Organizing research publications in Web 3 enviroment Anastasiou Lucas Vasilis Tzouvaras Stefanos Kollias NATIONAL TECHNICAL UNIVERSITY OF ATHENS Electrical and Computer Engineering School Department of Computer Science Image, Video and Multimedia Laboratory

Application description  Online reference software  Social bookmarking (e.g. delicious.com)  Semantic content social networking services  Use as collaboration tool  Reasoning services  Quick and sophisticated queries

Web 1.0 ⇒ Web 2.0  Tranformation of web applications Encarta → wikipedia Personal websites → blogs Content management systems → wikis doubleClick → AdSense Directories(taxonomy) → tagging (folksonomies) → syndication (RSS) → user generated content → RIAs → social networks →...  facilitates collaboration, communication, information sharing and interoperability

The 3 core dimensions of Web 2.0

Folksonomy  Social tagging  Classification and annotation of content  «Wisdom of the crowds»

What Web 2.0 is not  Not a new version of internet but a new approach using existing technologies “Web 1.0 was all about connecting people. It was an interactive space, and I think Web 2.0 is, of course, a piece of jargon, nobody even knows what it means. If Web 2.0 for you is blogs and wikis, then that is people to people. But that was what the Web was supposed to be all along.” Tim Berners Lee  Web 2.0 is not Semantic Web

Semantic Web  Today’s web suitable only for human consumption rather than using by machines or web agents  Web 2.0 gives the social aspect of web while semantic web pursues to: Get all information machine-readable. Information should be machine readable and processable by machines-web agents. Organize knowledge in conceptual spaces according to its meaning Use knowledge representation techniques to get implicit knowledge (automated reasoning) from pages content.

Description Logics  Knowledge representation languages  Strict and formal mathematical way to describe a domain, yet simple and elegant  Mathematical background of OWL  Descibes a knowledge domain using atomic concepts C, atomic roles R and individuals I  Constructors {¬, ⊓, ⊔, ∀, ∃, ≥n, ≤n, {},...} to build complex concepts  Terminological axioms

Description Logics - tradeoff  DLs with efficient reasoning algorithms lack modeling power  DLs with high modelling power suffer of high complexity

DL-Lite  Low expressivity capable though to capture conceptual modeling formalisms used in databases and software engineering such as ER and UML class diagrams  Queries run in polynomial time with respect to the size of ABox of Knowledge Base ⇒ tractable  Ideal as conceptual layer to store instances to traditional relational databases ⇒ persistance

Fuzzy-DL-Lite  The fuzzy extension of DL-Lite  Fuzzy assertions 〈 (George,Artificial Intelligence) : isInterestedIn ≥0.9 〉 〈 George : Tall ≥0.7 〉  Fuzzy-DL-Lite reasoner Works great with traditional relational databases (Oracle, mySQL, SQLite, …) Checks consistency of knowledge Features:  Threshold query  Top-k query

Ontology

Adding a new publication  Entering the following title={{From SHIQ and RDF to OWL: The making of a web ontology language}}, author={Horrocks, I. and Patel-Schneider, P.F. and Van Harmelen, F.}, journal={Web semantics: science, services and agents on the World Wide Web}, year={2003}, publisher={Elsevier} } results to creation of following assertions:

Application  Use of inference engine for reasoning  Quick queries through the conceptual layer  Semantic annotation of publications  User generated metadata

Conjunctive Queries  Get all publications which belong on area of Artificial Intelligence by 50%: q(x) ← Publication(x):1.0 ∧ isOnTheArea(x,y):0.5 ∧ Artificial_Intelligence(y):1.0  Get all authors who are interested in Computer Graphics by 50% q(x) ← Authors(x):1.0 ∧ isInterestedIn(x,y):0.5 ∧ Computer_Graphics(y):1.0

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