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Amol Deshpande (University of Maryland, USA)

 

“PrDB: Managing large-scale correlated probabilistic databases”

 

Increasing numbers of real-world application domains are generating 

data that is inherently noisy, incomplete, and probabilistic in nature.  Statistical inference and probabilistic modeling often introduce another layer of uncertainty on top of that.  Examples of such data include measurement data collected by sensor networks, observation data in the context of social networks, scientific and biomedical data, and data collected by various online cyber-sources.  Over the last few years, numerous approaches have been proposed, and several systems built, to integrate uncertainty into databases. However, these approaches typically make simplistic and restrictive assumptions concerning the types of uncertainties that can be represented. Most importantly, they often make highly restrictive independence assumptions, and cannot easily model rich correlations among the tuples or attribute values. Furthermore, they typically lack support for specifying uncertainties at different levels of abstractions, needed to handle large-scale uncertain datasets.

In this talk, I will begin by presenting our work on building a probabilistic data management system, called PrDB, aimed at supporting rich correlation structures often present in real-world uncertain datasets. I will present the PrDB representation model, which is based on probabilistic graphical models, and its key abstractions, and show how these enable PrDB to support uncertainties specified at various abstraction levels, from schema-level uncertainties that apply to entire relations to tuple-specific uncertainties that apply only to a specific tuple or a specific set of tuples. Query evaluation in PrDB can be seen as equivalent to inference in graphical models, and I will present some of the key novel techniques that we have developed to efficiently evaluate various types of queries over large-scale probabilistic databases. I will then briefly discuss our ongoing work and some of the open research challenges in this area.

 

 

 

Thomas Lukasiewicz (Oxford University, UK; TU Vienna, Austria)

 

“Uncertainty in the Semantic Web”

 

The Semantic Web (SW) has recently attracted much attention, both from academia and industry, and is widely regarded as the next step in the evolution of the World Wide Web. It aims at an extension of the current Web by standards and technologies that help machines to understand the information on the Web so that they can support richer discovery, data integration, navigation, and automation of tasks. The main ideas behind the SW are to add a machine-understandable "meaning" to Web pages, to use ontologies for a precise definition of shared terms in Web resources, to use KR technology for automated reasoning from Web resources, and to apply cooperative agent technology for processing the information of the Web. The SW consists of several hierarchical layers, where the Ontology layer, in form of the OWL Web Ontology Language, is currently the highest layer of sufficient maturity. On top of and in integration with the Ontology layer, sophisticated representation and reasoning capabilities for the Rules, Logic, and Proof layers of the SW are currently being developed next. Managing uncertainty and/or vagueness is another area, which has started to play an important role in research towards the SW. There are a plethora of applications on the Web, which require the management of uncertain and/or vague information. Some of the most prominent technologies for dealing with uncertainty are probably the ranking algorithms standing behind Web search engines. More generally, formalisms for dealing with uncertainty and/or vagueness are especially applied in ontology mapping, data integration, information retrieval, and database querying. Vagueness and imprecision also abound in multimedia information processing and retrieval, and are an important aspect of natural language interfaces to the Web.

In this talk, I will first give an overview of important applications on the Web that require the management of uncertainty and/or vagueness. I will then describe some own representation and reasoning formalisms for the SW, which extend ontology languages as well as hybrid languages integrating rules and ontologies by the ability to handle uncertainty and/or vagueness.

 

 

 

 


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