B. Groz, Aurélien Lemay, S. Staworko, Piotr Wieczorek
{"title":"Inference of Shape Graphs for Graph Databases","authors":"B. Groz, Aurélien Lemay, S. Staworko, Piotr Wieczorek","doi":"10.4230/LIPIcs.ICDT.2022.14","DOIUrl":null,"url":null,"abstract":"We investigate the problem of constructing a shape graph that describes the structure of a given graph database. We employ the framework of grammatical inference , where the objective is to find an inference algorithm that is both sound , i.e., always producing a schema that validates the input graph, and complete , i.e., able to produce any schema, within a given class of schemas, provided that a sufficiently informative input graph is presented. We identify a number of fundamental limitations that preclude feasible inference. We present inference algorithms based on natural approaches that allow to infer schemas that we argue to be of practical importance.","PeriodicalId":90482,"journal":{"name":"Database theory-- ICDT : International Conference ... proceedings. International Conference on Database Theory","volume":"9 1","pages":"14:1-14:20"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Database theory-- ICDT : International Conference ... proceedings. International Conference on Database Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4230/LIPIcs.ICDT.2022.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
We investigate the problem of constructing a shape graph that describes the structure of a given graph database. We employ the framework of grammatical inference , where the objective is to find an inference algorithm that is both sound , i.e., always producing a schema that validates the input graph, and complete , i.e., able to produce any schema, within a given class of schemas, provided that a sufficiently informative input graph is presented. We identify a number of fundamental limitations that preclude feasible inference. We present inference algorithms based on natural approaches that allow to infer schemas that we argue to be of practical importance.