Ching‐Chieh Kiu and Chien‐Sing Lee
The purpose of this paper is to present an automated ontology mapping and merging algorithm, namely OntoDNA, which employs data mining techniques (FCA, SOM, K‐means) to resolve…
Abstract
Purpose
The purpose of this paper is to present an automated ontology mapping and merging algorithm, namely OntoDNA, which employs data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities among distributed data sources in organizational memory and subsequently generate a merged ontology to facilitate resource retrieval from distributed resources for organizational decision making.
Design/methodology/approach
The OntoDNA employs unsupervised data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities to integrate distributed data sources in organizational memory. Unsupervised methods are needed as an alternative in the absence of prior knowledge for managing this knowledge. Given two ontologies that are to be merged as the input, the ontologies' conceptual pattern is discovered using FCA. Then, string normalizations are applied to transform their attributes in the formal context prior to lexical similarity mapping. Mapping rules are applied to reconcile the attributes. Subsequently, SOM and K‐means are applied for semantic similarity mapping based on the conceptual pattern discovered in the formal context to reduce the problem size of the SOM clusters as validated by the Davies‐Bouldin index. The mapping rules are then applied to discover semantic similarity between ontological concepts in the clusters and the ontological concepts of the target ontology are updated to the source ontology based on the merging rules. Merged ontology in a concept lattice is formed.
Findings
In experimental comparisons between PROMPT and OntoDNA ontology mapping and merging tool based on precision, recall and f‐measure, average mapping results for OntoDNA is 95.97 percent compared to PROMPT's 67.24 percent. In terms of recall, OntoDNA outperforms PROMPT on all the paired ontology except for one paired ontology. For the merging of one paired ontology, PROMPT fails to identify the mapping elements. OntoDNA significantly outperforms PROMPT due to the utilization of FCA in the OntoDNA to capture attributes and the inherent structural relationships among concepts. Better performance in OntoDNA is due to the following reasons. First, semantic problems such as synonymy and polysemy are resolved prior to contextual clustering. Second, unsupervised data mining techniques (SOM and K‐means) have reduced problem size. Third, string matching performs better than PROMPT's linguistic‐similarity matching in addressing semantic heterogeneity, in context it also contributes to the OntoDNA results. String matching resolves concept names based on similarity between concept names in each cluster for ontology mapping. Linguistic‐similarity matching resolves concept names based on concept‐representation structure and relations between concepts for ontology mapping.
Originality/value
The OntoDNA automates ontology mapping and merging without the need of any prior knowledge to generate a merged ontology. String matching is shown to perform better than linguistic‐similarity matching in resolving concept names. The OntoDNA will be valuable for organizations interested in merging ontologies from distributed or different organizational memories. For example, an organization might want to merge their organization‐specific ontologies with community standard ontologies.
Details
Keywords
Sylvia Ping-Ping Chin, Eric Tsui and Chien-Sing Lee
Guidelines for the design of knowledge-based e-learning usability systems are absent from the current recognized set of usability design heuristics and from an established…
Abstract
Purpose
Guidelines for the design of knowledge-based e-learning usability systems are absent from the current recognized set of usability design heuristics and from an established evaluation methodology of e-learning system developments. Such systems can help Web designers and instructional designers design for different user needs and decide which properties are of a higher priority, thus meriting more design and development efforts. The authors aim to help students develop higher-order thinking skills, such as application, evaluation and syntheses of knowledge.
Design/methodology/approach
The authors applied Merrill ' s first principles of instruction and usability properties as pedagogical and usability design guidelines, knowledge management (KM) and hierarchical task analysis as methodological knowledge bases. The authors proposed a KM e-learning usability framework which frames our mapping of Web usability attributes to e-learning usability properties. The authors aim to investigate whether adopting Merrill ' s first principles of instruction and usability properties as knowledge-based guidelines/design factors would help learners develop higher-order thinking skills and whether this design would result in positive technology acceptance. The authors also developed a method matrix to map the selected methods of cognitive engineering to its potential uses in the KM e-learning usability framework of this paper and mapped e-learning usability tools with components in the KM e-learning usability system.
Findings
Findings indicated that our design effectively helped learners to demonstrate higher-order thinking skills and positive technology acceptance, promising indications toward the design and development of knowledge-based usability frameworks and systems.
Research/limitations/implications
The sample size of this paper is small. Hence, conclusions are not generalizable at this moment.
Originality/Value
The authors’ contributions are twofold: First, the authors proposed a KM e-learning usability framework, which frames the mapping of KM processes to e-learning principles and usability properties. Second, the authors proposed a method matrix which maps the selected methods of cognitive engineering to its potential uses in their KM e-learning usability framework. Based on these mappings and focusing on the usability properties navigation and learning support, the authors used ICT/Web2.0 tools to present/visualize information more clearly and more sensibly/manageably to students, to help trigger new knowledge and develop higher-order thinking skills, such as application, evaluation and syntheses of knowledge and articulate information from different perspectives throughout the KM life cycle.
Details
Keywords
Carol Huang and Connie Chuyun Hu
The study examines how the tourism concept developed amongst Chinese students in the United States from 1905 to current juncture. Through the contrasting views presented in two…
Abstract
The study examines how the tourism concept developed amongst Chinese students in the United States from 1905 to current juncture. Through the contrasting views presented in two landmark mega-reviews of Chinese students in the United States and France, the authors concluded that tourism enhances understanding of the host countries resulting in more comprehensive and overall success of Study Abroad Program. After the reopening, China encouraged touring the host country but with extreme financial constraints in the beginning. Tourism of Chinese students became popular and fashionable only in late 1990s with China’s economic prosperity and policy changes to open tourism to foreign countries. As tension with China grew during the COVID pandemic, Chinese students in the United States were used by the Trump Administration as a lever in trade and diplomatic negotiation, and touring became wishful.