Library and Information Science Commons^™

Adaptive Modeling Of Workforce Domain Knowledge, Jian Qin, Peter Creticos, Wen-Yuan Hsiao

School of Information Studies - Faculty Scholarship

Workforce development is a multidisciplinary domain in which policy, laws and regulations, social services, training and education, and information technology and systems are heavily involved. It is essential to have a semantic base accepted by the workforce development community for knowledge sharing and exchange. This paper describes how such a semantic base—the Workforce Open Knowledge Exchange (WOKE) Ontology—was built by using the adaptive modeling approach. The focus of this paper is to address questions such as how ontology designers should extract and model concepts obtained from different sources and what methodologies are useful along the steps of ontology development. The …

The Semantic And Syntactic Model Of Metadata, Jian Qin, Javier Calzada Prado

School of Information Studies - Faculty Scholarship

As more information becomes “born digital”, metadata creation is increasingly becoming part of the information creation process. Current metadata schemes inherit much of the library cataloging tradition, which has shown limitations on representing “born digital” type of resources. Through analysis of issues of metadata schemes and review of metadata research and projects, the authors propose an ontology-based approach to building a modular metadata model in which semantics and syntax may be integrated to suit the needs for representing “born digital” resources. The authors use an learning object ontology as an example to demonstrate how the semantics and syntax may be …

Building Interoperable Vocabulary And Structures For Learning Objects, Jian Qin, Naybell Hernández

School of Information Studies - Faculty Scholarship

The structural, functional, and production views on learning objects influence metadata structure and vocabulary. We drew on these views and conducted a literature review and in-depth analysis of 14 learning objects and over 500 components in these learning objects to model the knowledge framework for a learning object ontology. The learning object ontology reported in this paper consists of 8 top-level classes, 28 classes at the second level, and 34 at the third level. Except class Learning object, all other classes have the three properties of preferred term, related term, and synonym. To validate the ontology, we conducted a query …

Certainty Identification In Texts: Categorization Model And Manual Tagging Results, Elizabeth D. Liddy, Victoria L. Rubin, Noriko Kando

School of Information Studies - Faculty Scholarship

This chapter presents a theoretical framework and preliminary results for manual categorization of explicit certainty information in 32 English newspaper articles. Our contribution is in a proposed categorization model and analytical framework for certainty identification. Certainty is presented as a type of subjective information available in texts. Statements with explicit certainty markers were identified and categorized according to four hypothesized dimensions – level, perspective, focus, and time of certainty.

The preliminary results reveal an overall promising picture of the presence of certainty information in texts, and establish its susceptibility to manual identification within the proposed four-dimensional certainty categorization analytical framework. …