Semantic Web & Peer-To-Peer HY566 Semantic Web Instructor Grigoris Antoniou

1. Semantic Web & Peer-To-Peer HY566 Semantic WebInstructor Grigoris Antoniou Πετράκη Μερόπη Σκυλογιάννης Θωμάς Ιούνιος 2003

2. Issues of Research Work • Semantic Web Vision • Peer to Peer technologies • The JXTA framework • 2 Main directions: Combining SW and P2P • Projects overview • InfoQuilt • Edutella • Elena project • Neurogrid • Swap • Discovery Service based on Edutella infrastructure • Hypercubes, Ontologies and efficient search on P2P networks

3. Peer-To-Peer overview (Napster - Gnutella) • Peer to peer systems have similar goals: to facilitate the location and exchange of files (typically images ,audio, or video) among a large group of independent users connected through the Internet. • In these systems, files are stored on the computers of the individual users or peers, and exchanged through a direct connectionbetween the downloading and uploading peers, over an HTTP-style protocol. • All peers in this system aresymmetric: they all have the ability to function both as a client and a server. • This symmetry distinguishes peerto-peer systems from many conventional distributed system architectures. • Though the process of exchanging files is similar in both systems, Napster and Gnutella differ substantially in howpeers locate files

4. Napster • In Napster, a large cluster of dedicated central servers maintain an index of the files that are currently beingshared by active peers. • Each peer maintains a connection to one of the central servers, through which the filelocation queries are sent. The servers then cooperate to process the query and return a list of matching files andlocations. • On receiving the results, the peer may choose to initiate a file exchange directly from another peer. • In addition to maintaining an index of shared files, the centralized servers also monitor the state of each peer • in the system, keeping track of metadata such as the peers’ reported connection bandwidth and the durationthat the peer has remained connected to the system. • This metadata is returned with the results of a query, sothat the initiating peer has some information to distinguish possible download sites.

5. Gnutella • There are no centralized servers in Gnutella, however. Instead, Gnutella peers form an overlay network byforging point-to-point connections with a set of neighbors. • To locate a file, a peer initiates a controlled flood of the network by sending a query packet to all of its neighbors. • Upon receiving a query packet, a peer checks ifany locally stored files match the query. • If so, the peer sends a query response packet back towards the query originator. • Whether or not a file match is found, the peer continues to flood the query through the overlay. • To help maintain the overlay as the users enter and leave the system, the Gnutella protocol includes pingand pong messages that help peers to discover other nodes.

6. Gnutella vs. Napster architecture

7. 2 Main directions: Combining SW and P2P • The first research direction uses peer-to-peer networks as a basis infrastructure for the exchange of semantic information. The peers can create and maintain their own ontologies and definitions. In addition, they can create their own relationships between their ontologies, or use the ontologies of other peers to create inter relationships. • The second research direction combines semantic web and peer-to-peer technology in order to support schema-based P2P networks. Τhe general objective here is to extend conventional peer-to-peer networks by allowing different and extensible schemas to describe the peer content

8. 1st Direction: InfoQuilt Overview • Challenges we need to address in order to realize SW vision • Info Quilt system • Why are P2P desirable to be an infrastructure for knowledge sharing • System’s Architecture - A multi agent information brokering system • Knowledge space construction and navigation • Semantic Search

9. Semantic web vision • The usage of programs that can “understand” the semantics of the data. • Use of ontologies in order to: • Provide the context for capturing the meaning of data • Capture the user’s intention in a query

10. Challenges we need to address in order to realize SW vision • A way to advertise knowledge and ontologies of different information domains, which are maintained by different persons, groups and organizations onthe Web. • A semantic search mechanism is needed to find most relevant setontologies using users’ context for information request and his profile. • Once the ontologies are locatedthere is a need forintroducing some relationships across ontologies and supporting techniques forontology interoperation • users need tools that would allow them to defineinformation requests

11. Peer to peer Semantic Web (PSW) • Consists of two basic components • DAML+OIL provides a specification framework for independently • creating • maintaining and • interoperating ontologies while preserving their semantics • Peer To Peer (P2P) systems are used to provide a distributed architecture which can support sharing of independently created and maintained ontologies

12. Info Quilt system • A system developed at the university of Georgia which facilitates: • Distributed and autonomous creation and maintenance of local ontologies, • Advertisement (i.e., registry) and search of (local) ontologies, • Introducing inter-ontological relationships between relevant ontologies as-needed basis once they are located, • Controlled sharing of knowledge base components among users in the network, • Ontology-driven semantic search of concepts and services, • Knowledge discovery and exploration of inter-ontological relationships.

