Majid Sazvar Knowledge Engineering Research Group Ferdowsi University of Mashhad Semantic Web Reasoning.

Slides:



Advertisements
Similar presentations
Slide 1 of 18 Uncertainty Representation and Reasoning with MEBN/PR-OWL Kathryn Blackmond Laskey Paulo C. G. da Costa The Volgenau School of Information.
Advertisements

Intelligent Technologies Module: Ontologies and their use in Information Systems Revision lecture Alex Poulovassilis November/December 2009.
The 20th International Conference on Software Engineering and Knowledge Engineering (SEKE2008) Department of Electrical and Computer Engineering
CH-4 Ontologies, Querying and Data Integration. Introduction to RDF(S) RDF stands for Resource Description Framework. RDF is a standard for describing.
By Ahmet Can Babaoğlu Abdurrahman Beşinci.  Suppose you want to buy a Star wars DVD having such properties;  wide-screen ( not full-screen )  the extra.
SIG2: Ontology Language Standards WebOnt Briefing Ian Horrocks University of Manchester, UK.
Of 27 lecture 7: owl - introduction. of 27 ece 627, winter ‘132 OWL a glimpse OWL – Web Ontology Language describes classes, properties and relations.
Provenance in Open Distributed Information Systems Syed Imran Jami PhD Candidate FAST-NU.
The Web of data with meaning... By Michael Griffiths.
Research topics Semantic Web - Spring 2007 Computer Engineering Department Sharif University of Technology.
Ontologies and the Semantic Web by Ian Horrocks presented by Thomas Packer 1.
PR-OWL: A Framework for Probabilistic Ontologies by Paulo C. G. COSTA, Kathryn B. LASKEY George Mason University presented by Thomas Packer 1PR-OWL.
Semantic Web and Web Mining: Networking with Industry and Academia İsmail Hakkı Toroslu IST EVENT 2006.
Where are the Semantics in the Semantic Web? Michael Ushold The Boeing Company.
 Manmatha MetaSearch R. Manmatha, Center for Intelligent Information Retrieval, Computer Science Department, University of Massachusetts, Amherst.
WebMiningResearch ASurvey Web Mining Research: A Survey By Raymond Kosala & Hendrik Blockeel, Katholieke Universitat Leuven, July 2000 Presented 4/18/2002.
Semantics For the Semantic Web: The Implicit, the Formal and The Powerful Amit Sheth, Cartic Ramakrishnan, Christopher Thomas CS751 Spring 2005 Presenter:
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall Chapter Chapter 7: Expert Systems and Artificial Intelligence Decision Support.
EXPERT SYSTEMS Part I.
1 CIS607, Fall 2005 Semantic Information Integration Presentation by Zebin Chen Week 7 (Nov. 9)
Knowledge Representation Reading: Chapter
Samad Paydar Web Technology Laboratory Computer Engineering Department Ferdowsi University of Mashhad 1389/11/20 An Introduction to the Semantic Web.
Building Knowledge-Driven DSS and Mining Data
1 DCS861A-2007 Emerging IT II Rinaldo Di Giorgio Andres Nieto Chris Nwosisi Richard Washington March 17, 2007.
Enhance legal retrieval applications with an automatically induced knowledge base Ka Kan Lo.
Software engineering on semantic web and cloud computing platform Xiaolong Cui Computer Science.
Web 3.0 or The Semantic Web By: Konrad Sit CCT355 November 21 st 2011.
Sepandar Sepehr McMaster University November 2008
Semantic Web Technologies Lecture # 2 Faculty of Computer Science, IBA.
Beyond Legal XML Günther Schefbeck 4th Workshop „Legislative XML“ Klagenfurt, 18 November 2005.
Semantic Interoperability Jérôme Euzenat INRIA & LIG France Natasha Noy Stanford University USA.
RDF (Resource Description Framework) Why?. XML XML is a metalanguage that allows users to define markup XML separates content and structure from formatting.
Katanosh Morovat.   This concept is a formal approach for identifying the rules that encapsulate the structure, constraint, and control of the operation.
Artificial Intelligence Introduction (2). What is Artificial Intelligence ?  making computers that think?  the automation of activities we associate.
Notes for Chapter 12 Logic Programming The AI War Basic Concepts of Logic Programming Prolog Review questions.
Mining the Semantic Web: Requirements for Machine Learning Fabio Ciravegna, Sam Chapman Presented by Steve Hookway 10/20/05.
 Knowledge Acquisition  Machine Learning. The transfer and transformation of potential problem solving expertise from some knowledge source to a program.
Abdul Rahim Ahmad MITM 613 Intelligent System Chapter 1: Introduction To Intelligent Systems.
TDT44 – Semantic Web Rune Sætre Jon Atle Gulla
Linked-data and the Internet of Things Payam Barnaghi Centre for Communication Systems Research University of Surrey March 2012.
LOGIC AND ONTOLOGY Both logic and ontology are important areas of philosophy covering large, diverse, and active research projects. These two areas overlap.
UNCERTML - DESCRIBING AND COMMUNICATING UNCERTAINTY WITHIN THE (SEMANTIC) WEB Matthew Williams
Artificial Intelligence: Introduction Department of Computer Science & Engineering Indian Institute of Technology Kharagpur.
Semantic based P2P System for local e-Government Fernando Ortiz-Rodriguez 1, Raúl Palma de León 2 and Boris Villazón-Terrazas 2 1 1Universidad Tamaulipeca.
THE SUPPORTING ROLE OF ONTOLOGY IN A SIMULATION SYSTEM FOR COUNTERMEASURE EVALUATION Nelia Lombard DPSS, CSIR.
Exploitation of Semantic Web Technology in ERP Systems Amin Andjomshoaa, Shuaib Karim Ferial Shayeganfar, A Min Tjoa (andjomshoaa, skarim, ferial,
Knowledge Management: The On-To-Knowledge Project Hans Akkermans Free University Amsterdam VUA.
Introduction to the Semantic Web and Linked Data
Trustworthy Semantic Webs Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #4 Vision for Semantic Web.
Of 33 lecture 1: introduction. of 33 the semantic web vision today’s web (1) web content – for human consumption (no structural information) people search.
Issues in Ontology-based Information integration By Zhan Cui, Dean Jones and Paul O’Brien.
KNOWLEDGE BASED SYSTEMS
International Conference on Fuzzy Systems and Knowledge Discovery, p.p ,July 2011.
Co-funded by the European Union Semantic CMS Community Reference Architecture for Semantic CMS Copyright IKS Consortium 1 Lecturer Organization Date of.
A Portrait of the Semantic Web in Action Jeff Heflin and James Hendler IEEE Intelligent Systems December 6, 2010 Hyewon Lim.
Semantic Data Extraction for B2B Integration Syntactic-to-Semantic Middleware Bruno Silva 1, Jorge Cardoso 2 1 2
Artificial Intelligence
Of 24 lecture 11: ontology – mediation, merging & aligning.
Chapter 8A Semantic Web Primer 1 Chapter 8 Conclusion and Outlook Grigoris Antoniou Frank van Harmelen.
EXPERT SYSTEMS BY MEHWISH MANZER (63) MEER SADAF NAEEM (58) DUR-E-MALIKA (55)
By Muhammad Safdar MCS [E-Section].  There are times in life when you are faced with challenging decisions to make. You have rules to follow and general.
Mechanisms for Requirements Driven Component Selection and Design Automation 최경석.
The Semantic Web By: Maulik Parikh.
CHAPTER 1 Introduction BIC 3337 EXPERT SYSTEM.
Chapter 25 - Automated Web Search (Search Engines)
ece 720 intelligent web: ontology and beyond
KNOWLEDGE REPRESENTATION
Ontology-Based Approaches to Data Integration
Social Abstractions for Information agents
ONTOMERGE Ontology translations by merging ontologies Paper: Ontology Translation on the Semantic Web by Dejing Dou, Drew McDermott and Peishen Qi 2003.
Presentation transcript:

