1. ARD Prasad Indian Statistical Institute, Bangalore

2. Università Degli Studi Di Trento - Italy Yahoo!, Spain SORA, Austria Consorzio Nazionale Interuniversitario Per Le Telecomunicazioni, Italy European Archive –France Università Degli Studi Di Pavia – Italy University of Southampton, United Kindom Indian Statistical Institute, India Gottfried Wilhelm Leibniz Universitaet, Germany. Max Planck Gesellschaft zur Foerderung der Wissenschaften – Germany. LK Project Partners

3. Diversity is the “key” Knowledge and its articulations are strongly influenced by diversity in

4. Diversity is the “key” Its an unavoidable and intrinsic property of knowledge because of

5. Vision  The vision is to consider diversity an asset and to make itdiversity  traceable,  understandable and  exploitable.  With the goal to improve navigation and search in very large multimodal datasets (e.g., the Web itself). the Web

6. Vision  The project will study the effect of diversity and time on opinions and bias and envisage a future where search and navigation tools (e.g., search engines) will automatically classify and organize opinions and bias (about, e.g., global warming or the Olympic games in China) and, therefore, will produce more insightful, better organized, easier-to- understand output.diversitytimeopinionsbiasopinionsbias

7. Interdisciplinarity  Philosophy of Science,  Cognitive Science,  Library Science,  Semiotics, etc.

8. Two Main Pillars The proposed solution is based on the foundational notions of context and its ability to localize meaning, and the notion of facet, as from library science, and its ability to organize knowledge as a set of interoperable components (i.e., facets). Context

9. The Future Predictor  will combine and test all methods necessary to answer factual queries regarding future events and statements, based on information available already on the Web.

10. Scientific & Technological Challenges (1) ‏  Studying knowledge sources and its effects by combining know-how and experiences from areas such as media research, multimodal information theory, information and library science, natural language processing and multimedia data analysis,

11. Scientific & Technological Challenges (2) ‏  Developing an interdisciplinary foundation for dealing systematically with diversity and its impact in search and retrieval of information.

12. Scientific & Technological Challenges (3) ‏  Detecting bias in text and in the use of multimedia as a reflection of the diversity as well as for analysing and tracing the underlying diversity, lineage and the bias and trustworthiness of sources.

13. Scientific & Technological Challenges (4) ‏  Developing methods for analysing the temporal binding of facts and opinions as well as the evolution of knowledge - considering evolution in articulated facts as well as evolution in the means for knowledge articulation and structuring.

14. Scientific & Technological Challenges (5) ‏  a new generation of search technology that supports the opinion-aware, diversity-aware and time-aware aggregation and exploration of knowledge.

15. Expected Results (1) ‏ extraction of facts and entities from web pages and documents; opinion mining; integration of related and complementary knowledge fragments obtained from different sources;

16. Expected Results ‏ analysis of the evolution of classification patterns and hierarchies; opinion evolution; diversity-aware knowledge representation; algorithms for taking evolution of knowledge into account for retrieval and clustering of information

17. Expected Results Information aggregation,summarization and diversity-aware search results

18. Subject Partners (1) ‏ Semiotics will allow for the definition of modal approaches to the discovery of knowledge diversity and of how web components and multimedia data can be used to express opinions and bias and will also help to understand “context”.

19. Subject Partners (2) ‏ Library science will provide the foundations and experience needed to organize information in categories and to realize innovative mechanisms for indexing hierarchical categorisation schemes with meaningful concept sequences, i.e., Facets.

20. Progress Beyond The ”state-of the- art” (1) ‏ Foundations of Evolution, Diversity and Bias in Knowledge

21. Progress Beyond The ”state-of the- art” (2) ‏ Fact and Opinion Extraction

22. Progress Beyond The ”state-of the- art” (3) ‏ Knowledge Evolution

23. Progress Beyond The ”state-of the- art” (4) ‏ Bias and Diversity Handling

24. Progress Beyond The ”state-of the- art” (5) ‏ Advanced Clustering & Aggregation Advanced Clustering & Aggregation Enhanced Search & Retrieval Enhanced Search & Retrieval

26. Innovation Roles

27. Thanks to You all IIPA and IBM