13. Why are P2P desirable to be an infrastructure for knowledge sharing • It encourages distributed architecture, and supports decentralization of control and access toinformation and services A way to harness the computing power andknowledge of millions of computers in the web. • It provides access to semantic information published by several independent content providers,and enables creation of personalized semantic relationships. • It supports for publishing peer definitions and relationships to other peers and software agents. • It offers user-centered, data-centered and computing centered models, which providesuitable architectures for distributed content management.

14. Knowledge Discovery • system includes: (a) language and tools to specifyIScapes (i.e., semantic information requests), and (b) tools and algorithms to perform what-if analyses tosearch the information space of semantically related data. • IScapes allow parameterized specification ofinformation requests and correlation that utilizes the domain ontologies, inter-ontological relationshipsand user defined functions to accurately describe a user’s information need. • IScapes are more than atraditional query in that they can understand user’s request by embedding semantic information

15. System’s Architecture - A multi agent information brokering system 2.Provides access and adds new Ontologies and user-defined inter/intra ontological relationships to the knowledge space 1.The ontologies define concepts ,their properties and relations with other ontologies 3.Using keywords the peer searches for relevant sets of ontologies at the knowledge space Composed of local ontologies of independent peers connected by inter-ontological relationships 4.Execution of IScape 6.The results are reranked based on the user profile and other factors

16. Knowledge space construction and navigation (1/3) • When a person defines his own concept or notion based on a predefinedor agreed upon concepts, it is marked up in the knowledge space by these references. • One concept (ontology)that survives is the one that is most referenced, other definitions go byunnoticed. • In using DAML+OIL, the only concepts everybody agrees upon are the basic classes likeThing • When anew ontology is created, it is already hooked up in the knowledge space because of the use of imports andnamespaces • In order for the programs to access this knowledge space programmatically, a data structure is used

17. Knowledge space construction and navigation (2/3) • RDF statement in an ontology can define aclass, its properties and its relationships with other classes. For example a statement would look like <#boy> <#drinks> <#coffee>. • Now all of the triples, boy, drinks and coffee are qualified by use of URIs. • The data structure storessubject (boy), the object (coffee) and the verb (drinks) that relates them. • So the core components of thedata structure are • Concepts: Concepts or KObjects contains information about each class. In theexample, boy and coffee qualify to be KObjects. • Links: Links or relationships contain information about the predicate and the KObjects it relates. In our example drinks qualifies as a Link.

18. Knowledge space construction and navigation (2/3) • For each KObject, the pointers to the Links that has this KObject as a subject or object and the ontology itis defined along with the user information are maintained. • In creation of the knowledge space thefollowing steps are involved: 1. Retrieve every RDF triple (subject, predicate, and object) from each source ontology, 2. For every assertion of a fact or a definition made in the ontology, recursively trace its link to themost general class of the knowledge space (#Thing), 3. Repeat 1 and 2 untill all the ontologies are hooked into the knowledge space. • For knowledge space navigation, we can start with the KObject Thing and then traverse through the Linksin the KObject.

19. Ontology registration • The peers can create and maintain their own ontologies conforming to DAML + OIL formalisms • They have control as whether or not to share an ontology • A peer who decides to share an ontology must upload it to the knowledge space (registration). • New concepts (Kobjects) and relationships (Links) are created appropriately • Once an ontology is uploaded, other peers can refer to its definitions • In case a peer removes his ontology, all definitions and assertions that refer to these definitions become invalid in the knowledge space • Tools like DAML validator can check for obsolete definitions and stale links

20. Semantic Search (1/3) • One of the key advantages of constructing a knowledge space is semantic search. In the IScape Builder,the user specifies the keywords (usually common nouns) used in the information request. • The data structure representing the knowledge space is a collection of KObjects and Links. • The input is a set of keywords and the output is a list ofontologies. Theprocess of searching involves the following steps…

21. Semantic Search (2/3) 1. Take each keyword and run a basic keyword match on the subject, object and the predicate (inthat particular order) in the entire knowledge space, 2. Retrieve the name of the ontologies that satisfy the above match along with the ownership details, 3. If the keywords result in a number of ontologies, compare the ontologies for common parents andeliminate the ontologies without any common links, 4. If there is more than one ontology describing the same keyword, perform search with morekeywords or compare the resulting ontologies to help user select the ontology.

