1 / 95

Semantic Web: State of the Art and Opportunities

Semantic Web: State of the Art and Opportunities. Industrial Ontologies Group. Vagan Terziyan Compiled, partly based on various online tutorials and presentations, with respect to their authors “Industrial Ontologies” Group http://www.cs.jyu.fi/ai/OntoGroup/index.html.

reuben-bishop
Download Presentation

Semantic Web: State of the Art and Opportunities

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Semantic Web:State of the Art and Opportunities Industrial Ontologies Group Vagan Terziyan Compiled, partly based on various online tutorials and presentations, with respect to their authors “Industrial Ontologies” Group http://www.cs.jyu.fi/ai/OntoGroup/index.html University of Jyväskylä

  2. 2020 And Beyond …….. Middle Agent Prafful’s Agent ContactsA Middle Agent to find out some hospital in powaihaving a recently admittedpatient named Hansa. 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 for Semantic Web

  4. Where we are Today: the Syntactic Web [Hendler & Miller 02]

  5. i.e. the Syntactic Web is… • A place where • computers do the presentation (easy) and • people do the linking and interpreting (hard). • Why not get computers to do more of the hard work? [Goble 03]

  6. Hard Work using the Syntactic Web… • Complex queries involving background knowledge • Find information about “animals that use sonar but are not either bats, dolphins or whales” • Locating information in data repositories • Travel enquiries • Prices of goods and services • Results of human genome experiments • Delegating complex tasks to web “agents” • Book me a holiday next weekend somewhere warm, not too far away, and where they speak French or English Almost impossible for machines and too hard for people without automation

  7. Limitations of the Web today Machine-to-human, not machine-to-machine

  8. Summarizing the Problem: Computers don’t understand Meaning • “My mouse is broken. I need a new one…” Use of ontology “My mouse is broken” vs. “My mouse is dead”

  9. Approach: Semantic Web “The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications” http://www.w3.org/sw/ The Semantic Web is an initiative with the goal of extending the current Web and facilitating Web automation, universally accessible web resources, and the 'Web of Trust', providing a universally accessible platform that allows data to be shared and processed by automated tools as well as by people.

  10. Tim Berners-Lee's Vision of Semantic Web (IJCAI-01)

  11. Semantic Web: New “Users” applications agents

  12. Semantic Web: Annotations applications agents Semantic annotations are specific sort of metadata, which provides information about particular domain objects, values of their properties and relationships, in a machine-processable, formal and standardized way.

  13. Semantic Web: Ontologies applications agents Ontologies make metadata interoperable and ready for efficient sharing and reuse. It provides shared and common understanding of a domain, that can be used both by people and machines. Ontologies are used as a form of agreement-based knowledge representation about the world or some part of it and generally describe: domain individuals, classes, attributes, relations and events.

  14. Semantic Web: Rules applications agents Logical support in form of rules is needed to infer implicit content, metadata and ontologies from the explicit ones. Rules are considered to be a major issue in the further development of the semantic web. On one hand, they can be used in ontology languages, in conjunction with or as an alternative to description logics. And on the other hand, they will act as a means to draw inferences, to configure systems, to express constraints, to specify policies, to react to events/changes, to transform data, to specify behavior of agents, etc.

  15. Semantic Web: Languages applications agents Languages are needed for machine-processable formal descriptions of: metadata (annotations) like e.g. RDF; ontologies like e.g. OWL.; rules like e.g. RuleML. The challenge is to provide a framework for specifying the syntax (e.g. XML) and semantics of all of these languages in a uniform and coherent way. The strategy is to translate the various languages into a common 'base' language (e.g. CL or Lbase) providing them with a single coherent model theory.

  16. Semantic Web: Tools applications agents User-friendly tools are needed for metadata manual creation (annotating content) or automated generation, for ontology engineering and validation, for knowledge acquisition (rules), for languages parsing and processing, etc.

  17. Semantic Web: Applications and Services applications agents Utilization of Semantic Web metadata, ontologies, rules, languages and tools enables to provide scalable Web applications and Web services for consumers and enterprises" making the web 'smarter' for people and machines.

