George Anadiotis, Spyros Kotoulas and Ronny Siebes VU University Amsterdam.

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George Anadiotis, Spyros Kotoulas and Ronny SiebesVU University Amsterdam

Why do we need distribution… Why do we need anytime behavior… Why is should be (very) scalable… Why should we drop consistency and

completeness… Why do we need trust/ontology ranking… etc

What is P2P? (1 slide) Relationship between P2P and SW(3 slides) Our Goal (1 slide) Distributed SW stores(1 slide)

◦ Structured P2P stores (3 slides)◦ Federated stores (2 slides)

Our approach (6 slides) Future work (1 slide)

Class of distributed systems Most important characteristics

◦ Same functionality across peers◦ Peer autonomy◦ Formation of overlay networks◦ Common interface◦ They respect some agreed-upon way to organize

File-sharing networks are NOT the only Peer-to-Peer systems.

Source of semantic information to self-organize

Interoperability

Scalable infrastructure for◦ Storage◦ Reasoning◦ Collaboration

Self-organization Autonomy – control of data Privacy Scalable algorithms Robustness No censorship No preferential treatment of information

Common misconception:All Peer-to-Peer systems can offer the above

Global-scale semantic web storage and reasoning◦ Scalability

Computation Administration

Structured peer-to-peer◦ Use DHTs ◦ One global distributed store◦ Peers do not maintain their own data

Federated stores◦ Each peer maintains its own store◦ Stores are interconnected◦ Either global schema or mappings between

schemata

• The mathematical abstraction for hashtables is a Map

• Functionality:• put(key,value)• get(key)

• Similar to normal hash-tables with the difference that each bucket now is a peer

• Accessing different buckets involves network traffic

• Routing to a bucket is done bothering approx. log(N) peers, N is network size

Values are stored in the peer with ID starting with the first letter of the key

a dcb e f

g jih k l

m pon q r

s uvt w x

<Key=horse, Value=the horse is an animal>

<rabbit, subClassOf, animal><seal, subClassOf, animal><animal, lives_in, habitat>

<monk_seal, subClassOf, seal><mseal1, type, monk_seal>

Peer 1 Peer 2

a dcb e f

g jih k l

m pon q r

s uvt w x

<rabbit, subClassOf, animal>

<animal, lives_in, habitat>

<monk_seal, subClassOf, seal>

<mseal1, type, monk_seal>

<seal, subClassOf, animal><monk_seal, subClassOf,

seal><rabbit, subClassOf, animal>

a dcb e f

g jih k l

m pon q r

s uvt w x

RDFS class axioms

(1) <X, subClassOf, Z> <- <X, subClassOf, Y> , <Y, subClassOf, Z>

(2) <X, type, Z> <- <X, type, Y>, <Y, subClassOf, Z>

<monk_seal, subClassOf, animal>

a dcb e f

g jih k l

m pon q r

s uvt w x

RDFS class axioms

(1) FORALL O,V O[rdfs:subClassOf->V] <- EXISTS W (O[rdfs:subClassOf->W] AND W[rdfs:subClassOf->V]).

(2) FORALL O,T O[rdf:type->T] <- EXISTS S (S[rdfs:subClassOf->T] AND O[rdf:type->S]).

<monk_seal, subClassOf, animal>

<mseal1, type, animal><mseal1, type, animal><mseal1, type, animal>

As shown, the transitive closure has to be calculated – backwards chaining would require many DHT messages

But it does not scale to large number of ontologies.◦ E.g. a animal hierarchy:

Adding the triple <animal, subClassOf, living_organism> means that for all triples with animal, we need to insert an additional triple.

Control over ontologies◦ Provenance of information◦ Ontologies and instance data are made public◦ Publishers are not in control of their ontologies/data

One super-user inserts all data

Each peer maintains its ontology and instance data

Mappings are (manually) defined between ontologies

Thus, a semantic topology is created Queries are posted according to such a

schema and forwarded following these mappings

Semantic Web counterpart of Federated Databases

Bootstrapping◦ New peers have to manually map their ontologies

to the ontology of a peer already in the network◦ Finding relevant ontologies requires flooding

Routing◦ The overlay is created according to the ontologies

understood by peers, not the data they contain. Possible scalability problem.

◦ Searching for instances requires flooding

Effort to combine both approaches◦ Use a DHT to efficiently find ontologies and

instance data◦ Exploit semantic locality by keeping ontologies

local to the publisher◦ Whenever possible, perform reasoning peer-to-

Peer 1 Peer 2

a dcb e f

g jih k l

m pon q r

s uvt w x

animal:P1

rabbit:P1monk_seal:P2mseal1:P2

habitat:P1 lives_in:P1

seal:P1,P2subClassOf:P1, P2

<seal, subClassOf, animal><animal, lives_in, habitat>

Peer 1 Peer 2

a dcb e f

g jih k l

m pon q r

s uvt w x

animal:P1

rabbit:P1

monk_seal:P2mseal1:P2

habitat:P1

<seal, subClassOf, X?> <Y?, subClassOf, seal>

P1, P2P1, P2

<monk_seal, subClassOf, seal><monk_seal, subClassOf, seal>

<seal, subClassOf, animal><seal, subClassOf, animal>

lives_in:P1

Peer 3

<monk_seal, subClassOf, seal><mseal1, has_type, monk_seal>

<seal, subClassOf, animal><animal, lives_in, habitat>

Peer 1 Peer 2

a dcb e f

g jih k l

m pon q r

s uvt w x

animal:P1

rabbit:P1

monk_seal:P2mseal1:P2

habitat:P1

<monk_seal, subClassOf, X?>Query

monk_seal?

<monk_seal, subClassOf, seal><monk_seal, subClassOf, seal>

<seal, subClassOf, animal><seal, subClassOf, animal>

lives_in:P1

Peer 3

<seal, subClassOf, X?>

Control◦ Access Control◦ Select which data is published on the index◦ Trust – ban spammers, remember good peers

Privacy◦ It is possible to obfuscate descriptors stored in the DHT

Responsibility◦ Publisher has the responsibility to maintain own data

Scalability◦ DHTs can scale to millions of nodes

Data is up-to-date

Based on the data of swoogle, there is currently small overlap between ontologies

The distribution of ontology popularity follows a power-law pattern

If most answers reside on the same peer, our approach outperforms those that rely on triple distribution on top of a DHT

Simulations using SWD from Swoogle and Watson (around 25.000)

Integration of privacy in the index Selecting the right ontologies/peers

George Anadiotis, Spyros Kotoulas and Ronny Siebes VU University Amsterdam.

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