This keynote will firstly explore the nature of uncertainty, arguing that uncertainty is in fact an umbrella term for a very broad spectrum of phenomena that can be very different in nature. Uncertainty is pervasive in everyday life and humans deal with it in many different ways, often very effectively but sometimes spectacularly unsuccessfully. Some of the methods for dealing with uncertainty, like probability theory, are well understood and have been thoroughly formalized; other methods far less so. Examples of different types of uncertainty and the different ways in which it can best be dealt with will be discussed.

It will then be argued that the best strategy for moving forward with methods to reason with uncertainty on the web is to develop a formal typology of uncertainty, and to catalogue, and if necessary, formalize, the different ways of dealing with it effectively. Each of these can then be investigated in depth to determine how it can best be made machine processable, and ultimately be implemented for the web. Drawing from the speaker’s current work on the Australian Humanities Networked Infrastructure (HuNI) Virtual Lab project, some practical examples of how information systems can represent and process uncertainty will be presented. The importance of provenance and keeping a record of the chain of reasoning will be highlighted.

Affiliations: Intersect Australia, Humanities Networked Infrastructure (HuNI) project, and the Open Knowledge Foundation Australia.

Bio: She has a diverse background in a range of academic disciplines and her commercial IT background encompasses programming, business analysis, data mining and senior level management, as well as project management and co-ordination. Anne has a doctorate in Computer Science from UNSW, sponsored by NICTA and specialising in semantic web technologies. She is on the Program Committee for several semantic Web-related conferences and has a BSc (Hons) in Psychology from the University of Sydney.

This position paper proposes a semantic/fuzzy collaboration method aiming to handle uncertainty in the information retrieval process. This method describes the way to incorporate semantic web technologies and fuzzy set theory in information retrieval system in order to handle uncertainty and to enhance consequently the returned results.

Public governments and authorities are increasingly sharing several sets of Open Government Data. These data often contain information that, in more or less detail, regard private citizens. For this reason, before being published, data are manipulated in order to remove any sensitive information while trying to preserve their reliability. The goal of this paper is to address the lack of tools and procedures aimed at measuring the reliability of these data. We present two procedures for the assessment of the Open Government Data reliability, one based on a comparison between open and closed data, and the other based on analysis of open data only. We evaluate the procedures over data from the data.police.uk website and from the Hampshire Police Constabulary in the United Kingdom. We show how the procedures effectively allow to estimate the reliability of these datasets, and how, actually, the reliability of open data is high even though they are aggregated and smoothed.

The recently introduced Datalog$^\pm$ family of tractable knowledge representation formalisms is able to represent and reason over light-weight ontologies. It extends plain Datalog by negative constraints and the possibility of rules with existential quantification and equality in rule heads, and at the same time restricts the rule syntax by the addition of so-called guards in rule bodies to gain decidability and tractability. In this paper, we investigate how a recently proposed probabilistic extension of Datalog$^\pm$ can be used for representing ontology mappings in typical information integration settings, such as data exchange, data integration, and peer-to-peer integration. The latter one can usually be encountered on the Semantic Web. To allow to reconstruct the history of the mappings in the peer-to-peer integration setting, to detect cycles, and to enable mapping debugging, we also propose to extend it by provenance annotations.

Although several languages have been proposed for dealing with uncertainty in the Semantic Web (SW), almost no support has been given to ontological engineers on how to create such probablistic ontologies (PO). This task of modeling POs has proven to be extremeally difficult and hard to replicate. This paper presents the first tool in the world to implement a process which guides users in modeling POs, the Uncertainty Modeling Process for Semantic Technologies (UMP-ST). The tool solves three main problems: the complexity in creating POs; the difficulty in maintaining and evolving existing POs; and the lack of a centralized tool for documenting POs. Besides presenting the tool, which is implemented as a plug-in for UnBBayes, this papers also presents how the UMP-ST plug-in could have been used to build the Probabilistic Ontology for Procurement Fraud Detection and Prevention in Brazil, a proof-of-concept use case created as part of a research project at the Brazilian Office of the General Comptroller (CGU).

The emergence in the last years of initiatives like the Linked Open Data (LOD) has led to a significant increase in the amount of structured semantic data on the Web. Central role to this development has been played by ontologies, as these enable the representation of real world domains in an explicit and formal way and, thus, the production of commonly understood and shareable semantic data. Nevertheless, the shareability and wider reuse of such data can very often be hampered by the existence of vagueness within it, as this makes the data's meaning less explicit. As a way to reduce this problem we propose in this paper a vagueness metaontology that may be used to represent in an explicit way the nature and characteristics of vague elements within semantic data.

This paper focuses on the incorporation of the Markov Logic Network (MLN) formalism as a plug-in for UnBBayes, a Java framework for probabilistic reasoning based on graphical models. MLN is a formalism for probabilistic reasoning which combines the capacity of dealing with uncertainty tolerating imperfections and contradictory knowledge based a Markov Network (MN) with the expressiveness of First Order Logic. A MLN provides a compact language for specifying very large MNs and the ability to incorporate, in modular form, large domain of knowledge (expressed in First Order Logic sentences) inside itself. A Graphical User Interface for the software Tuffy was implemented into UnBBayes to facilitate the creation, and inference of MLN models. Tuffy is a Java open source MLN engine.

TBD.