Rousset EID06

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Building scalable semantic PDMS: the SomeWhere approach. : 

Building scalable semantic PDMS: the SomeWhere approach. Marie-Christine Rousset Joint work with Philippe Adjiman, Philippe Chatalic, François Goasdoué, Gia-Hien Nguyen, Laurent Simon

How to make semantic approaches scalable to the web ?: 

How to make semantic approaches scalable to the web ? A data centered vision of the Semantic Web viewed as a huge semantic and distributed data management system SomeWhere a peer to peer infrastructure based on simple personalized ontologies and mappings distributed at large scale Focus of this talk

P2P Data Management Systems: 

P2P Data Management Systems Logical network of peers (≠ physical network) each peer is characterized by its physical address (IP) a description of the stored resources its neighbors in the network the peers to which it can transmit messages (queries,...) Various topologies random and dynamic (Gnutella) fixed (Chord, Hypercube) guided by the semantics SON, Edutella, Piazza, DRAGO, coDB, Somewhere

SomeWhere logical networks: 

SomeWhere logical networks The topology is not fixed Guided by mappings A peer joins by declaring mappings between its ontology and the ontologies of some peers that it knows leaves by removing the mappings with its acquaintances in the network

SomeWhere in a nutshell: 

SomeWhere in a nutshell Simple data model based on a propositional language of classes for defining ontologies, mappings, and queries a sublanguage of OWL DL (W3C) Scales up to one thousand peers logical network : « small world »

SomeWhere Data Model: 

SomeWhere Data Model Data Data Schema+Data

SomeWhere Data Model : 

SomeWhere Data Model


Semantics Standard FO logical semantics one single domain of interpretation a distributed set of formulas interpreted in the same way as if they were not distributed in contrast with some other approaches coDB: epistemic logic DRAGO: distributed semantics of DDL or DFOL based on a collection of domains of interpretations Our assumption: the objects have a unique URI objects stored at different peers and having the same URI are interpreted as being the same

Data model: example : 

Data model: example Musique Rock Pop Classique Français US St_pop Tchaikovsky St_Français St_US St_Tchaikovsky Mouv Rock P1 P2

Query answering : illustration: 

Query answering : illustration Musique Rock Pop Classique Français US St_pop Tchaikovsky St_Français St_US St_Tchaikovsky Music Pop_Rock Classical St_Pop_Rock St_Ru Tchai St_Tchai Ru It St_Pop_Rock Rewritings St_US St_Français St_Mouv St_Pop St_Pop St_Français St_Mouv St_Pop St_US St_Pop P1 P2

Query answering in SomeWhere: 

Query answering in SomeWhere Decomposition of queries/recombination of answers only atomic queries are transmitted to peers a complex query is splitted into atomic queries each solicited peer processes a given atomic query q and incrementally sends back intentional answers for it (conjunction of) extensional classes that are rewritings of q intentional answers of different atomic queries resulting from the split of a complex query must be recombined intentional answers can combine extensional classes of different peers Can be reduced to a consequence finding problem in distributed clausal propositional theories Ontologies and mappings are encoded as clauses The maximal conjunctive rewritings of a conjunctive query Q correspond exactly to the negation of the clauses that are proper prime implicates of the negation of Q w.r.t the union of the local theories and the mappings

Query answering algorithm: 

Query answering algorithm Message based local algorithm running on each peer query, answer, and termination messages Global properties soundness completeness termination (even for cyclic networks)


extension Flash demo of the SomeWhere


BenStiller Comedy Friends Humor Classes extensions


Friends Humor Action Suspense Thriller BenStiller Comedy


BenStiller Comedy Friends Humor Action Suspense Thriller


P3:Thriller P1:Action P1:Suspence P5:Drama P6:DramaComedy P2:BruceWillis P1:Suspense Rewritings of Thriller: evaluation Local Integration

SomeWhere infrastructure: 

SomeWhere infrastructure

SomeWhere infrastructure: 

