{"title":"报纸文章中犯罪事件的本体","authors":"Federica Rollo, Laura Po, Alessandro Castellucci","doi":"10.1145/3555776.3577862","DOIUrl":null,"url":null,"abstract":"The adoption of semantic technologies for the representation of crime events can help law enforcement agencies (LEAs) in crime prevention and investigation. Moreover, online newspapers and social networks are valuable sources for crime intelligence gathering. In this paper, we propose a new lightweight ontology to model crime events as they are usually described in online news articles. The Crime Event Model (CEM) can integrate specific data about crimes, i.e., where and when they occurred, who is involved (author, victim, and other subjects involved), which is the reason for the occurrence, and details about the source of information (e.g., the news article). Extracting structured data from multiple online sources and interconnecting them in a Knowledge Graph using CEM allow events relationships extraction, patterns and trends identification, and event recommendation. The CEM ontology is available at https://w3id.org/CEMontology.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"CEM: an Ontology for Crime Events in Newspaper Articles\",\"authors\":\"Federica Rollo, Laura Po, Alessandro Castellucci\",\"doi\":\"10.1145/3555776.3577862\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The adoption of semantic technologies for the representation of crime events can help law enforcement agencies (LEAs) in crime prevention and investigation. Moreover, online newspapers and social networks are valuable sources for crime intelligence gathering. In this paper, we propose a new lightweight ontology to model crime events as they are usually described in online news articles. The Crime Event Model (CEM) can integrate specific data about crimes, i.e., where and when they occurred, who is involved (author, victim, and other subjects involved), which is the reason for the occurrence, and details about the source of information (e.g., the news article). Extracting structured data from multiple online sources and interconnecting them in a Knowledge Graph using CEM allow events relationships extraction, patterns and trends identification, and event recommendation. The CEM ontology is available at https://w3id.org/CEMontology.\",\"PeriodicalId\":42971,\"journal\":{\"name\":\"Applied Computing Review\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Computing Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3555776.3577862\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computing Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3555776.3577862","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
CEM: an Ontology for Crime Events in Newspaper Articles
The adoption of semantic technologies for the representation of crime events can help law enforcement agencies (LEAs) in crime prevention and investigation. Moreover, online newspapers and social networks are valuable sources for crime intelligence gathering. In this paper, we propose a new lightweight ontology to model crime events as they are usually described in online news articles. The Crime Event Model (CEM) can integrate specific data about crimes, i.e., where and when they occurred, who is involved (author, victim, and other subjects involved), which is the reason for the occurrence, and details about the source of information (e.g., the news article). Extracting structured data from multiple online sources and interconnecting them in a Knowledge Graph using CEM allow events relationships extraction, patterns and trends identification, and event recommendation. The CEM ontology is available at https://w3id.org/CEMontology.
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