A Study on the Best Practice for Constructing a Cross-lingual Geospatial Information Ontology
Date Issued
2015
Date
2015
Author(s)
Cheng, Yi-Yun
Abstract
Ontologies, as the fundamental building blocks for the Semantic Web, are the highest-level classification scheme in the family of knowledge organization systems. With the emergence of big data, ontologies are keys to unraveling the information explosion problems, especially in the geospatial information domain. Under the big data situation, other language cultures, not limited to English, are also in a pressing need to construct ontologies. Many tries to be interoperable with ontologies written in other languages, but what lacks are a successful cross-lingual ontology mapping method, and a detailed mapping model for others to follow. The purpose of this study thus is to investigate such methodology for constructing a cross-lingual ontology, in hoping that the model and constructing steps can be recognized as the de-facto practice for future research. By using a three-phase design methodology, this study begins by reviewing literature on building ontologies and ontology mapping methods. In phase two, we try to map the geospatial information ontology—SWEET ontology—with the termlists from National Academy of Educational Research in Taiwan. In phase three, we model the mapped English/Chinese ontology in Protégé software to explore the prospect of this method. The results in phase one suggests that there are mainly three types of ontology building methods— starting from scratch, KOS-based, and using existing ontologies. As to ontology mapping methods, we divide them by either manual-processing or automatic/semi-automatic processing methods. In phases two and three, we propose a cross-lingual ontology mapping model and provide an actual step-to-step guide to produce a “switch” for connecting ontologies in different formats and languages. We have also used SKOS relationships to Chinese terms in our ontology to express synonyms and related terms. The semi-automatic mapping result from English to Chinese shows 80.66% accuracy on the exact-match terms; and the search process for the Chinese and English classes in Protégé have proven the feasibility of the practice in this study.
Subjects
ontology
cross-lingual ontology mapping
SWEET ontology
Type
thesis
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