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Deep entity resolution (ER) identifies matching entities across data sources using techniques based on deep learning. It involves two steps: a blocker for identifying the potential matches to generate the candidate pairs, and a matcher for accurately ...

Link prediction and entity resolution play pivotal roles in uncovering hidden relationships within networks and ensuring data quality in the era of heterogeneous data integration. This paper explores the utilization of large language models to ...

We introduce Rock, a system for cleaning relational data. Rock implements a framework that unifies machine learning (ML) and logic deduction by embedding ML classifiers in rules as predicates. In a unified process, it identifies tuples that refer to the ...

Entity resolution, which involves identifying and merging records that refer to the same real-world entity, is a crucial task in areas like Web data integration. This importance is underscored by the presence of numerous duplicated and multi-version data ...

Record linkage is the process of identifying and matching records from different datasets that refer to the same entity. This process can be framed as a pairwise binary classification problem, where a classification model predicts if a pair of ...${}_{\mathrm{}}$

The application of recommendation technologies has been crucial in the promotion of physical and digital content across numerous global platforms such as Amazon, Apple, and Netflix. Our study aims to investigate the advantages of employing recommendation ...

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 ...

Entity matching, a core data integration problem, is the task of deciding whether two data tuples refer to the same real-world entity. Recent advances in deep learning methods, using pre-trained language models, were proposed for resolving entity ...

Product Entity Matching (PEM)--a subfield of record linkage that focuses on linking records that refer to the same product--is a challenging task for many entity matching models. For example, recent transformer models report a near-perfect performance ...

IoT Device Management (DM) refers to the remote administration of customer devices. In practice, DM is ensured by multiple actors such as operators or device manufacturers, each operating independently via their DM solution. These siloed DM solutions ...

Entity resolution (ER) finds records that refer to the same entities in the real world. Blocking is an important task in ER, filtering out unnecessary comparisons and speeding up ER. Blocking is usually an unsupervised task. In this paper, we ...

Name disambiguation---a fundamental problem in online academic systems--is now facing greater challenges with the increasing growth of research papers. For example, on AMiner, an online academic search platform, about 10% of names own more than 100 ...

A comprehensive patient health history is essential for patient care and healthcare research. However, due to the distributed nature of healthcare services, patient health records are often scattered across multiple systems. Existing record linkage ...

Entity Resolution (ER) is a fundamental problem in data preparation. Standard deep ER methods have achieved state-of-the-art effectiveness, assuming that relations from different organizations are centrally stored. However, due to privacy concerns, it ...

In this paper we introduce SimTDE, a simple knowledge distillation framework to compress sentence embeddings transformer models with minimal performance loss and significant size and latency reduction. SimTDE effectively distills large and small ...

We present part of Huawei's efforts in building a Product Knowledge Graph (PKG). We want to identify which product attributes (i.e. properties) are relevant and important in terms of shopping decisions to product categories (i.e. classes). This is ...

This paper proposes an approach for entity resolution (ER) and conflict resolution (CR) in large-scale graphs. It is based on a class of Graph Cleaning Rules (GCRs), which support the primitives of relational data cleaning rules, and may embed machine ...

Entity resolution, a longstanding problem of data cleaning and integration, aims at identifying data records that represent the same real-world entity. Existing approaches treat entity resolution as a universal task, assuming the existence of a single ...

In this paper, we summarize the results obtained while evaluating 44 similarity measures for text values, which represent real institutional customers data. These data come from a project conducted for a large financial institution in Poland. The ...

IoT Device Management (DM) refers to registering, configuring, monitoring, and updating IoT devices. DM is facing new challenges as dependencies between IoT devices generate various threats, such as update breaks and cascading failures. Dependencies-...