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Semantic Web

“Realizing What Semantic Web Can Be…….”. Semantic Web. By, Anup Patel (07305042) Tanmay Mande (07305051) Sapan Shah (07305061) Nilesh Padariya (07305064). 2020 And Beyond ……. Middle Agent.

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Semantic Web

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  1. “Realizing What Semantic Web Can Be…….” Semantic Web By,Anup Patel (07305042)Tanmay Mande (07305051)Sapan Shah (07305061)Nilesh Padariya (07305064)

  2. 2020 And Beyond …….. Middle Agent Prafful’s Agent ContactsA Middle Agent to find out some hospital in powaihaving a recently admittedpatient named Gita. Agent: “Your wife is admitted at New Powai Hospital Ward No. 9” Agent: “Your meeting is re-scheduled to tomorrow 5:00 PM” New Powai Hospital Phone: “Your wife had an accident she is admitted at some hospital in powai …” Prafful: “I still don’t know where is she admitted in powai …. I should use my agent ….” Prafful: “I should inform my agent to reschedule meeting” Prafful: “I have a meeting with my boss and I am late …….” Prafful’s Agent Negotiates WithBoss’s Agent and re-schedule meeting to tomorrow.

  3. Motivation • Original driver: Automation - Make information on the Web more “machine-friendly” - Origins of the Semantic Web are in web metadata • Short term goal: Interoperability- Combining information from multiple sources- Web Services: discovery, composition • Long term goal: “Departure from the Tool Paradigm”- instead of using computers like tools, make them work on our behalf- removing humans from the loop to the extent possible

  4. Roadmap • Introduction to Semantic Web • Knowledge Representation • Agents in Semantic Web • Multi-Agent System Communication • Agent Communication Language • SPARQL • Status of Semantic Web • Conclusion • Bibliography

  5. 1. Semantic Web The Semantic Web is an evolving extension of the World Wide Web in which web content can be expressed not only in natural language, but also in a format that can be read and used by software agents, thus permitting them to find, share and integrate information more easily. -- Wikipedia

  6. 1.1 Semantic Web Architecture Trustworthiness Reasoning Knowledge Sharing Knowledge Representation

  7. 1.2 Tree of Knowledge Technologies Content Management Languages Semantic Technology Languages Process Knowledge Languages AI Knowledge Representation Software Modeling Languages

  8. 2. Ontologies in Semantic Web • What? .. Is an ontology • Why? .. Do we need ontology • How? .. Do we use ontology

  9. 2.1 What? • Ontology is a term borrowed from philosophy that refers to the science of describing the kinds of entities in the world and how they are related. • An ontology is explicit specification of conceptualization • An ontology defines the terms used to describe and represent an area of knowledge • Ontology provides the to be used in a knowledge domain

  10. 2.2 Why? • Does Web today understand information? • No • Can we make it understand the information? • Yes..First provide vocabulary!! Ontology

  11. 2.3 How? • Web Ontology Language (owl) • Goal is to provide machine-readable descriptions of the content and capabilities of Web accessible resources • Ontology may include descriptions of classes, properties and their instances. • Given such an ontology, the OWL formal semantics specifies how to derive its logical consequences, i.e. facts not literally present in the ontology, but entailed by the semantics.

  12. 2.4 OWL constructs • Classes: <owl:Class rdf:ID=“man"> <rdfs:subClassOf rdf:resource="#human" /> </owl:Class> • Properties: • Datatype properties: relates objects to datatype values • Object Properties: relates objects to objects • Example: . <owl:ObjectProperty rdf:ID=“isFatherOf"> <rdfs:domain rdf:resource="#man"/> <rdfs:range rdf:resource="#man"/> </owl:ObjectProperty>

  13. 2.4 OWL constructs (Contd.) • Property Restrictions: used when one requires to put some constraints • Example:<owl:Restriction> <owl:onProperty rdf:resource="#isFatherOf"/> <owl:maxCardinality rdf:datatype="&xsd;nonNegativeInteger"> 1 </owl:maxCardinality> </owl:Restriction> • Possible Use: A ‘is father of’ X & B ‘is father of’ X  ‘A = B’

  14. 2.5 Example • Interested in Buying a Ticket? • Scenario: I am interested in buying a ticket. • start_point: Mumbai • end_point: Delhi • date_of_journey: xxx • I launch my personal "Web agent" which crawls the Web looking for Web sites that can fulfill my request • Assume that there exists an OWL ‘Travel’ Ontology, which the Web agent can "consult" upon its travels across the Web.

  15. 2.6 Example (Contd.) The Web Agent finds this document at a Web site: <TravelAgent rdf:ID=“TravelEasy" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"> <location>Mumbai</location> <phone>9911224455</phone> <catalog rdf:parseType="Collection"> <airTicket rdf:ID=“AirIndia“ xmlns="http://www.air_india.org#"> <source> Mumbai </source> <destination> Delhi </destination> <date> xxx </date> <cost rdf:parseType="Resource"> <rdf:value>3250</rdf:value> <currency>Rs</currency> </cost> </airTicket> </catalog> </TravelAgent> Is it relevant ?

