For diversity and novelty evaluation in information retrieval, we expect that the novel documents are always ranked higher than the redundant ones and the relevant ones higher than the irrelevant ones. We also expect that the level of novelty and relevancy should be acknowledged. Accordingly, we expect that the evaluation algorithm would reward rankings that respect these expectations. Nevertheless, there are few research articles in the literature that study how to meet such expectations, even fewer in the field of biomedical information retrieval. In this article, we propose a new metric for novelty and relevancy evaluation in biomedical information retrieval based on an aspect‐level performance measure introduced by TREC Genomics Track with formal results to show that those expectations above can be respected under ideal conditions. The empirical evaluation indicates that the proposed metric,geNov, is greatly sensitive to the desired characteristics above, and the three parameters are highly tuneable for different evaluation preferences. By experimentally comparing with state‐of‐the‐art metrics for novelty and diversity, the proposed metric shows its advantages in recognizing the ranking quality in terms of novelty, redundancy, relevancy, and irrelevancy and in its discriminative power. Experiments reveal the proposed metric is faster to compute than state‐of‐the‐art metrics.

Nouns are more important than other parts of speech in information retrieval and are more often found near the beginning or the end of sentences. In this paper, we investigate the effects of rewarding terms based on their location in sentences on information retrieval. Particularly, we propose a novel Term Location (TEL) retrieval model based on BM25 to enhance probabilistic information retrieval, where a kernel-based method is used to capture term placement patterns. Experiments on five TREC datasets of varied size and content indicate the proposed model significantly outperforms the optimized BM25 and DirichletLM in MAP over all datasets with all kernel functions, and excels the optimized BM25 and DirichletLM over most of the datasets in [email protected] and [email protected] with different kernel functions.

In information retrieval, we are interested in the information that is not only relevant but also novel. In this paper, we study how to boost novelty for biomedical information retrieval through probabilistic latent semantic analysis. We conduct the study based on TREC Genomics Track data. In TREC Genomics Track, each topic is considered to have an arbitrary number of aspects, and the novelty of a piece of information retrieved, called a passage, is assessed based on the amount of new aspects it contains. In particular, the aspect performance of a ranked list is rewarded by the number of new aspects reached at each rank and penalized by the amount of irrelevant passages that are rated higher than the novel ones. Therefore, to improve aspect performance, we should reach as many aspects as possible and as early as possible. In this paper, we make a preliminary study on how probabilistic latent semantic analysis can help capture different aspects of a ranked list, and improve its performance by re-ranking. Experiments indicate that the proposed approach can greatly improve the aspect-level performance over baseline algorithm Okapi BM25.

Multiply sectioned Bayesian networks (MSBNs) support multi‐agent probabilistic inference in distributed large problem domains, where agents (subdomains) are organized by a tree structure (called hypertree). In earlier work, all belief updating methods on a hypertree are made of two rounds of propagation, each of which is implemented as a recursive process. Both processes need to be started from the same designated (root) hypernode. Agents perform local belief updating at most in a partial parallel manner. Such methods may not be suitable for practical multi‐agent environments because they are easy to crush for the problems happened in communication or local belief updating. In this paper, we present a fault‐tolerant belief updating method for multi‐agent probabilistic inference. In this method, multiple agents concurrently perform exact belief updating in a complete parallel. Temporary problems happened from time to time at some agents or some communication channels would not prevent agents from eventually converging to the correct beliefs. Permanently disconnected communication channels would not keep the properly connected portions of the system from appropriately finishing their belief updating within portions. Compared to the previous traversal‐based belief updating, the proposed approach is not only fault‐tolerant but also robust and scalable.

Abstract Multiply sectioned Bayesian networks (MSBNs) support multiagent probabilistic inference in distributed large problem domains. Inference with MSBNs can be performed using their compiled representations. The compilation involves moralization and triangulation of a set of local graphical structures. Privacy of agents may prevent us from compiling MSBNs at a central location. In earlier work, agents performed compilation sequentially via a depth‐first traversal of the hypertree that organizes local subnets, where communication failure between any two agents would crush the whole work. In this paper, we present an asynchronous compilation method by which multiple agents compile MSBNs in full parallel. Compared with the traversal compilation, the asynchronous one is robust, self‐adaptive, and fault‐tolerant. Experiments show that both methods provide similar quality compilation to simple MSBNs, but the asynchronous one provides much higher quality compilation to complex MSBNs. Empirical study also indicates that the asynchronous one is consistently faster than the traversal one. Copyright © 2009 John Wiley & Sons, Ltd.

Concerns for personal information privacy could be produced during information collection, transmission and handling. In information handling, privacy could be compromised from both inside and outside of organizations. Within an organization, private data are generally protected by organizations' privacy policies and the corresponding platforms for privacy practices. However, private data could still be misused intentionally or unintentionally by individuals who have legitimate accesses to them. In general, activities of a database operator form a stochastic process, and at different time, privacy intrusion behavior may show different features. In particular, one's past activities can help determine the natures of his/her current practices. In this paper, we propose to use dynamic Bayesian networks to model such temporal environments and detect any privacy intrusions happened within them.

Many organizations need to maintain a lot of private data to run their businesses. Private data could be violated by both the inside and the outside intruders. In this paper, we propose a probabilistic method to detect insider privacy intrusion in database systems

In pervasive (ubiquitous) environments, context-aware agents are used to obtain, understand, and share local contexts with each other so that the environments could be integrated seamlessly. Context sharing among agents should be made privacy-conscious. Privacy preferences are generally specified to regulate the exchange of the contexts, where who have rights under what conditions to have what contexts are designated. However, released contexts could be used to infer those unreleased. In particular, different contexts released could endanger the security of different contexts unreleased. The existing privacy preference specification platforms do not have a mechanism to prevent inference. To date, there have been very few inference control mechanisms specifically tailored to context management in pervasive (ubiquitous) environments. A Bayesian network based mechanism has been proposed to prevent privacy-sensitive contexts from being inferred from those to be released. Nevertheless, contexts in pervasive (ubiquitous) environments could change from time to time and are history dependent. In this paper, we propose to use dynamic Bayesian networks to track the most updated beliefs of the adversaries about the dynamic domains in order to evaluate which contexts in the domains could be released safely in various situations.

Contexts in ubiquitous environments, either sensed or interpreted, are usually ambiguous. However, to provide context-aware services and applications, agents in the environments need to have an as clear as possible understanding of their contexts. Ambiguous contexts can be made clearer by agents using inference based on their domain knowledge, local and global evidence. Bayesian networks have been proposed to represent and reason about uncertain contexts under the single agent paradigm. In distributed multiagent systems, multiply sectioned Bayesian networks (MSBNs) provide a coherent framework for distributed multiagent probabilistic inference, where agents' privacy is respected. In this paper, we propose to apply MSBNs to uncertain contexts representation and reasoning in ubiquitous environments