  18. The Semantic Web The Ontology Articulation Toolkit helps agents to understand unknown ontologies

  19. Can’t we just use XML? This is what a web-page in natural language looks like for a machine J. Hendler

  20. < > name < > education < > CV < > work < > private XML helps XML allows “meaningful tags” to be added toparts of the text J. Hendler

  21. XML machine accessible meaning < > < name > name <education> < > education < CV > < > CV <work> < > work <private> < > private But to your machine, the tags look like this…. J. Hendler

  22. Schemas take a step in the right direction Schemas help…. < CV > …by relating common termsbetween documents private J. Hendler

  23. But other people use other schemas < > name < > education < > CV < > work < > private Someone else has one like this…. name> <educ> < CV > <> <> J. Hendler

  24. The “semantics” isn’t there < CV > …which don’t fit in private J. Hendler

  25. KR provides “external” referents to merge on nme CV CV work vate CV educ educ Semantic Web languages add mappings and structure. J. Hendler

  26. Semantic Web basics… • RDF: • is a W3C standard, which provides tool to describe Web resources • provides interoperability between applications that exchange machine-understandable information • RDF Schema: • is a W3C standard which defines vocabulary for RDF • organizes this vocabulary in a typed hierarchy • capable to explicitly declare semantic relations between vocabulary terms

  27. RDF – Semantic Web over Web Resources John has_homepage Director has_job to_be_in_love_with Ontology has_job has_homepage Secretary Mary

  28. Resources • All things being described by RDF expressions are called resources: • entire Web page; • a specific XML element; • whole collection of pages; • an object that is not directly accessible via the Web.

  29. Resources and URIs • A resource can be anything that has identity • Uniform Resource Identifiers (URI)* provide a simple and extensible means for identifying a resource • Not all resources are network "retrievable"; e.g., human beings, corporations, and books in a library can also be considered resources * The term "Uniform Resource Locator" (URL) refers to the subset of URI that identify resources via a representation of their primary access mechanism (e.g., their network "location"), rather than identifying the resource by name or by some other attribute(s) of that resource.

  30. URI Venn diagram of Uniform Resource Identifier (URI) scheme categories. Schemes in the URL (locator) and URN (name) categories both function as resource IDs, so URL and URN are subsets of URI. They are also, generally, disjoint sets. However, many schemes can't be categorized as strictly one or the other, because all URIs can be treated as names, and some schemes embody aspects of both categories – or neither.

  31. RDF Statement • Subject of an RDF statement is a resource • Predicate of an RDF statement is a property of a resource • Object of an RDF statement is the value of a property of a resource

  32. Example of RDF Statement Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila. Subject (resource) http://www.w3.org/Home/Lassila Predicate (property) Creator Object (literal) “Ora Lassila”

  33. RDF Example (serialization syntax) Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila. <rdf:RDF> <rdf:Description about= "http://www.w3.org/Home/Lassila"> <s:Creator>Ora Lassila</s:Creator> </rdf:Description> </rdf:RDF> 's' is a specific namespace prefix, e.g. xmlns:s="http://description.org/schema/"

  34. RDF Example (abbreviated syntax) Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila. <rdf:RDF> <rdf:Descriptionabout="http://www.w3.org/Home/Lassila" s:Creator="Ora Lassila" /> </rdf:RDF>

  35. Statements about Statements (1) “Ralph Swick says that Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila” An unnamed node is the source of all five arcs. The first arc is labelled rdf:type and points to the node identified as rdf:Statement. The second arc is labelled rdf:predicate and points to the node identified as s:Creator. The third arc is labelled rdf:subject and points to a node labelled http://www.w3.org/Home/Lassila. The fourth arc is labelled rdf:object and points to a node containing the string value "Ora Lassila". The fifth and final arc is labelled a:attributedTo and points to a node containing the string value "Ralph Swick".

  36. Statements about Statements (2) “Ralph Swick says that Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila” <rdf:RDF xmlns:rdf="http://w3.org/TR/1999/PR-rdf-syntax-19990105#" xmlns:a="http://description.org/schema/"> <rdf:Description> <rdf:subjectresource="http://www.w3.org/Home/Lassila" /> <rdf:predicateresource="http://description.org/schema#Creator" /> <rdf:object>Ora Lassila</rdf:object> <rdf:typeresource="http://w3.org/TR/1999/PR-rdf-syntax- 19990105#Statement" /> <a:attributedTo>Ralph Swick</a:attributedTo> </rdf:Description> </rdf:RDF>

  37. What is RDFS ? • RDF Schema • Defines vocabulary for RDF • Organizes this vocabulary in a typed hierarchy(Class, subClassOf, type, Property, subPropertyOf) • Rich, web-based publication format for declaring semantics (XML for exchange) • Capability to explicitly declare semantic relations between vocabulary terms

  38. RDF Schema • Semantic network on the Web • Nodes are identified by URIs • rdfs:Class • rdfs:Property • rdfs:subClassOf

More Related