32509112
A Highly Adaptable Web Information Extractor Using Graph Data Model
We present an approach to build highly adaptable extractor for collecting data from diverse Web sites. This approach uses Graph Model to represent content and structures as well as their various types of features. The generated graph is accompanied by a script in a special language called GQML containing the extraction rules. The running of the script transforms the graph into a specified format such as XML file that stores data from various Web sites in a uniform format. The experimental results show the presented approach is both effective and efficient.
28964163
Hidden Conditioned Homomorphism for XPath Fragment Containment
As a query language for navigating XML trees and selecting a set of element nodes, XPath is ubiquitous in XML applications. One important issue of XPath queries is checking containment. In particular, we investigate a frequently used fragment of XPath that consists of node tests, the child axis (/), the descendant axis (//), branches ([]) and label wildcards (*). For special classes of pattern trees, the homomorphism algorithm returns false negatives. In order to address this problem, we propose two containment techniques, conditioned homomorphism and hidden conditioned homomorphism, and then present sound algorithms to check containment. The analytical result is given with an experiment.
33716862
Computation of Sparse Data Cubes with Constraints
For a data cube there are always constraints between dimensions or between attributes in a dimension, such as functional dependencies. We introduce the problem that when there are functional dependencies, how to use them to speed up the computation of sparse data cubes. A new algorithm CFD is presented to satisfy this demand. CFD determines the order of dimensions by considering their cardinalities and functional dependencies between them together. It makes dimensions with functional dependencies adjacent and their codes satisfy monotonic mapping, thus reduces the number of partitions for such dimensions. It also combines partitioning from bottom to up and aggregate computation from top to bottom to speed up the computation further. In addition CFD can efficiently compute a data cube with hierarchies from the smallest granularity to the coarsest one, and at most one attribute in a dimension takes part in the computation each time. The experiments have shown that the performance of CFD has a significant improvement.
45051194
2-PS Based Associative Text Classification
Recent studies reveal that associative classification can achieve higher accuracy than traditional approaches. The main drawback of this approach is that it generates a huge number of rules, which makes it difficult to select a subset of rules for accurate classification. In this study, we propose a novel association-based approach especially suitable for text classification. The approach first builds a classifier through a 2-PS (Two-Phase) method. The first phase aims for pruning rules locally, i.e., rules mined within every category are pruned by a sentence-level constraint, and this makes the rules more semantically correlated and less redundant. In the second phase, all the remaining rules are compared and selected with a global view, i.e., training examples from different categories are merged together to evaluate these rules. Moreover, when labeling a new document, the multiple sentence-level appearances of a rule are taken into account. Experimental results on the well-known text corpora show that our method can achieve higher accuracy than many well-known methods. In addition, the performance study shows that our method is quite efficient in comparison with other classification methods.