{"title":"贝叶斯证据管理的本体支持设计原则","authors":"M. Huhns, M. Valtorta, Jingsong Wang","doi":"10.3233/978-1-60750-581-5-163","DOIUrl":null,"url":null,"abstract":"This chapter describes work on an integrated system that can assist analysts in exploring hypotheses using Bayesian analysis of evidence from a variety of sources. The hypothesis exploration is aided by an ontology that represents domain knowledge, events, and causality for Bayesian reasoning, as well as models of information sources for evidential reasoning. We are validating the approach via a tool, Magellan, that uses both Bayesian models and logical models for an analyst's prior knowledge about how evidence can be used to evaluate hypotheses. The ontology makes it possible and practical for complex situations of interest to be modeled and then analyzed formally.","PeriodicalId":412076,"journal":{"name":"International Ontology for the Intelligence Community","volume":"6 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Design Principles for Ontological Support of Bayesian Evidence Management\",\"authors\":\"M. Huhns, M. Valtorta, Jingsong Wang\",\"doi\":\"10.3233/978-1-60750-581-5-163\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This chapter describes work on an integrated system that can assist analysts in exploring hypotheses using Bayesian analysis of evidence from a variety of sources. The hypothesis exploration is aided by an ontology that represents domain knowledge, events, and causality for Bayesian reasoning, as well as models of information sources for evidential reasoning. We are validating the approach via a tool, Magellan, that uses both Bayesian models and logical models for an analyst's prior knowledge about how evidence can be used to evaluate hypotheses. The ontology makes it possible and practical for complex situations of interest to be modeled and then analyzed formally.\",\"PeriodicalId\":412076,\"journal\":{\"name\":\"International Ontology for the Intelligence Community\",\"volume\":\"6 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Ontology for the Intelligence Community\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/978-1-60750-581-5-163\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Ontology for the Intelligence Community","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/978-1-60750-581-5-163","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design Principles for Ontological Support of Bayesian Evidence Management
This chapter describes work on an integrated system that can assist analysts in exploring hypotheses using Bayesian analysis of evidence from a variety of sources. The hypothesis exploration is aided by an ontology that represents domain knowledge, events, and causality for Bayesian reasoning, as well as models of information sources for evidential reasoning. We are validating the approach via a tool, Magellan, that uses both Bayesian models and logical models for an analyst's prior knowledge about how evidence can be used to evaluate hypotheses. The ontology makes it possible and practical for complex situations of interest to be modeled and then analyzed formally.