|
|
|
| An Ontology Fusion Method Based on Binary Similarity Calculation |
| Lou Wen1,2, Wang Hui1,2, Ju Yuan3 |
1.Department of Information Management, Faculty of Economics and Management, East China Normal University, Shanghai 200241
2.Institute for Academic Evaluation and Development, East China Normal University, Shanghai 200241
3.Dianping.com, Shanghai 200042 |
|
|
|
|
Abstract Heterogeneous ontology causes redundancy in knowledge retrieval. Therefore, knowledge fusion based on heterogeneous ontology is necessary. However, because of the massive capacity and complicated processes required for semantic similarity computing, knowledge fusion has become less simple. In this paper, we propose an ontology fusion method based on binary metrics of semantic similarity calculation. In the fusion process, there will be only binary matching, thus aiming to further simplify the calculation of fusion from semantic similarity. Thus, the present research represents a shift from methods locating computing progress at the beginning of original ontology construction. We adopted three experiments to test the usability of our approach, from the perspectives of (1) actual library resources, (2) a small dataset, and (3) a large dataset. In experiment one, bibliographic data from Wuhan University Library were used to test our proposal s feasibility and capabilities. Results showed that our approach can completely merge two ontologies into a single theme. The second and third experiments both verified that our approach has the ability to accurately detect merging couples and decrease time cost. The tests demonstrated a good overall fusion result; nevertheless, recall requires future improvement. This method is expected to extend the implementation of expert ontology and aid in cost reduction of ontology construction.
|
|
Received: 20 December 2017
|
|
|
|
1 ZachG, ChrisG, RichardG, et al. Data fluency: Empowering your organization with effective data communication[M]. John Wiley & Sons, 2015: 91-140.
2 刘晓娟, 李广建, 化柏林. 知识融合: 概念辨析与界说[J]. 图书情报工作, 2016, 60(13): 13-19, 32.
3 林海伦, 王元卓, 贾岩涛, 等. 面向网络大数据的知识融合方法综述[J]. 计算机学报, 2017, 40(1): 1-27.
4 DingY, FooS. Ontology research and development. Part 2 - a review of ontology mapping and evolving[J]. Journal of Information Science, 2002, 28(5): 123-136.
5 KimJ, KimP, ChungH. Ontology construction using online ontologies based on selection, mapping and merging[J]. International Journal of Web and Grid Services, 2011, 7(2): 170-189.
6 WuZ X, TianX Y. Research of ontology merging based on concept similarity[C]// Proceedings of the Seventh International Conference on Measuring Technology and Mechatronics Automation. IEEE, 2015: 831-834.
7 于晓繁, 王效岳, 白如江. 本体集成方法和工具综述[J]. 现代图书情报技术, 2011(1): 14-21.
8 AstrovaI. Rules for mapping SQL relational databases to OWL ontologies[C]// Proceedings of the International Conference on Metadata and Semantics Research. Boston: Springer, 2009: 415-424.
9 GaoW, GaoY, ZhuL. Ranking based ontology learning algorithm for similarity measuring and ontology mapping using representation theory[J]. Journal of Information & Optimization Sciences, 2016, 37(2): 303-320.
10 王效岳, 胡泽文, 白如江, 等. 本体集成: 概念、过程、工具与方法综述[J]. 图书情报工作, 2011, 55(16): 119-125.
11 米杨, 曹锦丹. 基于PROMPT的本体映射实例分析[J]. 情报学报, 2010, 29(6): 987-991.
12 BurgunA, BodenreiderO. Mapping the UMLS semantic network into general ontologies[J]. Proceedings of AMIA Annual Symposium, 2001: 86-90.
13 MignardC, NicolleC. Merging BIM and GIS using ontologies application to urban facility management in ACTIVe3D[J]. Computers in Industry, 2014, 65(9): 1276-1290.
14 do AmaralM B, RobertsA, RectorA L. NLP techniques associated with the OpenGALEN ontology for semi-automatic textual extraction of medical knowledge: abstracting and mapping equivalent linguistic and logical constructs[J]. Proceedings of AMIA Annual Symposium, 2000: 76-80.
15 徐健, 方安, 洪娜. 一种基于词语相似度计算的本体映射方法[J]. 现代图书情报技术, 2013(2): 36-42.
16 姚晓明, 王锋, 林兰芬, 等. 一种高效的多策略本体映射方法[J]. 中国科技论文, 2013, 8(7): 642-647.
17 李凯, 李万龙, 郑山红, 等. 改进的多策略本体映射方法[J]. 吉林大学学报(信息科学版), 2016, 34(4): 536-542.
18 裘江南, 李丽冬, 吴力文, 等. 基于传递的语义相关度计算方法研究[J]. 情报学报, 2010, 29(4): 749-758.
19 裘江南, 李丽冬, 吴力文, 等. 本体中同种语义关系间的可传递规律研究[J]. 情报学报, 2009, 28(5): 658-663.
20 唐杰, 梁邦勇, 李涓子, 等. 语义Web中的本体自动映射[J]. 计算机学报, 2006, 29(11): 1956-1976.
21 于娟, 熊振辉, 欧忠辉. 基于哈斯图的本体偏序关系消冗方法研究[J]. 情报学报, 2015, 34(3): 279-285.
22 MareeM, BelkhatirM. Addressing semantic heterogeneity through multiple knowledge base assisted merging of domain-specific ontologies[J]. Knowledge-Based Systems, 2015, 73: 199-211.
23 郭强, 关欣, 潘丽娜, 等. 一种基于条件证据网络的多源异类知识融合识别方法[J]. 控制与决策, 2015, 30(12): 2153-2160.
24 董慧, 姜赢, 高巾, 等. 基于数字图书馆的本体演化和知识管理研究(Ⅰ)——本体分子理论[J]. 情报学报, 2009, 28(3): 323-330.
25 苗壮, 张亚非, 陆建江. 从多个RDFS本体中抽取子本体[J]. 情报学报, 2007, 26(1): 71-76.
26 毕强, 牟冬梅, 范轶. 数字图书馆语义互联中的桥本体构建[J]. 情报学报, 2010, 29(6): 1051-1057.
27 蔡丽宏, 马静. 基于综合方法的一种本体映射实验研究[J]. 情报学报, 2010, 29(5): 820-825.
28 滕广青, 毕强. 基于概念格的跨本体映射中概念相似度计算方法[J]. 情报学报, 2012, 31(4): 390-397.
29 LiJ L, HeZ Y, ZhuQ L. An Entropy-based weighted concept lattice for merging multi-source geo-ontologies[J]. Entropy, 2013, 15: 2303-2318.
30 SinghS, CheahY N. Hybrid approach towards ontology mapping[C]// Proceedings of the International Symposium on Information Technology. IEEE, 2010: 1490-1493.
31 王汀, 高迎, 刘经纬. 一种面向中文本体模式的本体对齐框架[J]. 数据分析与知识发现, 2017, 1(2): 47-57.
32 王顺, 康达周, 江东宇. 本体映射综述[J]. 计算机科学, 2017, 44(9): 1-10.
33 黄奇, 范佳林, 陆佳莹, 等. 本体映射系统的评价体系研究[J]. 情报学报, 2017, 36(8): 781-789.
34 楼雯. 馆藏资源语义化关键技术及实证研究[J]. 中国图书馆学报, 2013, 39(6): 27-40.
35 GuJ, XuB, ChenX. An XML query rewriting mechanism with multiple ontologies integration based on complex semantic mapping[J]. Information Fusion, 2008, 9(4): 512-522. |
|
|
|