SomeWhere infrastructure Zoom on one machine 100 % JAVA 1.5 somewhere.jar ~ 250 Ko

Scalability experiments [IJCAI 05]: 

Scalability experiments [IJCAI 05] on randomly generated networks 1000 peers deployed on a cluster of 75 machines small world topology Close to the topology of the web peers ontologies random clauses of length 2 mappings random clauses of length 2 or 3


Varying topologies P: probability of redirecting an edge Model of Watts and Strogatz

Scalability results: 

Scalability results Varying parameters Number of mappings between peers complexity of mappings ratio of clauses of length 3 (0%, 20%, 100%) timeout : 30 s/query Depth of query processing Small depth (less than 7) even on the hard cases Time to produce a number of answers In 90% cases, the first answer is produced within 2 seconds Easy cases (simple mappings): few answers per query (5 on average) very fast (less than 0.1s) to compute all the answers without timeouts Hard cases (complex and more mappings per edge) around 1000 answers per query (but > 30% queries not complete : timeouts) quite fast to obtain them (less than 20s)

Ongoing work (1): 

Ongoing work (1) Extending the data model to RDF(S) W3C recommendation for describing web resources Classes and (binary) relations between objects each object is identified by a URI Triple notation: <resource, property, value> Relational notation: property(resource, value)



SomeRDFS: data model : 

SomeRDFS: data model a simple fragment of RDFS distributed through simple mappings (using the same constructors)

Query rewriting: 

Query rewriting Propositionalisation of RDFS statements Query rewriting using SomeWhere C1dom  C2dom C1range  C2range P1rel  P2rel Prel  Cdom Prel  Crange


illustration Q(X,Y): P2.Work(X)P2.refersTo(X,Y)


illustration Q(X,Y): P2.Work(X)P2.refersTo(X,Y)

Ongoing work (2): 

Ongoing work (2) Handling inconsistencies how to define them ? insatisfiability (no model) => inconsistency not a necessary condition how to check consistency? at each join of a new peer how to deal with inconsistency? correct it or reason with it ? for each A, there exists a model in which A is non empty: S | A


illustration path m1: AIPubli is a subclass of Conf. inconsistencies are caused by mappings. Article Theory Expe P2 path m0 -> m2: AIPublic is a subclass of Journal. Conf and Journal are disjoint, therefore AIPUbli is necessarily empty P1

P2P detecting of inconsistencies: 

P2P detecting of inconsistencies Propagation of m1: { ¬AIPubli v Conf; ¬AIPubli v Publi; ¬AIPubli v ¬Journal; ¬BDPubli v Conf; ¬BDPubli v Publi; ¬BDPubli v ¬Journal }. No production of unit clause No inconsistency Propagation of m2: { ¬Theory v Journal; ¬AIPubli v Journal; …..; ¬AIPubli ; …; ¬AIPubli v ¬Conf}. Production of a unit clause Inconsistency {m1,m2} is a NoGood stored at P3 ¬Conf v Publi ¬Journal v Publi ¬Journal v ¬Conf ¬AIPubli v 2005 ¬BDPubli v 2005 ¬Theory v Article ¬Expe v Article ¬AIPubli v Theory

Distributed storage of the NoGoods: 

Distributed storage of the NoGoods


Principle: avoid the inconsistencies when constructing answers Semantics of « well-founded » answer: obtained from a consistent subset of formulas Algorithm: for each answer, build its set of mapping supports and return the set of NoGoods encountered during the reasoning, discard the mapping supports including a NoGood return the answers having a not empty set of mapping supports P2P well-founded reasoning It will be presented in more details at ECAI 06


Perspectives Coupling SomeWhere to a DHT for optimizing lookup queries Adapting the SomeWhere algorithm to support the epistemic semantics Modeling and handling trust in P2P Semantic overlay networks based on a logical approach P2P discovery and composition of smart devices based on a semantic description of the functionality, inputs and outputs of devices

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