15198141
Build Presentation Layer for Semantic Contents
Large scale of semantically enriched data is the foundation of the semantic web. We introduce the model used in SESQ system as the presentation layer of the semantic contents. It is an abstract graph independent of the data storage layer and application layer. Semantic contents of a specified domain are organized as nodes and arcs in the graph. GQML, a manipulation language, is designed for the graph, which is also used as the query language to semantic contents. With this model, the interoperation and integration of different sources will be easier. Now the model has been implemented on Berkley Database System and Relational Database.
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.
8920901
Exploit Sequencing to Accelerate XML Twig Query Answering
Speeding up query evaluation in large XML repositories becomes a challenging and all-important problem with vast XML-related applications arising. In this paper, we present SCALER, an efficient algorithm for XML query answering based on UDFTS and effective twig structure matching scheme. UDFTS not only constructs a one-to-one correspondence between trees and sequences but also maintains critical parent-child relationships for twig structure matching. With SCALER, XML queries can be performed by subsequence matching without breaking twigs into sub paths and evaluating these paths individually. Thus, costly join operations can be avoided elegantly. We also show the correctness of query answering by eliminating false dismissals and false alarms naturally in SCALER. By a thorough experimental study on various real-life data, we prove the efficiency and scalability of SCALER over the previous known alternative.
11780341
Sentential Association Based Text Classification Systems
We recently proposed a novel sentential association based approach SAT-MOD for text classification, which views a sentence rather than a document as an association transaction, and uses a novel heuristic called MODFIT to select the most significant itemsets for constructing a category classifier. Based on SAT-MOD, we have developed a prototype system called SAT-Class. In this demo, we demonstrate the effectiveness of our text classification system, and also the readability and refinability of acquired classification rules.
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.