Majid Sazvar Knowledge Engineering Research Group Ferdowsi University of Mashhad Semantic Web Reasoning

Semantic Web Reasoning - Majid Sazvar Overview Introduction What is the meaning? Where is meaning in the Semantic Web? Structured Web vs. Reasonable Web SWR Challenges and Solutions Conclusion

Semantic Web Reasoning - Majid Sazvar The WWW is penetrating our society Social contacts (social networking platforms, blogging,...) Economics (buying, selling, advertising,...) Administration (eGovernment) Education (eLearning, Web as information system,...) Work life (information gathering and sharing) Recreation (games, role play,...) Introduction

Semantic Web Reasoning - Majid Sazvar The current Web Immensely successful. – Huge amounts of data. – Syntax standards for transfer of unstructured data. – Machine-processable, human-readable documents. BUT: – Content/knowledge cannot be accessed by machines. – Meaning (semantics) of transferred data is not accessible. Introduction

Semantic Web Reasoning - Majid Sazvar Limitations of the current Web Too much information with too little structure and made for human consumption – Content search is very simplistic – future requires better methods Web content is heterogeneous – in terms of content – in terms of structure – in terms of character encoding future requires intelligent information integration Humans can derive new (implicit) information from given pieces of information but on the current Web we can only deal with syntax – requires automated reasoning techniques Introduction

Semantic Web Reasoning - Majid Sazvar Basic ingredients for the Semantic Web Open Standards for describing information on the Web Methods for obtaining further information from such descriptions Introduction

Semantic Web Reasoning - Majid Sazvar The Semantic Web Layer Cake Introduction

Semantic Web Reasoning - Majid Sazvar Syntax and Semantics Syntax: character strings without meaning Semantics: meaning of the character strings What is the meaning?

