Semantic Network Analysis 11.07.05

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Semantic Network Analysis 11.07.05

• Analyzing Semantic Interoperability in
  Bioinformatic Database Networks
• Philippe Cudré-Mauroux, EPFL
• Joint work with
• Julien Gaugaz, Adriana Budura and Karl Aberer

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Overview

• Peer Data Management Systems (PDMS)
• Semantic Interoperability in the Large
• Generatingfunctionologic framework
• The Sequence Retrieval System
• Degree distribution
• Analysis of giant component
• Weighted analysis
• Conclusions

3
Beyond Keyword Search

• searching semantically richer objects in large
  scale heterogeneous networks

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Decentralized Data Integration

• Large Scale Information Systems (e.g., WWW)
• Number of sources gt 100
• Unreliable data
• Autonomy
• Semi-structured data
• E.g., XML/RDF
• No integrity constraints
• No transactions
• Simple SP queries
• E.g., triple patterns, ranking
• Schemata created by end users
• Network churn

• Distributed Databases
• Number of sources lt 100
• Consistent data
• Coordination
• Structured data
• E.g., Relational data model
• Integrity constraints
• Transactions
• Powerful queries
• E.g., SQL, aggregation
• Schemas created by administrators
• Relatively Fixed topology

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Data Integration LAV/GAV

• Traditional database techniques (e.g., LAV/GAV)
  rely on centralized schemas to integrate data
  sources
• Not applicable to our context
• Scale (upper ontologies?)
• Churn
• Autonomy

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Semantic Interoperability
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• ? Extending semantic interoperability techniques
  to decentralized settings

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1. Peer Data Management Systems
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• Pairwise mappings
• Peer Data Management Systems (PDMS)
• Local mappings overcome global heterogeneity
• Iterative query rewriting

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Semantic Mediation Layer
Semantic Mediation Layer
Correlated / Uncorrelated
Overlay Layer
Correlated / Uncorrelated
Physical layer
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Schema-to-Schema Graph

• Inter-organization of the different schemas used
  by the peers
• Logical model
• Directed
• Weighted
• Redundant

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The Semantic Connectivity Graph

• Definition (Semantic Interoperability)
• Two peers are said to be semantically
  interoperable if they can forward queries to each
  other in the Schema-to-Schema graph, potentially
  through series of semantic translation links
• Idea
• As for physical network analyses, create a
  connectivity layer to account for semantic
  interoperability
• The semantic connectivity Graph S
• Unweighted, irreflexive and non-redundant version
  of the Schema-to-Schema graph

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Observations

• Theorem
• Peers in a set Ps are semantically
  interoperable iff Ss is strongly connected, with
  Ss ? s ?p ? Ps, p?s
• Observation 1
• A set of peers Ps cannot be semantically
  interoperable if
• Es lt Vs
• Observation 2
• A set of peers Ps is semantically
  interoperable if
• Es gt Vs (Vs-1) - (Vs-1)

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2. Semantic Interoperability in the Large

• Question
• How can we analyze semantic interoperability in
  large-scale PDMS?
• Idea use percolation theory to detect the
  emergence of a strongly connected component in S
• Necessary condition for vertex-strong
  connectivity
• Necessary condition for semantic interoperability

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The Model

• Adaptation of a recent graph-theoretic framework
• Newman, Strogatz, Watts 2001
• Large-scale semantic graphs as random graphs with
  arbitrary degree distribution
• Exponentially distributed, small-world,
  scale-free graphs
• Specificities of our model
• Strong clustering (clustering coefficient cc)
• Bidirectionality (bidirectionality coefficient
  bc) (for directed networks)
• Based on generatingfunctionology
• Percolation ci gt 0

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Size of the giant component

• With u the smallest non-negative solution of
• And G1 the distribution of edges from first to
  second-order neighbors

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3. The Sequence Retrieval System (SRS)

• Commercial information indexing and retrieval
  system
• Bioinformatic libraries
• EMBL
• SwissProt
• Prosite
• Etc.
• Schemas described in a custom language (Icarus)
• Mappings (links) from one database to others

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Why is SRS interesting?

• Applying our heuristics on a real large-scale
  corpus of interconnected databases
• More than 380 databanks
• More than 500 (undirected) links
• Data used by professionals on a daily basis

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Crawling the SRS schema-to-schema graph

• Custom crawler
• As of May 2005
• (EBI repository)
• 388 nodes
• 518 edges
• Giant connected component 187 nodes
• Power-law distribution of node degrees
• Clustering coefficient 0.32
• Diameter 9

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Results

• Connectivity indicator ci 25.4
• Super-critical state
• Size of the giant component
• 0.47 (derived)
• 0.48 (observed)

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Graphs with same power-law degree distr.

• Varying number of edges

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10x Bigger Graph
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Analyzing weighted networks

• Do we have a sufficient number of good mappings?
• Introducing quality measures from the mappings
• Weights
• Attribute / schema level
• Cf. Chatty Web (WWW03)
• Semantic query forwarding
• Per-hop forwarding behaviors
• Only forward if wi gt ?
• ? 0 flooding
• ? 1 exact answers

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Weighted Results

• Same degree distribution (388 nodes)
• Uniformly distributed weights between 0 and 1

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4. Conclusions

• Analyzing a real network of bioinformatic
  databases
• Accurate results (even for relatively small
  networks)
• Weighted / unweighted
• Current works
• Compositions of weights along a path
• Semantic random walkers
• Public domain simulator
• Future works
• Analyzing other forwarding behaviors
• Implementation in a real PDMS (self-organizing
  mappings)
• GridVine

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References
A Necessary Condition for Semantic
Interoperability in the Large Philippe
Cudré-Mauroux and Karl Aberer ODBASE
2004 GridVine Building Internet-Scale Semantic
Overlay Networks Karl Aberer, Philippe
Cudré-Mauroux and Tim van Pelt ISWC
2004 Semantic Overlay Networks (Tutorial) Karl
Aberer and Philippe Cudré-Mauroux VLDB 2005
complete reference list at http//lsirpeople.epf
l.ch/pcudre/
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Thank you for your attention

• Questions ?