Information retrieved from open distributed systems can be of uncertain reliability and sifting through a large amount of data can be a complex procedure. In real life, we handle such problems through the social mechanisms of trust and word-of-mouth, or reputation. We propose a sociologically motivated trust and reputation model for the problem of reliable information retrieval and present an e...

Purpose In any information security risk assessment, vulnerabilities are usually identified by information-gathering techniques. However, vulnerability identification errors wrongly identified or unidentified vulnerabilities can occur as uncertain data are used. Furthermore, businesses’ security needs are not considered sufficiently. Hence, security functions may not protect business assets suf...

We present two new approaches to the problem of integrating information retrieval (IR) and database (DB) systems. On the logical level, IR is based on uncertain inference, which is a generalization to the certain inference process employed in DB systems. As an implementation of this concept, we present a probabilistic relational algebra. On the conceptual level, we distinguish between the logic...

In this paper, an introduction and survey over probabilistic information retrieval (IR) is given. First, the basic concepts of this approach are described: the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions; a conceptual model for IR along with the corresponding event space clarify the interpretation of the probabilistic parameters i...

Learning from uncertain data has been drawing increasing attention in recent years. In this paper, we propose a tree induction approach which can not only handle uncertain data, but also furthermore reduce epistemic uncertainty by querying the most valuable uncertain instances within the learning procedure. We extend classical decision trees to the framework of belief functions to deal with a v...

In many real-world situations, the data cannot be assumed to be precise. Indeed uncertain data are often encountered, due for example to the imprecision of measurement devices or to continuously moving objects for which the exact position is impossible to obtain. One way to model this uncertainty is to represent each data value as a probability distribution function; recent works show that adeq...

Retrieval models form the theoretical basis for computing the answer to a query. They differ not only in the syntax and expressiveness of the query language, but also in the representation of the documents. Following Rijsbergen’s approach of regarding IR as uncertain inference, we can distinguish models according to the expressiveness of the underlying logic and the way uncertainty is handled. ...

This paper compares two formalisms for uncertain inference, Kyburg's Combinatorial Semantics and Dempster-Shafer belief function theory, on the basis of an example from the domain of medical diagnosis. I review Shafer's example about the imaginary disease ploxoma and show how it would be represented in Combinatorial Semantics. I conclude that belief function theory has a qualitative advantage b...

Shannnon entropy is an efficient tool to measure uncertain information. However, it cannot handle the more uncertain situation when the uncertainty is represented by basic probability assignment (BPA), instead of probability distribution, under the framework of Dempster Shafer evidence theory. To address this issue, a new entropy, named as Deng entropy, is proposed. The proposed Deng entropy is...

Resolving domain incompatibility among independently developed databases often involves uncertain information. DeMichiel 5] showed that uncertain information can be generated by the mapping of connicting attributes to a common domain, based on some domain knowledge. In this paper, we show that uncertain information can also arise when the database integration process requires information not di...