ARD PrasadIndian Statistical Institute, Bangalore

Università Degli Studi Di Trento - ItalyYahoo!, SpainSORA, AustriaConsorzio Nazionale Interuniversitario Per Le Telecomunicazioni, ItalyEuropean Archive –FranceUniversità Degli Studi Di Pavia – ItalyUniversity of Southampton, United KindomIndian Statistical Institute, IndiaGottfried Wilhelm Leibniz Universitaet, Germany.Max Planck Gesellschaft zur Foerderung der Wissenschaften – Germany.

LK Project Partners

Diversity is the “key”Diversity is the “key”

Knowledge and its Knowledge and its articulations are articulations are strongly influenced strongly influenced by by diversitydiversity in in

Diversity is the “key”Diversity is the “key”

Its an unavoidable Its an unavoidable and intrinsic and intrinsic property of property of knowledge because knowledge because of of

VisionVision

The vision is to consider diversity an asset and to make it traceable, understandable and exploitable.

With the goal to improve navigation and search in very large multimodal datasets (e.g., the Web itself).

VisionVision 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.

InterdisciplinarityInterdisciplinarity

Philosophy of Science, Cognitive Science, Library Science, Semiotics, etc.

Two Main Two Main PillarsPillars

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).

ContextContext

The Future PredictorThe 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.

Scientific & Technological Scientific & Technological Challenges (1)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,

Scientific & Technological Scientific & Technological Challenges (2)Challenges (2)

Developing an interdisciplinary foundation for dealing systematically with diversity and its impact in search and retrieval of information.

Scientific & Technological Scientific & Technological Challenges (3)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.

Scientific & Technological Scientific & Technological Challenges (4)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.

Scientific & Technological Scientific & Technological Challenges (5)Challenges (5)

a new generation of search technology that supports the opinion-aware, diversity-aware and time-aware aggregation and exploration of knowledge.

Expected Results (1)Expected Results (1)

extraction of facts extraction of facts and entities from and entities from web pages and web pages and documents; opinion documents; opinion mining; integration mining; integration of related and of related and complementary complementary knowledge knowledge fragments obtained fragments obtained from different from different sources;sources;

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

Expected ResultsExpected Results

Information Information aggregation ,summariaggregation ,summarization and diversity-zation and diversity-aware search resultsaware search results

Subject Partners (1)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”.

Subject Subject Partners (2)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.

Progress Beyond The ”state-of Progress Beyond The ”state-of the-art” (1)the-art” (1)

Foundations of Foundations of Evolution, Diversity Evolution, Diversity and Bias in and Bias in KnowledgeKnowledge

Progress Beyond The ”state-of Progress Beyond The ”state-of the-art” (2)the-art” (2)

Fact and Opinion Fact and Opinion ExtractionExtraction

Progress Beyond The ”state-of Progress Beyond The ”state-of the-art” (3)the-art” (3)

Knowledge EvolutionKnowledge Evolution

Progress Beyond The ”state-of Progress Beyond The ”state-of the-art” (4)the-art” (4)

Bias and Diversity Bias and Diversity HandlingHandling

Progress Beyond The ”state-of Progress Beyond The ”state-of the-art” (5)the-art” (5)

•Advanced Clustering Advanced Clustering & Aggregation& Aggregation

•Enhanced Search & Enhanced Search & Retrieval Retrieval

Innovation Roles

Thanks toYou allIIPA andIBM