Institutional Repository
| Measuring ontology information by rules based transformation | |
| Ma, Yinglong (1); Lu, Ke (3); Zhang, Ying (1); Jin, Beihong (2); Ma, Y.(yinglongma@gmail.com) | |
| 2013 | |
| Source | Knowledge-Based Systems
 |
| ISSN | 9507051 |
| Volume | 50Pages:234-245 |
| English Abstract | Ontologies have currently attracted much attention of researchers and engineers in many fields such as knowledge management, etc. It is attractive for ontology engineers to select and reuse the existing ontologies by measuring and evaluating them because ontology construction is rather tedious and costly. In this paper, a general framework for stable semantic ontology measurement is proposed. We first clarify the concepts of syntactic, semantic and stable semantic ontology measurement. Then we present the semantic derived model (SDM) to represent the semantic model of an ontology. By rule based transformation, an ontology can be automatically transformed into its final semantic derived model (FSDM) which is unique. Furthermore, we can measure ontologies based on FSDM by analyzing the types of entities of the existing ontology metrics. The related experiments are made to illustrate that our framework can effectively excavate and stably measure the semantics of ontologies. © 2013 Elsevier B.V. All rights reserved.; Ontologies have currently attracted much attention of researchers and engineers in many fields such as knowledge management, etc. It is attractive for ontology engineers to select and reuse the existing ontologies by measuring and evaluating them because ontology construction is rather tedious and costly. In this paper, a general framework for stable semantic ontology measurement is proposed. We first clarify the concepts of syntactic, semantic and stable semantic ontology measurement. Then we present the semantic derived model (SDM) to represent the semantic model of an ontology. By rule based transformation, an ontology can be automatically transformed into its final semantic derived model (FSDM) which is unique. Furthermore, we can measure ontologies based on FSDM by analyzing the types of entities of the existing ontology metrics. The related experiments are made to illustrate that our framework can effectively excavate and stably measure the semantics of ontologies. © 2013 Elsevier B.V. All rights reserved. |
| Indexed Type | SCI ; EI |
| Keyword | Ontology Ontology Measurement Stable Measurement Semantic Measurement Ontology Engineering |
| Department | (1) School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; (2) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; (3) University of Chinese Academy of Sciences, Beijing 100049, China |
| Language | 英语 |
| WOS ID | WOS:000323875500018 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16916 |
| Collection | 中国科学院软件研究所 |
| Corresponding Author | Ma, Y.(yinglongma@gmail.com) |
| Recommended Citation GB/T 7714 | Ma, Yinglong ,Lu, Ke ,Zhang, Ying ,et al. Measuring ontology information by rules based transformation[J]. Knowledge-Based Systems,2013,50:234-245. |
| APA | Ma, Yinglong ,Lu, Ke ,Zhang, Ying ,Jin, Beihong ,&Ma, Y..(2013).Measuring ontology information by rules based transformation.Knowledge-Based Systems,50,234-245. |
| MLA | Ma, Yinglong ,et al."Measuring ontology information by rules based transformation".Knowledge-Based Systems 50(2013):234-245. |
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