Progressive Approach to Relational Entity Resolution
By Yasser Altowim, Dmitri V. Kalashnikov, Sharad Mehrotra. 2014
82 citations recorded by the source corpus.
This paper proposes a progressive approach to entity resolution (ER) that allows users to explore a trade-off between the resolution cost and the achieved quality of the resolved data. In particular, our approach aims to produce the highest quality result given a constraint on the resolution budget, specified by the user. Our proposed method monitors and dynamically reassesses the resolution progress to determine which parts of the data should be resolved next and how they should be resolved. The comprehensive empirical evaluation of the proposed approach demonstrates its significant advantage in terms of efficiency over the traditional ER techniques for the given problem settings.
Subfields: Artificial Intelligence, Management Science and Operations Research
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Citations in the corpus
- AlphaClean: Automatic Generation of Data Cleaning Pipelines (2019, background): “cleaning [4, 23, 28, 30, 41, 45] has revealed human-time — Machine Learning in Data Cleaning: Machine learning Application-Aware Cleaning: Semantics about the down-stream Generating Cleaning Programs: A composable data”
- BoostClean: Automated Error Detection and Repair for Machine Learning (2017, background): “Over the past few years, there have been several significant data cleaning advances in scalability [7,28,49].”
- ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016, background): “ActiveClean is inspired by the recent success of progressive data cleaning where a user can gradually clean more data until the desired accuracy is reached [6, 34, 30, 17, 26, 38, 37].”
- Data Cleaning: Overview and Emerging Challenges (2016, background): “…been a surge of interest from both industry and academia on different aspects of data cleaning including new abstractions [10, 30, 77, 22, 33, 14, 73], interfaces [1, 26], approaches for scalability [75, 45, 3, 53, 67], and crowdsourcing techniques [35, 69, 62, 18, 82, 23, 37, 60, 61, 76, 71, 78].”
- A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets (2016, background): “Progressive data cleaning is a well studied problem especially in the context of entity resolution [3, 34, 27, 16].”
- ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, methodology): “The challenges with Simpson’s paradox and sampling are problematic because recent advances in SQL data cleaning, such as Sample-and-Clean [33] and Progressive Data Cleaning [5,28,36], actually advocate cleaning subsets of data to avoid the potentially expensive cleaning costs.”
- Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “Most related to fact-checking is relational entity resolution [Altowim et al. 2014], where current techniques are either distancebased (e.g., [Kouki et al. 2016]) or dependence-based (e.g., [Bhattacharya and Getoor 2007]).”
- Query-Oriented Data Cleaning with Oracles (2015, methodology): “Existing tools address problems such as deduplication [17], entity resolution [1, 4], and schema matching [46].”
- A Survey of Fake News (2020, methodology): “, [Altowim et al. 2014; Bhattacharya and Getoor 2007; Trivedi et al. 2018]) can be used to identify proper matchings.”
- QuERy: A Framework for Integrating Entity Resolution with Query Processing (2015, unknown): “, [3, 26], are considering how to clean the data progressively, while interactively analyzing the partially cleaned data to compute better results.”