Magellan

Konda, Pradap; Das, Sanjib; Suganthan, G C Paul; Doan, AnHai; Ardalan, Adel; Ballard, Jeffrey R.; Li, Han; Panahi, Fatemah; Zhang, Haojun; Naughton, Jeff; Prasad, Shishir; Krishnan, Ganesh; Deep, Rohit; Raghavendra, Vijay

doi:10.14778/2994509.2994535

Cited by 181 publications

(13 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the validity of the proposed model, we also collect the dataset [29] for the experimental study of Entity Matching (EM) published by SIGMOD in 2018, and this dataset includes tabular data in the fields of bibliography, music, e-commerce, etc. Moreover, Magellan [30] , Corleone [31] , Falcon [32] , and other datasets that can also be used for EM experiments are also adopted. Because these datasets involve common fields, they are merged and re-divided to obtain two new English datasets.…”

Section: Datasetsmentioning

confidence: 99%

Construction and Optimization of Co-occurrence-attribute-interaction Model for Column Semantic Recognition

Gao¹,

Yuan²,

Lü³

et al. 2023

International Journal of Software and Informatics

View full text Add to dashboard Cite

Government data governance is undergoing a new phase of transition from "physical data aggregation" to "logical semantic unification". Thus far, long-term "autonomy" of government information silos, leads to a wide spectrum of metadata curation issues, such as attributes with the same names but having different meanings, or attributes with different names but having the same meanings. Instead of either rebuilding/modifying legacy information systems or physically aggregating data from government information silos, logical semantic unification solves this problem by unifying the semantic expression of the metadata in government information silos and achieves the standardized metadata governance. This paper focuses on the logical semantic unification that semantically aligns the metadata in each government information silo with the existing standard metadata. Specifically, the names of the standard metadata are abstracted as semantic labels, and the column projections of silo relational data are semantically recognized to semantically align column names with the standard metadata and ultimately achieve the standardized governance of silo metadata. The existing semantic recognition techniques based on column projection fail to capture the column orderindependent features of relational data and the correlation features among attributes and semantic labels. To address the above problem, we propose a two-phase model based on a prediction phase and a correction phase. In the prediction phase, a Co-occurrence-Attribute-Interaction (CAI) model is proposed to guarantee the column order-independent property by employing the parallelized self-attention mechanism; in the correction phase, a correction mechanism is introduced to optimize the prediction results of the CAI model by utilizing the co-occurrence of semantic labels. Experiments are conducted on a government benchmark dataset and several public English datasets, such as Magellan, and the results show that the two-phase model with a correction mechanism outperforms the current optimal model in macro-average and weighted average by up to 20.03% and 13.36%, respectively.

show abstract

Section: Datasetsmentioning

confidence: 99%

Construction and Optimization of Co-occurrence-attribute-interaction Model for Column Semantic Recognition

Gao¹,

Yuan²,

Lü³

et al. 2023

International Journal of Software and Informatics

View full text Add to dashboard Cite

show abstract

“…Following the seminal Fellegi-Sunter model for record linkage [16], a major focus of prior work has been on classifying pairs of input records as match, non-match, or potential match. While even some of the early work on record linkage incorporated a 1-1 matching constraint [62], the primary focus of most recent works has been on the effectiveness of the classification task, mainly by leveraging machine [28] and deep learning [5,35,40] methods.…”

Section: Related Workmentioning

confidence: 99%

An analysis of one-to-one matching algorithms for entity resolution

et al. 2023

View full text Add to dashboard Cite

Entity resolution (ER) is the task of finding records that refer to the same real-world entities. A common scenario, which we refer to as Clean-Clean ER, is to resolve records across two clean sources (i.e., they are duplicate-free and contain one record per entity). Matching algorithms for Clean-Clean ER yield bipartite graphs, which are further processed by clustering algorithms to produce the end result. In this paper, we perform an extensive empirical evaluation of eight bipartite graph matching algorithms that take as input a bipartite similarity graph and provide as output a set of matched records. We consider a wide range of matching algorithms, including algorithms that have not previously been applied to ER, or have been evaluated only in other ER settings. We assess the relative performance of these algorithms with respect to accuracy and time efficiency over ten established real-world data sets, from which we generated over 700 different similarity graphs. Our results provide insights into the relative performance of these algorithms and guidelines for choosing the best one, depending on the data at hand.

show abstract

“…The user can choose among several workflows and configure each step through both graphical and programming interfaces. Magellan [6] is a popular state-of-the-art ecosystem of entity matching tools for data scientists. Users can try out different blockers and matchers, utilize builtin debugging helpers, and use the provided guides to work through the process.…”

Section: Background and State Of The Artmentioning

confidence: 99%

“…Of course, most of these pain points have been addressed by varying degrees by existing work, but few efforts treat them holistically. For example, Magellan [6] offers flexibility and a short-term iterative workflow, but it is not suited for domain experts and lacks longterm iterative workflows. While CloudMatcher [4] can be used by domain experts, but are rigid.…”

Section: Challenges For Industrial Usementioning

confidence: 99%

Tailoring Entity Matching for Industrial Settings

Barlaug

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

Entity matching has received significant attention from the research community over many years. Despite some limited success, most state-of-the-art methods see no widespread usage in industry. In this paper, we present the author's PhD research, which aims at identifying issues that hold techniques and methods developed by the research community back from use in industry, and look at how they might be adapted to address those issues. In our proposed approach, we implement a modular framework, which will be used for real-world user testing and quantitative experiments of our adapted methods. We will have three main contributions from our research: 1) We develop a modular framework for interactive entity matching combining intra-and inter-session iterations. 2) We show how active learning methods for entity matching can be adapted to learn not only classification of matches but also classification of which records are of interest to the user jointly, and how it compares to current methods. 3) We show how deep learning can be used to synthesize interpretable rules for entity matching, and how it compares to traditional methods.

show abstract

Magellan

Cited by 181 publications

References 22 publications

Construction and Optimization of Co-occurrence-attribute-interaction Model for Column Semantic Recognition

Construction and Optimization of Co-occurrence-attribute-interaction Model for Column Semantic Recognition

An analysis of one-to-one matching algorithms for entity resolution

Tailoring Entity Matching for Industrial Settings

Contact Info

Product

Resources

About