{"type":"ScholarlyArticle","title":"REPLACE: A Logical Framework for Combining Collective Entity Resolution and Repairing","authors":[{"name":"Meghyn Bienvenu","slug":"meghyn-bienvenu","given":"Meghyn","family":"Bienvenu","affiliation":"Univ. Bordeaux , CNRS , Bordeaux INP , LaBRI , UMR 5800"},{"name":"Gianluca Cima","slug":"gianluca-cima","given":"Gianluca","family":"Cima","affiliation":"Department of Computer, Control and Management Engineering , Sapienza University of Rome"},{"name":"Víctor Gutiérrez-Basulto","slug":"victor-gutierrez-basulto","given":"Víctor","family":"Gutiérrez-Basulto","affiliation":"School of Computer Science & Informatics , Cardiff University"}],"abstract":"This paper considers the problem of querying dirty databases, which may contain both erroneous facts and multiple names for the same entity. While both of these data quality issues have been widely studied in isolation, our contribution is a holistic framework for jointly deduplicating and repairing data. Our REPLACE framework follows a declarative approach, utilizing logical rules to specify under which conditions a pair of entity references can or must be merged and logical constraints to specify consistency requirements. The semantics defines a space of solutions, each consisting of a set of merges to perform and a set of facts to delete, which can be further refined by applying optimality criteria. As there may be multiple optimal solutions, we use classical notions of possible and certain query answers to reason over the alternative solutions, and introduce a novel notion of most informative answer to obtain a more compact presentation of query results. We perform a detailed analysis of the data complexity of the central reasoning tasks of recognizing optimal solutions and (most informative) possible and certain answers, for each of the three notions of optimal solution and for both general and restricted specifications.","doi":"10.24963/ijcai.2023/349","url":"https://ijcai.org/proceedings/2023/349","pdf":"https://ijcai.org/proceedings/2023/0349.pdf","markdown":"https://ijcai.org/proceedings/2023/349.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/349","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":349,"pages":{"first":3132,"last":3139},"keywords":["Knowledge Representation and Reasoning: KRR: Knowledge representation languages","Knowledge Representation and Reasoning: KRR: Computational complexity of reasoning","Multidisciplinary Topics and Applications: MDA: Databases"],"videos":[],"references":[{"position":1,"raw":"[Arasu et al., 2009] Arvind Arasu, Christopher Ré, and Dan Suciu. 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