Semantic Web Reasoning - Majid Sazvar Semantics of Programming Languages What is the meaning?

Semantic Web Reasoning - Majid Sazvar Semantics of Logic What is the meaning?

Semantic Web Reasoning - Majid Sazvar What Semantics Is Good For Semantic Web requires a shareable, declarative and computable semantics. I.e., the semantics must be a formal entity which is clearly defined and automatically computable. Ontology languages provide this by means of their formal semantics. Semantic Web Semantics is given by a relation – the logical consequence relation. What is the meaning?

Semantic Web Reasoning - Majid Sazvar In other words We capture the meaning of information not by specifying its meaning (which is impossible) but by specifying how information interacts with other information. We describe the meaning indirectly through its effects. What is the meaning?

Semantic Web Reasoning - Majid Sazvar Ontology languages Of central importance for the realisation of Semantic Technologies are suitable representation languages. Meaning (semantics) provided via logic and deduction algorithms (automated reasoning). Scalability is a challenge. Where is meaning in the Semantic Web?

Semantic Web Reasoning - Majid Sazvar Explicit vs. Implicit Knowledge if an RDFS document contains and then is implicitly also the case: it’s a logical consequence. (We can also say it is deduced (deduction) or inferred (inference). We do not have to state this explicitly. Which statements are logical consequences is governed by the formal semantics (covered in the next session). Where is meaning in the Semantic Web?

RDFS Semantic Semantic Web Reasoning - Majid Sazvar Where is meaning in the Semantic Web?

OWL-Horst Semantic Semantic Web Reasoning - Majid Sazvar Where is meaning in the Semantic Web?

OWL-Horst Semantic Semantic Web Reasoning - Majid Sazvar Where is meaning in the Semantic Web?

Semantic Web Reasoning - Majid Sazvar Using implicit knowledge Backward Chaining Method (Goal-Driven) Where is meaning in the Semantic Web?

Semantic Web Reasoning - Majid Sazvar Using implicit knowledge Forward Chaining Method (Data-Driven) Where is meaning in the Semantic Web?

Importance of meaning in the Semantic Web Structured Web + Semantic = Reasonable Web = Semantic Web Semantic Web Reasoning - Majid Sazvar Structured Web vs. Reasonable Web

LOD Cloud Semantic Web Reasoning - Majid Sazvar Structured Web vs. Reasonable Web 19,562,409,691 Triples* * The Semantic Web is Here But we're not ready!?

Semantic Web Reasoning - Majid Sazvar Vastness The World Wide Web contains at least 24 billion pages as of this writing (June 13, 2010). Any automated reasoning system will have to deal with truly huge inputs. Distributed Reasoning is a promising technique. SWR Challenges and Solutions

Semantic Web Reasoning - Majid Sazvar Vagueness These are imprecise concepts like "young" or "tall". This arises from the vagueness of user queries, of concepts represented by content providers, of matching query terms to provider terms and of trying to combine different knowledge bases with overlapping but subtly different concepts. Fuzzy logic is the most common technique for dealing with vagueness. SWR Challenges and Solutions

Semantic Web Reasoning - Majid Sazvar Uncertainty These are precise concepts with uncertain values. For example, a patient might present a set of symptoms which correspond to a number of different distinct diagnoses each with a different probability. Probabilistic reasoning techniques are generally employed to address uncertainty. SWR Challenges and Solutions

Semantic Web Reasoning - Majid Sazvar Inconsistency These are logical contradictions which will inevitably arise during the development of large ontologies, and when ontologies from separate sources are combined. Deductive reasoning fails catastrophically when faced with inconsistency, because "anything follows from a contradiction". Inductive reasoning, defeasible reasoning and paraconsistent reasoning are techniques which can be employed to deal with inconsistency. SWR Challenges and Solutions

Semantic Web Reasoning - Majid Sazvar Privacy Q: Can a reasoner answer queries using hidden knowledge without exposing hidden knowledge? A: Privacy-Preserving Reasoning SWR Challenges and Solutions

Semantic Web Reasoning - Majid Sazvar Deceit This is when the producer of the information is intentionally misleading the consumer of the information. Trust and Cryptography techniques are currently utilized to alleviate this threat. SWR Challenges and Solutions

Semantic Web Reasoning - Majid Sazvar Conclusion We need to move towards the Reasonable Web.

?