Entity Matching (EM), which aims to identify all pairs of records referring to the same real-world entity from relational tables, is one of the most important tasks in real-world data management systems. Due to the labeling process of EM being extremely labor-intensive, unsupervised EM is more applicable than supervised EM in practical scenarios. Traditional unsupervised EM assumes that all entities come from two tables; however, it is more common to match entities from multiple tables in practical applications, that is, multi-table entity matching (multi-table EM). Unfortunately, effective and efficient unsupervised multi-table EM remains under-explored. To fill this gap, this paper formally studies the problem of unsupervised multi-table entity matching and proposes an effective and efficient solution, termed as MultiEM. MultiEM is a parallelable pipeline of enhanced entity representation, table-wise hierarchical merging, and density-based pruning. Extensive experimental results on six real-world benchmark datasets demonstrate the superiority of MultiEM in terms of effectiveness and efficiency.

Table interpretation (TI), which aims to predict the column types and relations of tables, plays an essential role in necessary decision-making actions for data management systems. Typically, TI is followed by a manual verification, where experts manually verify the correctness of TI’s predictions. Manual verification is able to ensure the quality of decision-making actions but labor-intensive. To reduce the labour costs, providing explanations for TI’s predictions is necessary as these explanations can help them do faster and more accurate verification. However, existing TI approaches overlook the manual verification process and lack explainability1. To fill this gap, this paper explores the challenging explainable table interpretation problem, which aims to provide faithful explanations and meanwhile achieve high prediction performance. Furthermore, we propose ExplainTI framework. ExplainTI consists of two phases: (i) tables are converted to sequences and lightweight column graphs; and (ii) a pre-trained transformer encoder is fine-tuned to provide multi-view explanations and aggregate contextual information. Extensive experiments on both real Web tables and database tables confirm that ExplainTI outperforms competitive baselines. Moreover, systematical analysis of explainability demonstrates that our framework can provide faithful explanations to facilitate the manual verification process.

Entity Matching (EM) aims to find data instances from different sources that refer to the same real-world entity. The existing EM techniques can be either costly or tailored for a specific data type. We present ZeroMatcher, a cost-off entity matching system, which supports (i) handling EM tasks with different data types, including relational tables and knowledge graphs; (ii) keeping its EM performance always competitive by enabling the sub-modules to be updated in a lightweight manner, thus reducing development costs; and (iii) performing EM without human annotations to further slash the labor costs. First, ZeroMatcher automatically suggests users a set of appropriate modules for EM according to the data types of the input datasets. Users could specify the modules for the subsequent EM process according to their preferences. Alternatively, users are able to customize the modules of ZeroMatcher. Then, the system proceeds to the EM task, where users can track the entire EM process and monitor the memory usage changes in real-time. When the EM process is completed, ZeroMatcher visualizes the EM results from different aspects to ease the understanding for users. Finally, ZeroMatcher provides EM results evaluation, enabling users to compare the effectiveness among different parameter settings.

Entity Matching (EM), which aims to identify whether two entity records from two relational tables refer to the same real-world entity, is one of the fundamental problems in data management. Traditional EM assumes that two tables are homogeneous with the aligned schema, while it is common that entity records of different formats (e.g., relational, semi-structured, or textual types) involve in practical scenarios. It is not practical to unify their schemas due to the different formats. To support EM on format-different entity records, Generalized Entity Matching (GEM) has been proposed and gained much attention recently. To do GEM, existing methods typically perform in a supervised learning way, which relies on a large amount of high-quality labeled examples. However, the labeling process is extremely labor-intensive, and frustrates the use of GEM. Low-resource GEM, i.e., GEM that only requires a small number of labeled examples, becomes an urgent need. To this end, this paper, for the first time, focuses on the low-resource GEM and proposes a novel low-resource GEM method, termed as PromptEM. PromptEM has addressed three challenging issues (i.e., designing GEM-specific prompt-tuning, improving pseudo-labels quality, and running efficient self-training) in low-resource GEM. Extensive experimental results on eight real benchmarks demonstrate the superiority of PromptEM in terms of effectiveness and efficiency.