22. Semantic Search (3/3) • One other utility awhich uses theknowledge space is the ability to compare two ontologies. This involves the following steps: 1. Identify the KObjects (concepts) used in each ontology in the knowledge space, 2. Find a common parent KObject that links two Objects that are defined in each of the comparedontologies; in other words, find a connecting link (relationship) between the two ontologies andtrace it for the user.

23. Semantic Search - An example (1/5) • “Find all earthquakes with epicenter in a 5000 mile radius of the location at latitude 60.790North and longitude 97.570 East and find all tsunamis that they might have caused.” • The keywords in the above information request are earthquake, epicenter, radius, location, latitude,longitude, and tsunamis. • Let assume the following results for the keyword matches on the subject,object and the predicate of all the triples in the DAML+OIL ontologies:

24. Semantic Search - An example (2/5)

25. Semantic Search - An example (3/5) • After the above resultsare obtained, we have to arrive at the semantically relevant set of ontologies. • This is done by comparing every KObject in the ontology with every other KObject in the other ontologies. • If they have acommon KObject linked by both the KObjects, they are related (e.g., tsunamis and earthquakes arerelated because they have a common parent, i.e., a KObject, namely disaster).

26. Semantic Search - An example (4/5) • In addition, of these ontologies earthquake.daml, location.daml and tsunami.daml, are linked with KObjects latitude, longitude. So therelevant set of ontologies will be • earthquake.daml • location.daml • tsunami.daml • …discarding damage.daml, weather.daml and circle.daml. • The ontology weather.daml is discarded even though it has a reference to the definition of location because, although the definition of weather involves country, which is a sub-class of location it is notrelated in the sense it does not have a common parent with earthquake and tsunami.

27. Semantic Search - An example (5/5) • Thus, the systemconsiders earthquake, tsunami, and location ontologies as relevant because this is the minimal set ofontologies with all keyword matches and has at least one common KObject linked.

28. 2nd Direction :Οverview • Introduction to the second main research area • Problems of current P2P implementations • Project Edutella • JXTA Framework • Overview: Edutella Services • Edutella Query Service • Example: O-Telos provider peer

29. The 2nd research area: Semantic Web & Peer-to-Peer • Combines semantic web and peer-to-peer technology in order to support schema-based P2P networks • Aims to extend conventional peer-to-peer networks by allowing different and extensible schemas to describe the peer content • “Metadata for the WWW are important, but metadata for Peer-to-Peer networks are absolutely crucial”

30. Problems of P2P applications • Information Resources in P2P networks are no longer organized in hypertext like structures • Information resources are stored on numerous peer waiting to be queried • If we know what we want to retrieve • Which peer is able to provide that information • Querying peers requires metadata describing the resources • Easy for specialized cases: like exchanging music files • Non trivial for general applications: like exchanging educational material

31. Problems of P2P applications • Current P2P implementations: • Concentrate on domain specific formats: appear to be fragmenting into niche markets • No unifying mechanisms for future P2P applications • There is indeed a great danger that unifying interfaces and protocols introduced by the WWW get lost in the P2P arena

32. Project Edutella • Edutella Project: addresses shortcomings of current P2P applications by building on W3C metadata standard RDF • Edutella is a metadata-based P2P system • Integrate heterogeneous peers (Different repositories, Query languages, Functionality) • Different kinds of metadata schemas • Common ground is an essential assumption • All resources (metadata) maintained in the Edutella network can be described in RDF • First application:a P2P network for the exchange of education resources

33. Background: The JTXA P2P Framework • Project JXTA (Sun Microsystems) • An open source programming platform to enable P2P services and applications • Interoperability, Platform Independence • Layered Approach

34. JXTA & Edutella • Edutella Services • Complements the JXTA Service Layer • Build upon the JXTA Core Layer • Described in web service language like DAML-S, WSDL • Edutella Peers • Live on the JXTA Application layer • Are using the functionality provided by the Edutella Services

35. Edutella Services • Query Services • Standardized query and retrieval of RDF metadata • Replication Services • Providing data persistence/availability and workload balancing • Mapping Service • Translate between different metadata vocabularies to enable interoperability between different peers. • Annotation Service • Annotate materials stored anywhere in the Edutella Network.

