Ing-Xiang Chen

An iterative approach for web catalog integration with support vector machines

Web catalog integration is an emerging problem in current digital content management. Past studies show that more improvement on integration accuracy can be achieved with advanced classifiers. Because Support Vector Machine (SVM) has shown its supremeness in recent research, we propose an iterative SVM-based approach (SVM-IA) to improve the integration performance. We have conducted experiments of real-world catalog integration to evaluate the performance of SVM-IA and cross-training SVM. The results show that SVM-IA has prominent accuracy performance, and the performance is more stable.

On hierarchical web catalog integration with conceptual relationships in thesaurus

Web catalog integration is an interesting problem in current digital content management. Past studies have shown that using a flattened structure with auxiliary information extracted from the source catalog can improve the integration results. However, the nature of a flattened structure ignores the hierarchical relationships, and thus the performance improvement of catalog integration may be reduced. In this paper, we propose an enhanced hierarchical catalog integration (EHCI) approach with conceptual thesauri extracted from the source catalog. The results show that our enhanced hierarchical integration approach effectively boosts the accuracy of hierarchical catalog integration.

RDF/XTM ontology construction based on a topic maps-driven framework

Ontology construction plays an important role in many semantically working environments, such as knowledge management systems and digital libraries. The efficiency, quality, and comprehensiveness of the construction process highly depends on the employed development framework. In this paper, we propose a Topic Maps-driven framework, called XRVAT, to alleviate the cumbersome construction procedure and generate both RDF/XTM ontologies with an effective XTM-to-RDF translation kernel. This paper briefly presents its design and an operational example.

Improving hierarchical taxonomy integration with semantic feature expansion on category-specific terms

In recent years, the taxonomy integration problem has obtained much attention in many research studies. Many sorts of implicit information embedded in the source taxonomy are explored to improve the integration performance. However, the semantic information embedded in the source taxonomy has not been discussed in the past research. In this paper, an enhanced integration approach called SFE (Semantic Feature Expansion) is proposed to exploit the semantic information of the category-specific terms. From our experiments on two hierarchical Web taxonomies, the results are positive to show that the integration performance can be further improved with the SFE scheme.

Learning to integrate web catalogs with conceptual relationships in hierarchical thesaurus

Web catalog integration has been addressed as an important issue in current digital content management. Past studies have shown that exploiting a flattened structure with auxiliary information extracted from the source catalog can improve the integration results. Although earlier studies have also shown that exploiting a hierarchical structure in classification may bring better advantages, the effectiveness has not been testified in catalog integration. In this paper, we propose an enhanced catalog integration (ECI) approach to extract the conceptual relationships from the hierarchical Web thesaurus and further improve the accuracy of Web catalog integration. We have conducted experiments of real-world catalog integration with both a flattened structure and a hierarchical structure in the destination catalog. The results show that our ECI scheme effectively boosts the integration accuracy of both the flattened scheme and the hierarchical scheme with the advanced Support Vector Machine (SVM) classifiers.

XRVAT: A Visual Topic Maps-Based Environment for Ontology Construction

Ontology construction is important in many semantically working environments, such as knowledge management systems and digital libraries. The efficiency, quality, and comprehensiveness of the construction process highly depend on the employed development framework. In this paper, we present a Topic Maps-driven framework called XRVAT for XTM/RDF ontology construction with an effective XTM-to-RDF translation processing kernel. The framework has two major design features. First, the Topic Maps-driven graphical user interface alleviates the cumbersome construction procedure and provides users with a clear top-down conceptual view. Second, the effective XTM-to-RDF translation kernel allows users to incorporate topic features from Topic Maps into RDF and generate XTM/RDF ontologies. Currently, a prototype has been implemented in Java. This paper presents its design methodology and demonstrates its operations with examples. Besides, quantitative analyses were conducted to demonstrate the improvement of the XRVAT interface upon the previous textbox-based interface and its enhanced interface with the visual authoring functionality.

A peer-to-peer architecture for web annotation sharing

In this paper, we present a peer-to-peer (P2P) architecture design called PWAS to share personal Web annotations with a hybrid hierarchical P2P approach. PWAS maintains a user-centric annotation environment in which personal annotations can be flexibly shared with a reduced number of query messages.