Semantic Network Analysis 11.07.05

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The National Centres of Competence in Research are managed by the Swiss National Science Foundation on behalf of the Federal Authorities

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

1. Peer Data Management Systems (PDMS)2. Semantic Interoperability in the Large

• Generatingfunctionologic framework

3. The Sequence Retrieval System• Degree distribution• Analysis of giant component• Weighted analysis

4. Conclusions

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Beyond Keyword Search

searching semantically richer objects in large scale heterogeneous networks

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<myRDF:Date> Jan 1, 2005 </myRDF:Date>

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Decentralized Data Integration

• Large Scale Information Systems (e.g., WWW)– Number of sources > 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 < 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

VS

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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

• How can we foster semantic interoperability in decentralized settings?

Date

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Semantic Interoperability

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Photoshop(own schema)

<WinFSImage> <GUID>178A8CD8866</GUID> <Author> <DisplayName> Henry Peach Robinson <DisplayName> <Role>Photographer</Role> <Author> <Keyword> Tunbridge </Keyword> <Keyword>Council</Keyword> …</WinFSImage>

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Extending semantic interoperability techniques to decentralized settings

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1. Peer Data Management Systems

• Pairwise mappings– Peer Data Management Systems (PDMS)

• Local mappings overcome global heterogeneity– Iterative query rewriting

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Semantic Mediation Layer

Correlated / Uncorrelated

Correlated / Uncorrelated

“Physical”layer

Overlay Layer

SemanticMediation 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, ps}

• Observation 1 A set of peers Ps cannot be semantically interoperable if

|Es| < |Vs|

• Observation 2 A set of peers Ps is semantically interoperable if

|Es| > |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 > 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 >= = 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 LargePhilippe Cudré-Mauroux and Karl AbererODBASE 2004

GridVine: Building Internet-Scale Semantic Overlay NetworksKarl Aberer, Philippe Cudré-Mauroux and Tim van PeltISWC 2004

Semantic Overlay Networks (Tutorial)Karl Aberer and Philippe Cudré-MaurouxVLDB 2005

… complete reference list athttp://lsirpeople.epfl.ch/pcudre/

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Thank you for your attention

Questions ?

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Semantic Network Analysis 11.07.05