F. Amato, Aniello De Santo, V. Moscato, Fabio Persia, A. Picariello, S. Poccia
{"title":"由基于结构的方法驱动的本体划分","authors":"F. Amato, Aniello De Santo, V. Moscato, Fabio Persia, A. Picariello, S. Poccia","doi":"10.1109/icosc.2015.7050827","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel structure-based partitioning algorithm able to break a large ontology into different modules related to specific topics for the domain of interest. In particular, we leverage the topological properties of the ontology graph and exploit several techniques derived from Network Analysis to produce an effective partitioning without considering any information about semantics of ontology relationships. An automated partitioning tool has been developed and several preliminary experiments have been conducted to validate the effectiveness of our approach with respect to other techniques.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Partitioning of ontologies driven by a structure-based approach\",\"authors\":\"F. Amato, Aniello De Santo, V. Moscato, Fabio Persia, A. Picariello, S. Poccia\",\"doi\":\"10.1109/icosc.2015.7050827\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a novel structure-based partitioning algorithm able to break a large ontology into different modules related to specific topics for the domain of interest. In particular, we leverage the topological properties of the ontology graph and exploit several techniques derived from Network Analysis to produce an effective partitioning without considering any information about semantics of ontology relationships. An automated partitioning tool has been developed and several preliminary experiments have been conducted to validate the effectiveness of our approach with respect to other techniques.\",\"PeriodicalId\":126701,\"journal\":{\"name\":\"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/icosc.2015.7050827\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icosc.2015.7050827","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Partitioning of ontologies driven by a structure-based approach
In this paper, we propose a novel structure-based partitioning algorithm able to break a large ontology into different modules related to specific topics for the domain of interest. In particular, we leverage the topological properties of the ontology graph and exploit several techniques derived from Network Analysis to produce an effective partitioning without considering any information about semantics of ontology relationships. An automated partitioning tool has been developed and several preliminary experiments have been conducted to validate the effectiveness of our approach with respect to other techniques.