{"title":"Document structure-driven investigative information retrieval","authors":"Tuomas Ketola, Thomas Roelleke","doi":"10.1016/j.is.2023.102315","DOIUrl":null,"url":null,"abstract":"<div><p>Data-driven investigations are increasingly dealing with non-moderated, non-standard and even manipulated information Whether the field in question is journalism, law enforcement, or insurance fraud it is becoming more and more difficult for investigators to verify the outcomes of various black-box systems To contribute to this need of discovery methods that can be used for verification, we introduce a methodology for document structure-driven investigative information retrieval (InvIR) InvIR is defined as a subtask of exploratory IR, where transparency and reasoning take centre stage The aim of InvIR is to facilitate the verification and discovery of facts from data and the communication of those facts to others From a technical perspective, the methodology applies recent work from structured document retrieval (SDR) concerned with formal retrieval constraints and information content-based field weighting (ICFW) Using ICFW, the paper establishes the concept of relevance structures to describe the document structure-based relevance of documents These contexts are then used to help the user navigate during their discovery process and to rank entities of interest The proposed methodology is evaluated using a prototype search system called Relevance Structure-based Entity Ranker (RSER) in order to demonstrate its the feasibility This methodology represents an interesting and important research direction in a world where transparency is becoming more vital than ever.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"121 ","pages":"Article 102315"},"PeriodicalIF":3.0000,"publicationDate":"2023-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0306437923001515/pdfft?md5=934dc470062407433a9cf64fc9053b41&pid=1-s2.0-S0306437923001515-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306437923001515","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Data-driven investigations are increasingly dealing with non-moderated, non-standard and even manipulated information Whether the field in question is journalism, law enforcement, or insurance fraud it is becoming more and more difficult for investigators to verify the outcomes of various black-box systems To contribute to this need of discovery methods that can be used for verification, we introduce a methodology for document structure-driven investigative information retrieval (InvIR) InvIR is defined as a subtask of exploratory IR, where transparency and reasoning take centre stage The aim of InvIR is to facilitate the verification and discovery of facts from data and the communication of those facts to others From a technical perspective, the methodology applies recent work from structured document retrieval (SDR) concerned with formal retrieval constraints and information content-based field weighting (ICFW) Using ICFW, the paper establishes the concept of relevance structures to describe the document structure-based relevance of documents These contexts are then used to help the user navigate during their discovery process and to rank entities of interest The proposed methodology is evaluated using a prototype search system called Relevance Structure-based Entity Ranker (RSER) in order to demonstrate its the feasibility This methodology represents an interesting and important research direction in a world where transparency is becoming more vital than ever.
期刊介绍:
Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems.
Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.