{"title":"ASPEN: ASP-Based System for Collective Entity Resolution","authors":"Zhiliang Xiang, Meghyn Bienvenu, Gianluca Cima, Víctor Gutiérrez-Basulto, Yazmín Ibáñez-García","doi":"arxiv-2408.06961","DOIUrl":null,"url":null,"abstract":"In this paper, we present ASPEN, an answer set programming (ASP)\nimplementation of a recently proposed declarative framework for collective\nentity resolution (ER). While an ASP encoding had been previously suggested,\nseveral practical issues had been neglected, most notably, the question of how\nto efficiently compute the (externally defined) similarity facts that are used\nin rule bodies. This leads us to propose new variants of the encodings\n(including Datalog approximations) and show how to employ different\nfunctionalities of ASP solvers to compute (maximal) solutions, and\n(approximations of) the sets of possible and certain merges. A comprehensive\nexperimental evaluation of ASPEN on real-world datasets shows that the approach\nis promising, achieving high accuracy in real-life ER scenarios. Our\nexperiments also yield useful insights into the relative merits of different\ntypes of (approximate) ER solutions, the impact of recursion, and factors\ninfluencing performance.","PeriodicalId":501123,"journal":{"name":"arXiv - CS - Databases","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Databases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.06961","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we present ASPEN, an answer set programming (ASP)
implementation of a recently proposed declarative framework for collective
entity resolution (ER). While an ASP encoding had been previously suggested,
several practical issues had been neglected, most notably, the question of how
to efficiently compute the (externally defined) similarity facts that are used
in rule bodies. This leads us to propose new variants of the encodings
(including Datalog approximations) and show how to employ different
functionalities of ASP solvers to compute (maximal) solutions, and
(approximations of) the sets of possible and certain merges. A comprehensive
experimental evaluation of ASPEN on real-world datasets shows that the approach
is promising, achieving high accuracy in real-life ER scenarios. Our
experiments also yield useful insights into the relative merits of different
types of (approximate) ER solutions, the impact of recursion, and factors
influencing performance.
在本文中,我们介绍了 ASPEN,它是最近提出的集体身份解析(ER)声明式框架的答案集编程(ASP)实现。虽然之前已经提出了 ASP 编码,但有几个实际问题却被忽略了,其中最突出的是如何高效计算规则体中使用的(外部定义的)相似性事实。这促使我们提出了编码的新变体(包括 Datalog 近似值),并展示了如何利用 ASP 求解器的不同功能来计算(最大)解以及可能合并集和确定合并集(的近似值)。在真实数据集上对 ASPEN 进行的综合实验评估表明,该方法很有前途,在真实的 ER 场景中实现了很高的准确性。其他实验还对不同类型(近似)ER 解决方案的相对优点、递归的影响以及影响性能的因素提出了有益的见解。