  16. 2.6 Example (Contd.) • Is there a match ? • To answer this question, following questions must be answered: • Is there a match between airTicket and ticket? • Is there a match between start-point and source? • Is there a match between end-point and destination?

  17. 2.6 Example (Contd.) • Relationship between ticket and airTicket? • The Web agent "consults" the OWL travel Ontology. • This OWL statement tells the Web agent that a airTicketis a type of ticket: <owl:Class rdf:ID=“airTicket"> <rdfs:subClassOf rdf:resource="#ticket"/> </owl:Class> "Relationship between ticket and airTicket?" <TravelAgent rdf:ID=“TravelEasy" <airTicket> … </airTicket> </TravelAgent> <owl:Class rdf:ID=“airTicket"> <rdfs:subClassOfrdf:resource="#ticket"/> </owl:Class> Web Agent “airTicket is a type of ticket." Travel.owl TravelAgent.xml

  18. 2.6 Example (Contd.) • Relationship between start-point and source? • This OWL statement tells the Web agent that Start-point is equivalent to source: <owl:DatatypeProperty rdf:ID=“start-point"> <owl:equivalentProperty rdf:resource="#source"/> <rdfs:domain rdf:resource="#place"/> <rdfs:range rdf:resource="&xsd;#airplane"/> </owl:DatatypeProperty> start-point is synonymous with source.

  19. 2.6 Example (Contd.) • Relationship between date and date_of_journey? • This OWL statement tells the Web agent that date is equivalent to date_of_journey: <owl:DatatypeProperty rdf:ID=“date"> <owl:equivalentProperty rdf:resource="#date_of_journey"/> <rdfs:domain rdf:resource="#airplane"/> <rdfs:range rdf:resource="&xsd;#Date"/> </owl:DatatypeProperty>

  20. 2.6 Example (Contd.) • The Web agent now recognizes that the XML document it found at the Web site • Is talking about tickets • It does show the start-point and end-point • It does show a date • Values are matching • Thus, the Web agent recognizes that the XML document is a match!

  21. 3. Agents in Semantic Web • Agent in AI is any thing that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors, showing a rational behavior.E.g. A human agent has eyes, ears and other organs as as sensors, and hands, legs, mouth, and other body parts for effectors. • Agent = Architecture + Program. • Semantic Web Agents are agents in the web environment.

  22. 3.1 Agent Definition • The definition of agents has not been agreed upon universally but, we can have some good characteristic of such agents, which are : - Autonomy - Reasoning Ability - Learning Ability - Mobility - Sociability - Cooperation - Negotiation

  23. 3.1 Agent Definition (Contd..) • From semantic web point of view agents can be thought of as intelligent software program that host a collection of web services. • Unlike standard Web Services, an agent can reason about: - How to handle external request ? - Order in which to carry out the request ?

  24. 3.2 Multi-Agent System (MAS) • MAS is distributed system which incorporates more than one independent agents. • The collection of agents interact, and solve problems that are outside their individual capacities. • Agents in MAS display a dual behavior: on the one hand they are goal directed programs that autonomously solve problems and on the other hand have a social dimension when they interoperate as part of MAS. • Semantic web in future will be one large MAS containing millions of agents communicating with each other.

  25. 3.2 Multi-Agent System (Contd.) • Ontologies in MAS provide agents :- The basic representation that allows them to reason about interactions with other agents.- Shared knowledge that they can use to communicate and work together. • In general we can distinguish between Private Ontologies that allow the agent to organize its own problem solving and reasoning, and Public Ontologies that the agent shares with the rest of the agents in the MAS. • Private ontologies are used to represent Private Knowledgewhereas, public ontologies are used to represent Public Knowledge of an semantic web agent.

  26. 3.2 Multi-Agent System (Contd.) • Example to illustrate use of private and public knowledge. Private Knowledge Private Knowledge Public Knowledge Public Knowledge

  27. 4. MAS Communication • In MAS communication we are effectively seeking to mimic the process of (verbal) communication between humans, which by itself is very ambitious task. • At the lowest level, there are two main techniques that facilitate communication:- Message Passing: The agents communicate by the direct exchange of messages that encapsulate knowledge.- Shared State: The Agents communicate by asserting and retracting facts in a shared knowledge base. • The web uses a message passing approach (TCP + UDP) so, semantic web communication also have based on message passing approach (HTTP + XML).

  28. 4. MAS Communication (Contd.) • For communication on semantic web some issues must bepromptly addressed, like:- Automatic discovery of agents.- Effectively manage the shared knowledge.- It must be coordinated, correct, and robust to failure. • To solve the problem of automatic discovery of agents we have Middle-Agent architectures. • To solve the problem of managing shared knowledge wehave network architectures.

  29. 4.1 Middle Agent Architecture • Middle-agents assist in locating service providers, and connecting service providers with service requesters. • A variety of middle agent types based on privacy considerations of service providers capabilities and requesters preferences are possible. • Middle Agent Architectures are techniques to solve problem of automated discovery of agents in MAS.