36. Edutella Query Services • Peer register the queries they may be asked through the query service • Specifying the supported metadata schema • Peer provide metadata according to DCMI standards • Specifying the individual properties • Peer provides metadata of the form dc_title(X,Y) • Queries are sent through the Edutella network to the subset of peers who have registered to be interested in this kind of query

37. Mediation, metadata mapping Edutella Hub Edutella Working Scenario Edutella Consumer Edutella Provider Edutella Provider Registration: Query: Replication:

38. Query Exchange Architecture • Edutella Common Data Model (ECDM) • Provides the syntax and semantics for an overall standard query interface • Edutella network uses the query language family RDF-QEL as a standardized query language format • How to enable the peer to participate in the Edutella network? • Edutella wrappers are used to translate queries and results from the Edutella query and result exchange format (ECDM) to the local format and vice versa • There are several RDF-QEL-i exchange language levels describing which kind of queries a peer can handle

39. Edutella Common Data Model (ECDM)

40. RDF-QEL-i Language Levels • RDF-QEL-1 • Simple and readable syntax following the QBE paradigm • Query graph has exact the same structure as the answer graph • Logical conjunctive formula • RDF-QEL-2 • Extends the 1-level with disjunction • Reified RDF statements are building blocks for each query • Linked together by an AND-OR tree

41. RDF-QEL-i Language Levels • RDF-QEL-3 • Allows conjunction, disjunction and negation of literals • RDF-QEL is essentially Datalog • RDF-QEL-4 • Allows recursion to express transitive closure • Compatible with SQL3 • Relational query engine with full conformance to the SQL3 standard will be able to support RDF-QEL-4

42. Example Knowledge Base Query: Return all resources that are a book having the title ‘Artificial Intelligence’ or that are an AI book

43. Example Query: RDF-QEL-1

44. Example Query: RDF/XML serialization <edu:QEL1Query rdf:ID="AI_Query_1"> <edu:hasVariable rdf:resource="#X"/> </edu:QEL1Query> <edu:Variable rdf:ID="X" rdfs:label="X"> <rdf:type rdf:resource="http://www.lit.edu/types#AIBook"/> </edu:Variable> <edu:QEL1Query rdf:ID="AI_Query_2"> <edu:hasVariable rdf:resource="#Y"/> </edu:QEL1Query> <edu:Variable rdf:ID="Y" rdfs:label="X"> <rdf:type rdf:resource="http://www.lit.edu/types#Book"/> <dc:title>Artificial Intelligence</dc:title> </edu:Variable>

45. Standard Result Set • Query results are represented as as set of tuples of variables and their bindings <edu:ResultSet rdf:ID="AI_Results"> <edu:hasResult rdf:parseType="Resource"> <rdf:type rdf:resource="edu:TupleResult"/> <edu:hasVariable rdf:parseType="Resource"> <rdf:type rdf:resource="edu:VariableBinding"/> <edu:bindsVariable rdf:resource="#X"/> <rdf:valuerdf:resource="http://www.xyz.com/ai.html"/> </edu:hasVariable> </edu:hasResult> </edu:ResultSet>

46. An O-Telos provider peer for the RDF-based Edutella P2P network

47. O-Telos Provider • Describes a provider peer and its services for the Edutella network • Provider-peer uses the ConceptBase as a repository for storing meta-data • ConceptBase: implements a meta language representation language O-Telos

48. Provider Peer: Two basic services • Storage service: designed to store RDF(S) data in the ConceptBase repository • The data represented in RDF(S) are translated to O-Telos • Query service: the provider-peer serves as query interface to the RDF data stored in the ConceptBase • Queries are formulated in RDF-QEL • The peer translates the them into O-Telos queries • O-Telos queries are answered by the ConceptBase • The peer translates answers from O-Telos into RDF

49. Schematic Representation of the architecture

50. Questions ??? End!