There is a growing need for scalable semantic web repositories which support inference and provide efficient queries. There is also a growing interest in representing uncertain knowledge in semantic web datasets and ontologies. In this paper, I present a bit vector schema specifically designed for RDF (Resource Description Framework) datasets. I propose a system for materializing and storing inferred knowledge using this schema. I show experimental results that demonstrate that this solution simplifies inference queries and drastically improves results. I also propose and describe a solution for materializing and persisting uncertain information and probabilities. Thresholds and bit vectors are used to provide efficient query access to this uncertain knowledge. My goal is to provide a semantic web repository that supports knowledge inference, uncertainty reasoning, and Bayesian networks, without sacrificing performance or scalability.

The objective of my doctoral research is bring together two fields: partially-observable reinforcement learning (PORL) and non-parametric Bayesian statistics (NPB) to address issues of statistical modeling and decision-making in complex, real-world domains.

In recent years Bayesian networks have been used in many fields, from Online Analytical Processing (OLAP) performance enhancement (Margaritis 2003) to medical service performance analysis (Acid et al. 2004), gene expression analysis (Friedman et al. 2000), breast cancer prognosis and epidemiology (Holmes and Jain 2008). The high dimensionality of the data sets common in these domains have led to the development of several learning algorithms focused on reducing computational complexity while still learning the correct network. Some examples are the Grow-Shrink algorithm in Margaritis (2003), the Incremental Association algorithm and its derivatives in Tsamardinos et al. (2003) and in Yaramakala and Margaritis (2005), the Sparse Candidate algorithm in Friedman et al. (1999), the Optimal Reinsertion in Moore and Wong (2003) and the Greedy Equivalent Search in Chickering (2002). The aim of the bnlearn package is to provide a free implementation of some of these structure learning algorithms along with the conditional independence tests and network scores used 2 Learning Bayesian Networks with the bnlearn R Package to construct the Bayesian network. Both discrete and continuous data are supported. Furthermore, the learning algorithms can be chosen separately from the statistical criterion they are based on (which is usually not possible in the reference implementation provided by the algorithms' authors), so that the best combination for the data at hand can be used.

Lifting can greatly reduce the cost of inference on first-order probabilistic graphical models, but constructing the lifted network can itself be quite costly. In online applications (e.g., video segmentation) repeatedly constructing the lifted network for each new inference can be extremely wasteful, because the evidence typically changes little from one inference to the next. The same is true in many other problems that require repeated inference, like utility maximization, MAP inference, interactive inference, parameter and structure learning, etc. In this paper, we propose an efficient algorithm for updating the structure of an existing lifted network with incremental changes to the evidence. This allows us to construct the lifted network once for the initial inference problem, and amortize the cost over the subsequent problems. Experiments on video segmentation and viral marketing problems show that the algorithm greatly reduces the cost of inference without affecting the quality of the solutions.

BLOG is a powerful language to express models with an unknown number of objects and identity uncertainty. Current inference engines for BLOG are either too slow or require users to write a model-specific proposal distribution. We describe here, ongoing work to design a new, fast, generic inference engine for BLOG called blogc. The new implementation uses Gibbs sampling for finite-valued variables and performs an analysis of the model to generate customized sampling code in C. We describe our algorithms and methods in the context of various commonly used models and demonstrate significant performance improvement.

The overall goal of our research is the automatic recognition of the intended message of a grouped bar chart. This paper presents our preliminary work on a system that utilizes the communicative signals in a grouped bar chart as evidence in a Bayesian network that hypothesizes the primary message conveyed by the graphic. The paper discusses the kinds of communicative signals present in grouped bar charts and an ACT-R model for computationalizing one important communicative signal, the relative effort involved in performing the perceptual tasks necessary for the recognition. It also describes our Bayesian network and its implementation on a subset of the kinds of messages that can be conveyed by grouped bar charts.

Markov logic networks (MLNs) use first-order formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (4-5 literals) due to extreme computational costs, and thus are unable to represent complex regularities in data. To address this problem, we present LSM, the first MLN structure learner capable of efficiently and accurately learning long clauses. LSM is based on the observation that relational data typically contains patterns that are variations of the same structural motifs. By constraining the search for clauses to occur within motifs, LSM can greatly speed up the search and thereby reduce the cost of finding long clauses. LSM uses random walks to identify densely connected objects in data, and groups them and their associated relations into a motif. Our experiments on three real-world datasets show that our approach is 2-5 orders of magnitude faster than the state-of-the-art ones, while achieving the same or better predictive performance.

The paper argues that (1) stochastic planning should be used as a core problem domain for relational probabilistic models providing problems of interest that are challenging for current approaches and significant scope for extending their capabilities, (2) that symbolic dynamic programming solving such problems can be seen as a prime example of lifted inference in relational probabilistic problems, (3) that first order decision diagrams provide a useful tool to drive such lifted computations, and (4) that the resulting lifted inference is qualitatively different from what other approaches are providing. As a result, this relationship can be studied to the benefit of developing foundations for relational probabilistic models and to the benefit of stochastic planning.

Much has been achieved in the field of AI, yet much remains Gibbs sampling code in C/C . Chechetka et al. investigate relational learning for collective classification of entities to be done if we are to reach the goals we all imagine. in images. Choi et al. present a lifted inference One of the key challenges with moving ahead is closing approach for relational continuous models. Logical AI has Gogate and Domingos shows how to exploit logical structure mainly focused on complex representations, and statistical in lifted probabilistic inference. Hadiji et al. discuss AI on uncertainty.

Providing energy conservation services becomes a hot research topic because more and more people attach importance to environmental protection. This research proposes a framework that consists of four process models: appliance recognition, activity-appliances model, unattended appliances detection, and energy conservation service. Appliance recognition model can recognizes the operating states of appliances from raw sensing data of electric power. An activity-appliances model has been built to associate activities with appliances according to the data of Open Mind Common Sense Project. Using the relationship between activities can help to detect unattended appliances, which are consuming electric power but not take part in the resident’s activities. After obtain information of appliance operating states and unattended appliances, residents can receive energy conservation services for notifying the energy consumption information. Finally, the experimental results show that dynamic Baysian network approach can achieve higher than 92% accuracy for appliance recognition. Data of activity-appliances model shows most appliances are strong activity-related.