Entity blocking, which aims to find all potentially matched tuple pairs in large-scale data, is an important step for entity resolution. It is non-trivial because it needs to consider both of the effectiveness and efficiency, and the emergence of representation learning has made it possible. Although there exist existing representation learning models for entity blocking, all of them require self-curated training instances in the target domain, which limits their capabilities for unseen data. In this paper, we propose UDAEB, a framework for Unsupervised Domain Adaptation for Entity Blocking that is fine-tuned between the source and target domains using contrastive learning by leveraging the capabilities of LLMs. UDAEB first adopts the adversarial learning strategy to reduce the distribution discrepency between source and target domains as the warmup step. Based on the initially learned representations, UDAEB involves pre-trained LLMs to enrich robust and distinguishable attributes for source and target domains. Furthermore, we propose an iterative step to fine-tune entity blocking model by selecting high-quality training instances with pseudo-labels by leveraging LLMs. Finally we conduct comprehensive experiments to show UDAEB has the superior performance against the state-of-the-art algorithms with aspects of the pair completeness (PC), pair quality (PQ) and the candidate set size ratio (CSSR).

Data preprocessing (DP) that transforms erroneous and raw data to a clean version is a cornerstone of the data mining pipeline. Due to the diverse requirements of downstream tasks, data scientists and domain experts have to handcraft domain-specific rules or train ML models with annotated examples, which is costly/time-consuming. In this paper, we present MELD (Mixture of Experts on Large Language Models for Data Preprocessing), a universal solver for low-resource DP. MELD adopts a Mixture-of-Experts (MoE) architecture that enables the amalgamation and enhancement of domain-specific experts trained on limited annotated examples. To fine-tune MELD, we develop a suite of expert-tuning and MoE-tuning techniques, including a retrieval augmented generation (RAG) system, meta-path search for data augmentation, expert refinement and router network training based on information bottleneck. To further verify the effectiveness of MELD, we theoretically prove that MoE in MELD is superior than a single expert and the router network is able to dispatch data to the right experts. Finally, we conducted extensive experiments on 19 datasets over 10 DP tasks to show that MELD outperforms the state-of-the-art methods in both effectiveness and efficiency. More importantly, MELD is able to be fine-tuned in a low-resource environment, e.g. a local, single and low-priced 3090 GPU.

Data quality is critical across many applications. The utility of data is undermined by various errors, making rigorous data cleaning a necessity. Traditional data cleaning systems depend heavily on predefined rules and constraints, which necessitate significant domain knowledge and manual effort. Moreover, while configuration-free approaches and deep learning methods have been explored, they struggle with complex error patterns, lacking interpretability, requiring extensive feature engineering or labeled data. This paper introduces GIDCL ( G raph-enhanced I nterpretable D ata C leaning with L arge language models), a pioneering framework that harnesses the capabilities of Large Language Models (LLMs) alongside Graph Neural Network (GNN) to address the challenges of traditional and machine learning-based data cleaning methods. By converting relational tables into graph structures, GIDCL utilizes GNN to effectively capture and leverage structural correlations among data, enhancing the model's ability to understand and rectify complex dependencies and errors. The framework's creator-critic workflow innovatively employs LLMs to automatically generate interpretable data cleaning rules and tailor feature engineering with minimal labeled data. This process includes the iterative refinement of error detection and correction models through few-shot learning, significantly reducing the need for extensive manual configuration. GIDCL not only improves the precision and efficiency of data cleaning but also enhances its interpretability, making it accessible and practical for non-expert users. Our extensive experiments demonstrate that GIDCL significantly outperforms existing methods, improving F1-scores by 10% on average while requiring only 20 labeled tuples.

This paper studies a new problem of relation enrichment. Given a relationDof schemaRand a knowledge graphGwith overlapping information, it is to identify a small number of relevant features fromG, and extend schemaRwith the additional attributes, to maximally improve the accuracy of resolving entities represented by the tuples ofD.We formulate the enrichment problem and show its intractability. Nonetheless, we propose a method to extract features fromGthat are diverse from the existing attributes ofR, minimize null values, and moreover, reduce false positives and false negatives of entity resolution (ER) models. The method links tuples and vertices that refer to the same entity, learns a robust policy to extract attributes via reinforcement learning, and jointly trains the policy and ER models. Moreover, we develop algorithms for (incrementally) enrichingD.Using real-life data, we experimentally verify that relation enrichment improves the accuracy of ER above 15.4% (percentage points) by adding 5 attributes, up to 33%.

There has been a host of work on entity resolution (ER), to identify tuples that refer to the same entity. This paper studies the inverse of ER, to identify tuples to which distinct real-world entities are matched by mistake, and split such tuples into a set of tuples, one for each entity. We formulate the tuple splitting problem. We propose a scheme to decide what tuples to split and what tuples to correct without splitting, fix errors/assign attribute values to the split tuples, and impute missing values. The scheme introduces a class of rules, which embed predicates for aligning entities across relations and knowledge graphs G, assessing correlation between attributes, and extracting data from G. It unifies logic deduction, correlation models, and data extraction by chasing the data with the rules. We train machine learning models to assess attribute correlation and predict missing values. We develop algorithms for the tuple splitting scheme. Using real-life data, we empirically verify that the scheme is efficient and accurate, with F-measure 0.92 on average.