http://people.csail.mit.edu/lpk/papers/aij98-pomdp.pdf

Planning and Acting in Partially Observable Stochastic Domains

This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms. We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient. Inference in the simpified model provides bounds on probabilities of interest in the original model. We describe a general framework for generating variational transformations based on convex duality. Finally we return to the examples and demonstrate how variational algorithms can be formulated in each case.

/pdf/an-introduction-to-variational-methods-for-graphical-models-4o3vosl5nw.pdf

An introduction to variational methods for graphical models

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

Data fusion

In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for defining the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it gives us little or no additional information beyond that subsumed by the remaining features. In particular, this will be the case for both irrelevant and redundant features. We then give an efficient algorithm for feature selection which computes an approximation to the optimal feature selection criterion. The conditions under which the approximate algorithm is successful are examined. Empirical results are given on a number of data sets, showing that the algorithm effectively handles datasets with a very large number of features.

Toward optimal feature selection

Semi-structured queries in an information page employing semi-structured data are pre-processed to identify the data to be selectively retrieved from the semi-structured data, and the storage locations of the data in a collection of structured data comprising the semi-structured data. The pre-processing references a description of how the semi-structured data are stored in the collection of structured data. Equivalent structured queries to retrieve the data from the collection of structured data are generated; and the semi-structured queries are replaced with the equivalent structured queries. In one application, these pre-processed information pages are stored to fulfill requests from remote clients, to enhance response time to requests. In response to a request, the requested information page is dynamically completed, executing the replacement equivalent structured queries instead, without having to determine in real time where the semi-structured data are stored in the structured organization.

Method and apparatus for generating information pages using semi-structured data stored in a structured manner

An Approach to Planning with Incomplete Information.

Most AI representations and algorithms for plan generation have not included the concept of information-producing actions (also called diagnostics, or tests, in the decision making literature). We present a planning representation and algorithm that models information-producing actions and constructs plans that exploit the information produced by those actions. We extend the BURIDAN (Kushmerick et al. 1994) probabilistic planning algorithm, adapting the action representation to model the behavior of imperfect sensors, and combine it with a framework for contingent action that extends the CNLP algorithm (Peot and Smith 1992) for conditional execution. The result, C-BURIDAN, is an implemented planner that builds plans with probabilistic information-producing actions and contingent execution.

/pdf/probabilistic-planning-with-information-gathering-and-3k0l12y5tk.pdf

Probabilistic planning with information gathering and contingent execution

In response to the provision of a collection of semi-structured data (or its schema), a mapper generates a structured organization to store the collection of semi-structured data. The mapper further collaterally generates a description of how the semi-structured data are stored under the structured organization. In one embodiment, normalized relational tables are used for the structured organization. In alternate embodiments, non-normalized relational tables or other structured organization may also be used. The reverse, that is, the creation of a semistructured data organization for a collection of structured data may also be performed.

Method and apparatus for storing semi-structured data in a structured manner

For better or for worse, XML has emerged as a de facto standard for data interchange. This consensus is likely to lead to increased demand for technology that allows users to integrate data from a variety of applications, repositories, and partners, which are located across the corporate intranet or on the Internet. Nimble Technology has spent two years developing a product to service this market. Originally conceived after decades of person-years of research on data integration, the product is now being deployed at several Fortune-500 beta-customer sites. The article reports on the key challenges faced in the design of our product and highlights some issues which require more attention from the research community. In particular we address architectural issues arising from designing a product to support XML as its core representation, choices in the design of the underlying algebra, on-the-fly data cleaning and caching and materialization policies.

The Nimble XML data integration system

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