experiences and issues

1. Semantic Technologies at work Experiences and issues A talk by Yannis Kalfoglou at Date of presentation here

6. 13/14 Semantic technologies  Experiences - MyPlanet Experiences – CAS Experiences – e-Response Issues MyPlanet: a personalised ontology-driven front-end to KMi Planet Java Applet run on any Java-enabled Web browser accessible from http://eldora.open.ac.uk/my-planet/

7. MyPlanet: the User Interface and email address Browse ontology-drawn hierarchy Display ontology-drawn information for the selected node Edit selections Control buttons Password-protected access Semantic technologies  Experiences - MyPlanet Experiences – CAS Experiences – e-Response Issues User enters account details

8. MyPlanet: ontology-drawn information Ontology-drawn hierarchy class: kmi-research-area class: kmi-research-theme class: organization class: project class: kmi-member class: kmi-technology class: application-domain User selects: Enrico Motta Instance of class: kmi-senior-research-fellow Local slots: has-job-title involved-in-projects Semantic technologies  Experiences - MyPlanet Experiences – CAS Experiences – e-Response Issues

9. MyPlanet: finding relevant e-Stories Standard “string-matching” Ontology-based reasoning: use of ontological relations that hold for the particular entity: kmi-member involved-in-projects Υ do string-matching on fire ontological relations that hold for Y: has-research-area Z do string-matching on … and so on. Y Z Z Semantic technologies  Experiences - MyPlanet Experiences – CAS Experiences – e-Response Issues

10. MyPlanet: using cue phrases to increase the answer set To increase the number of possible matches, we employ the notion of cue phrases which we associate with ontology instances: genetic-algorithms , instance of class: kmi-research-area is associated with cue phrases: evolutionary computing, evolutionary algorithms Hence, we do string-matching on those phrases: X has-cue-phrases Y do string-matching on hence, this story is related to X (genetic algorithms) Y Semantic technologies  Experiences - MyPlanet Experiences – CAS Experiences – e-Response Issues

12. Experiences 15/18 Semantic technologies  Experiences – MyPlanet  Experiences – CAS Experiences – e-Response Issues

16. Semantic Technologies maturity The Gartner Hype Cycle (7/06) – time to “plateau of productivity” (Public) Semantic Web: trough of disillusionment  5-10y (Corporate) Semantic Web: peak of inflated expectations  5-10y Web 2.0: peak of inflated expectations  2-5y Web 2.0 More visible Large end user base (Public) Semantic Web Invisible to the end user Infrastructure (Corporate) Semantic Web Better information management Visibility to end user is not an issue 7/18 Semantic technologies  Experiences – MyPlanet  Experiences – CAS  Experiences – e-Response  Issues

19. Knowledge society an example case Social media No moderation or censorship; two-way communication Individuals’ contributions clearly acknowledged; anonymity discouraged Spin and attempt to control are discouraged Pull system – let people bring to them the content and relationships they want Highly distributed, not centralised Adopted from Dion Hinchcliffe’s web site Participation powered by the network effect Low cost People in charge (use and control) Benefit of global scale syndication Democratisation and change of ground rules Shift from institutional control to consumer control 9/18 Semantic technologies  Experiences – MyPlanet  Experiences – CAS  Experiences – e-Response  Issues

21. A talk delivered by Yannis Kalfoglou at Semantic Technologies at work Experiences and issues Detica presentation date here Thank you for listening