combining semantic and multimedia query routing techniques for unified data retrieval in a pdms*

Combining Semantic and Multimedia Combining Semantic and Multimedia Query Routing Techniques for Unified Query Routing Techniques for Unified

Data Retrieval in a PDMS*Data Retrieval in a PDMS*

Claudio GennaroClaudio Gennaro11, Federica Mandreoli, Federica Mandreoli2,42,4, Riccardo Martoglia, Riccardo Martoglia22,,

Matteo MordacchiniMatteo Mordacchini11, Wilma Penzo, Wilma Penzo3,43,4, and , and Simona SassatelliSimona Sassatelli22

11 ISTI – PI/CNR, Pisa ISTI – PI/CNR, Pisa22 DII - Università degli Studi di Modena e Reggio Emilia DII - Università degli Studi di Modena e Reggio Emilia

3 3 DEIS - Università degli Studi di BolognaDEIS - Università degli Studi di Bologna44 IEIIT – BO/CNR, Bologna IEIIT – BO/CNR, Bologna

Claudio GennaroClaudio Gennaro11, Federica Mandreoli, Federica Mandreoli2,42,4, Riccardo Martoglia, Riccardo Martoglia22,,

Matteo MordacchiniMatteo Mordacchini11, Wilma Penzo, Wilma Penzo3,43,4, and , and Simona SassatelliSimona Sassatelli22

11 ISTI – PI/CNR, Pisa ISTI – PI/CNR, Pisa22 DII - Università degli Studi di Modena e Reggio Emilia DII - Università degli Studi di Modena e Reggio Emilia

3 3 DEIS - Università degli Studi di BolognaDEIS - Università degli Studi di Bologna44 IEIIT – BO/CNR, Bologna IEIIT – BO/CNR, Bologna

* This work is partially supported by the Italian co-founded Project NeP4B* This work is partially supported by the Italian co-founded Project NeP4B* This work is partially supported by the Italian co-founded Project NeP4B* This work is partially supported by the Italian co-founded Project NeP4B

ISDSI 2009 – June 25th, 2009 ISDSI 2009 – June 25th, 2009 ISDSI 2009 – June 25th, 2009 ISDSI 2009 – June 25th, 2009

Motivation ICTs over the Web have become a strategic asset Internet-based global market place where automatic cooperation

and competition are allowed and enhanced

The aim: development of an advanced technological infrastructure for SMEs to allow them to search for partners, exchange data and negotiate without limitations and constraints

The architecture: inspired by Peer Data Management Systems (PDMSs)

NeP4B Project(Networked Peers for Business)

ISDSI’09 - C. Gennaro, F. Mandreoli, R. Martoglia, M. Mordacchini, W. Penzo, S. Sassatell - Combining Semantic and Multimedia Query Routing Techniques for Unified Data Retrieval in a PDMS

Peer1

Peer2

Peer3

Peer5

Peer4

Peer6

TXT MM

TXT MM

TXT MM

TXT MM

TXT MM

TXT MM

The Reference Scenario A peer: a single SME or a mediator

It is queried by exploiting a SPARQL-like query language similarity predicates (FILTER function LIKE )

It keeps its data in an OWL ontology Multimedia objects multimedia

attributes in the ontology (e.g. image)

Tile

pricecompany

Image

imagePeer1

domdom dom ran

size

dom

materialdom

origindom


SELECT ?companyWHERE { ?tile a Tile .

?tile company ?company . ?tile price ?price . ?tile origin ?origin . ?tile image ?image . FILTER ( (?price < 30) && (?origin = ‘Italy’) &&

LIKE (?image, ‘myimage.jpg’, 0.3))}LIMIT 100

Peer1

Peer2

Peer3

Peer5

Peer4

Peer6

TXT MM

TXT MM

TXT MM

TXT MM

TXT MM

TXT MM

The Reference Scenario (cont.) Peers are connected by means of semantic

mapping (with scores) Peers collaborate in solving users queries

How to effectively and efficiently answer a query?

By adopting effective and efficient query routing techniques Flooding is not adequate

Overloads the network (traffic & computational effort) Overwelms the querying peer (irrelevant results)

Q

Q’

Q’’

Queries are formulated on the peer’s ontology

Answers can come from any peer that is connected through a semantic path of mappings


Combining Semantic and Multimedia Query Routing Techniques We leverage our distinct experiences on semantic [Mandreoli et al.

WIDM 2006, Mandreoli et al. WISE 2007] and multimedia [Gennaro et al. DBISP2P 2007, Gennaro et al. SAC 2008] query routing

We propose to combine our approaches in order to design an innovative mechanism for a unified data retrieval

We pursue: Effectiveness by selecting the semantically best suited subnetworks Efficiency by promoting the network’s zones where potentially

matching objects are more likely to be found, while pruning the others


Two main aspects characterize our scenario: the semantic heterogeneity of the peers’ ontologies the execution of multimedia predicates

Outline

Motivation

Query Answering Semantics

Query Routing Semantic Query Routing Multimedia Query Routing Combined Query Routing

Routing Strategies

Experimental Evaluation

Conclusions and Future Works


Query Answering Semantics Peer pi has an ontology Oi ={Ci1 , … , Cim}

A semantic mapping: a fuzzy relation M(Oi,Oj) OiOj where each instance (C,C’) has a membership grade μ(C,C’) [0,1]

evaluation of f on a local data instance i: s(f,i) [0,1] s(f,i) = s( f(φ1, …, φn), i ) = sfunφ ( s(φ1,i), … , s(φn,i) )

Boolean semantics for relational predicates non-Boolean semantics for similarity predicates t-norm ( resp. t-conorm) for scoring conjunctions (resp. disjunctions)

Local query answers: Ans(f,pi) = { (i,s(f,i)) | s(f,i)>0 }

Local query execution

Query formula:f ::= <triple_pattern> <filter_pattern>

<triple_pattern> ::= triple | <triple_pattern> ∧ <triple_pattern>

<filter_pattern> ::= φ | <filter_pattern> <filter_pattern> |∧ <filter_pattern> ∨ <filter_pattern> |

(<filter_pattern>) φ is a relational (=,<,>, <=, >=) or similarity (~t) predicate

pi

Oi

f


Ans(f,pi)

Query Answering Semantics (cont.)

f undergoes a chain of reformulations ff1…fm

s(f, Pp…pm ) = sfunr (s(f, p1), s(f1, p2), … , s(fm-1, pm))

sfunr is a t-norm

One-step query reformulation (pipj) according to the mapping M(Oi,Oj)

s(f,pj) = sfunc ( μ(C1 ,C’1), … , μ(Cn ,C’n) )

sfunc is a t-norm

Query answering semantics f submitted to p

P’ = {p1,…,pm} : set of accessed peers

Pp…pi : path used to reformulate f over each pi in P’

Ans (f, {p} U P’) = Ans(f,p) U Ans(f, Pp…p1 ) U … U Ans(f, Pp1…pm )

where : Ans(f, Pp…pi ) = (Ans(f,pi), s(f, Pp…pi ))

Multi-step query reformulation (pp1…pm = Pp1…pm )

pi pj

M(Oi,Oj)

Oi Oj

f


p p1 pm…

ff1

f2fm

(Ans(f,pj), s(f,pj))

(Ans(f,pm), s(f,Pp…pm))