning and deducing ability via
  TBox reasoning.

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Ontology based semantic integration software
architecture
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Future work

• Still this system relies on global ontology which
  as discussed in earlier papers is hard come up
  with as it constraints the distributed resources.
• Need to automate the process so that local
  ontology can learn from data source.
• Query optimization.

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References

• http//en.wikipedia.org/wiki/SPARQL
• http//en.wikipedia.org/wiki/Axiom
• http//en.wikipedia.org/wiki/TBox
• http//ieeexplore.ieee.org/Xplore/login.jsp?url/i
  el5/4287452/4287802/04287953.pdftparnumber4287
  953isnumber4287802

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Questions ????