  30. 4.1 Middle Agent Architecture (Contd.) • Two important types of middle-agent have been identified. • Service Matchmaker: The Matchmaker serves as a "yellow pages" of agent capabilities, matching service providers with service requestors based on agent capability descriptions.The Matchmaker system allows agents to find each other by providing a mechanism for registering each agent's capabilities.For each query it searches its dynamic database of "advertisements" for a registered agent that can fulfill theincoming request.

  31. 4.1 Middle Agent Architecture (Contd.) Service Matchmaker

  32. 4.1 Middle Agent Architecture (Contd.) • Service Broker: Service Broker is similar to matchmaker, but also processes the requests. Service Broker

  33. 4.1 Middle Agent Architecture (Contd.) • A variety of middle agent types based on privacy considerations of service providers capabilities and requesters preferences are possible.

  34. 4.2 Network Architecture • Network Architectures so far, mainly assumed some kind of centralized client/server architecture. But Service Oriented Architectures can equally well be decentralized. • Network Architectures are techniques to effectively storeand retrieve shared knowledge of all agents in MAS. • We can three types of architectures possible here: - Centralized (Client-Server) - Decentralized (Peer-to-Peer) - Hybrid (Client-Server and Peer-to-Peer)

  35. 4.2 Network Architecture (Contd.) • Centralized (Client-Server):

  36. 4.2 Network Architecture (Contd.) • In Client-Server system, a centralized server is used to manage the shared resources. • Servers works as central repository of the shared resources or the shared knowledge. • It is very easy to adapt current knowledge representation like owl and rdf for client-server system. • There are hard limits to number of clients that can be servedfrom a single server or a cluster of servers. This limits are primarily a function of available network bandwidth.

  37. 4.2 Network Architecture (Contd.) • Decentralized (Peer-to-Peer):

  38. 4.2 Network Architecture (Contd.) • P2P is a self-organizing system of equal, autonomous entities (peers) which aims for the shared usage of distributed resources in a networked environment avoiding central services. • Peers interact directly with each other, usually without central coordination. Each peer has autonomy over its own resources. • Peers can act as both clients and servers; i.e., no intrinsic asymmetry of role. • The network saturation problem does not occur todecentralized P2P network.

  39. 4.2 Network Architecture (Contd.) • In this approach information is copied and distributed throughout network. Thus, when a client wish to obtain some information it can retrieve it from multiple sources and thereby avoid overloading at one node.For Example: Bit Torrent, DC++ • Construction of P2P architecture for semantic web has important design implications :- The communicative process must be adapted to work with specific P2P technique.- The reasoning process must make decisions on what information to share and how to retrieve information required for reasoning.

  40. 4.2 Network Architecture (Contd.) • Hybrid (Client-Server and Peer-to-Peer):

  41. 5. Agent Communication Language • Abbreviated as ‘ACL’ for short. • In agent communication our source of inspiration in human communication. • We try to mimic human communication in ACL. • The foundation of ACL lies in the Speech Act Theory.

  42. 5.1 Speech Act • Proposed by John Austin extended by John Searle. • How language is used by people everyday to achieve their goals and intentions. • Certain natural language utterances have the characteristics of physical actions. • Certain performative verbs in speech act changes the state of the world like physical actions.

  43. 5.2 Types of Speech Acts • Representative: which commits the speaker to the truth of what is being asserted. e.g. inform • Directive: attempts to get the hearer to do something e.g., ‘please make the tea’ • Commisives: which commit the speaker for doing something, e.g., ‘I promise to…’ • Expressive: whereby a speaker expresses a mental state, e.g., ‘thank you!’ • Declarative: effect some change on the state of affairs.e.g. declaring war.

  44. 5.3 Components of Speech Act • In general Two Components: – Performative Verb (e.g., request, inform, promise, … ) – Propositional Content (e.g., “the door is closed”) • More Examples: performative = request content = “the door is closed” speech act = “please close the door” performative = inquire content = “the door is closed” speech act = “is the door closed ?”

  45. 5.4 ACL Examples • Communication is performed by exchanging messages where each message has an associated performative-message types. • Agent Communication Languages define common sets of performatives. • Two Popular ACLs - KQML - FIPA-ACL.

  46. 5.5 FIPA-ACL Performative Ontology

  47. 5.6 Basic Problem of FIPA-ACL • Semantics Verification Problem Sincerity Assumption – agent always acts in accordance with their intentions. • Too restrictive in open environment – web. • Despite these FIPA-ACL remained popular - e.g. JADE multi agent platform – performatives are used to facilitate the exchange of message but compliance with formal model is not enforce.

  48. 5.7 Dialogue • Communication rarely consists of a single act of speech in isolation. • It typically consists of sequence of messages exchanges between participants such as Conversation. • This type of communication is termed as Dialogue.

  49. 5.8 Categories of Dialogues

  50. 5.9 Dialogue frames • Key construct – Dialogue Type identifies dialogue type & kind of values over which it operates. • Different Dialogues can take different kind of values. e.g. Beliefs, Contract, Plans • Frame F is a tuple with four elements ( T, V , t, U) T = Dialogue Type V = Value over which the dialogue operates t = Topic of the Dialogue U = list of utterances which define the actual dialogue steps between the participants x & y e.g. {U}

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