An Overview of Bayesian Network Applications in Uncertain Domains <s> VII. BAYESIAN NETWORKS APPLICATIONS MISCELLANEOUS DOMAINS <s> The concept of ontology modeling and engineering has drastically evolved with the emergence of semantic web. Today in a collaborative environment ontologies need to interoperate for efficient data exchange. Due to their deterministic nature, ontologies are unable to capture uncertainty inherent in many real world problems. Efforts have been made to add the dimension of uncertainty by synergizing Bayesian network with ontology - a combination in which domain knowledge and probabilistic information go hand in hand. This paper categorizes different approaches that have been proposed to blend ontology and Bayesian networks. It also discusses the scope of these approaches and their intended application areas. Their relative strengths and weaknesses are also highlighted. The paper contributes significantly by providing a comprehensive comparison which could serve as the starting point for new researchers. <s> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VII. BAYESIAN NETWORKS APPLICATIONS MISCELLANEOUS DOMAINS <s> In this paper, a new method is presented for the recognition of online forms filled manually by a digital-type clip. This writing process is not very restrictive but it is only sending electronic ink without the pre-printed form, which will require to undertake field recognition without context. To identify the form model of filled fields, we propose a method based on Bayesian networks. The networks use the conditional probabilities between fields in order to infer the real structure. We associate multiple Bayesian networks for different structures levels (i.e. sub-structures) and test different algorithms for form structure learning. The experiments were conducted on the basis of 3200 forms provided by the Actimage company, specialist in interactive writing processes. The first results show a recognition rate reaching more than 97%. <s> BIB002 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VII. BAYESIAN NETWORKS APPLICATIONS MISCELLANEOUS DOMAINS <s> Understanding functional relationships within ecological networks can help reveal keys to ecosystem stability or fragility. Revealing these relationships is complicated by the difficulties of isolating variables or performing experimental manipulations within a natural ecosystem, and thus inferences are often made by matching models to observational data. Such models, however, require assumptions—or detailed measurements—of parameters such as birth and death rate, encounter frequency, territorial exclusion, and predation success. Here, we evaluate the use of a Bayesian network inference algorithm, which can reveal ecological networks based upon species and habitat abundance alone. We test the algorithm's performance and applicability on observational data of avian communities and habitat in the Peak District National Park, United Kingdom. The resulting networks correctly reveal known relationships among habitat types and known interspecific relationships. In addition, the networks produced novel insights into ecosystem structure and identified key species with high connectivity. Thus, Bayesian networks show potential for becoming a valuable tool in ecosystem analysis. <s> BIB003 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VII. BAYESIAN NETWORKS APPLICATIONS MISCELLANEOUS DOMAINS <s> Bayesian networks are graphical modeling tools that have been proven very powerful in a variety of application contexts. The purpose of this paper is to provide education practitioners with the background and examples needed to understand Bayesian networks and use them to design and implement student models. The student model is the key component of any adaptive tutoring system, as it stores all the information about the student (for example, knowledge, interest, learning styles, etc.) so the tutoring system can use this information to provide personalized instruction. Basic and advanced concepts and techniques are introduced and applied in the context of typical student modeling problems. A repertoire of models of varying complexity is discussed. To illustrate the proposed methodology a Bayesian Student Model for the Simplex algorithm is developed. <s> BIB004 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VII. BAYESIAN NETWORKS APPLICATIONS MISCELLANEOUS DOMAINS <s> Current approaches to instruction cache analysis for determining worst-case execution time rely on building a mathematical model of the cache that tracks its contents at all points in the program. This requires perfect knowledge of the functional behaviour of the cache and may result in extreme complexity and pessimism if many alternative paths through code sections are possible. To overcome these issues, this paper proposes a new hybrid approach in which information obtained from program traces is used to automate the construction of a model of how the cache is used. The resulting model involves the learning of a Bayesian network that predicts which instructions result in cache misses as a function of previously taken paths. The model can then be utilised to predict cache misses for previously unseen inputs and paths. The accuracy of this learned model is assessed against real benchmarks and an established statistical approach to illustrate its benefits. <s> BIB005 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VII. BAYESIAN NETWORKS APPLICATIONS MISCELLANEOUS DOMAINS <s> Existing methods for the semantic analysis of multimedia, although effective for single-medium scenarios, are inherently flawed in cases where knowledge is spread over different media types. In this work we implement a cross media analysis scheme that takes advantage of both visual and textual information for detecting high-level concepts. The novel aspect of this scheme is the definition and use of a conceptual space where information originating from heterogeneous media types can be meaningfully combined and facilitate analysis decisions. More specifically, our contribution is on proposing a modeling approach for Bayesian Networks that defines this conceptual space and allows evidence originating from the domain knowledge, the application context and different content modalities to support or disproof a certain hypothesis. Using this scheme we have performed experiments on a set of 162 compound documents taken from the domain of car manufacturing industry and 118581 video shots taken from the TRECVID2010 competition. The obtained results have shown that the proposed modeling approach exploits the complementary effect of evidence extracted across different media and delivers performance improvements compared to the single-medium cases. Moreover, by comparing the performance of the proposed approach with an approach using Support Vector Machines (SVM), we have verified that in a cross media setting the use of generative rather than discriminative models are more suited, mainly due to their ability to smoothly incorporate explicit knowledge and learn from a few examples. <s> BIB006
In this section, additional but dissimilar BN applied domains are reviewed. The reviewed literature provides an overview on a variety of BN applied domains. In ontology enhancement, Larik et al. BIB001 proposed a blend of ontology and BN approaches in order to classify various methods according to their application. Moreover, the strengths and weaknesses of these methods are identified comprehensively. The open source tool used for ontologies and BN, such as Protѐgѐ and UnBBayes, are utilized for validation purposes. Similarly, Matlab Toolbox  is used by Phillippot et al. BIB002 to construct BN. The aim of constructing BN structure was to classify the manually filled form fields by digital pen. For this purpose, Maximum Weight Spanning Tree (MWST) algorithm is more efficient, which diverted the focus from BN to MWST as well as more dependency on manual intervention. Milns et al. BIB003 and Bartlett et al. BIB005 presented their BN based ecological network and probabilistically cache analysis respectively. The prior method focused to analyze spatial data with massive data preparation requirement and the latter one automates cache use on program traces. Lakka et al. BIB006 used BN modeling approach for cross media analysis. The objective is to enhance the efficient cross media by smooth adaptation of explicit knowledge. However, few media types such as text and images are ignored. An adaptive tutoring system is developed by Millan et al. BIB004 to endow with a background to education practitioners. The adaptive tutoring system as a student engineering model, target variable lost its generality with the use of knowledge feature.