Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

N-Grams Assisted Long Web Search Query Optimization

Commercial search engines do not return optimal search results when the query is a long or multi-topic one [1]. Long queries are used extensively. While the creator of the long query would most likely use natural language to describe the query, it contains extra information. This information dilutes the results of a web search, and hence decreases the performance as well as quality of the results returned. Kumaran et al. [22] showed that shorter queries extracted from longer user generated queries are more effective for ad-hoc retrieval. Hence reducing these queries by removing extra terms, the quality of the search results can be improved. There are numerous approaches used to address this shortfall. Our approach evaluates various versions of the query, thus trying to find the optimal one. This variation is achieved by reducing the query length using a combination of n-grams assisted query selection as well as a random keyword combination generator. We look at existing approaches and try to improve upon them. We propose a hybrid model that tries to address the shortfalls of an existing technique by incorporating established methods along with new ideas. We use the existing models and plug in information with the help of n-grams as well as randomization to improve the overall performance while keeping any overhead calculations in check

A Multiple-Expert Binarization Framework for Multispectral Images

In this work, a multiple-expert binarization framework for multispectral images is proposed. The framework is based on a constrained subspace selection limited to the spectral bands combined with state-of-the-art gray-level binarization methods. The framework uses a binarization wrapper to enhance the performance of the gray-level binarization. Nonlinear preprocessing of the individual spectral bands is used to enhance the textual information. An evolutionary optimizer is considered to obtain the optimal and some suboptimal 3-band subspaces from which an ensemble of experts is then formed. The framework is applied to a ground truth multispectral dataset with promising results. In addition, a generalization to the cross-validation approach is developed that not only evaluates generalizability of the framework, it also provides a practical instance of the selected experts that could be then applied to unseen inputs despite the small size of the given ground truth dataset.Comment: 12 pages, 8 figures, 6 tables. Presented at ICDAR'1