Data fusion is a hot topic in data integration which at least includes the two stages: entity resolution and data conflict resolution. However, the existing fusion process is transparent and the fusion stages are isolated. So in this paper, we proposed a traceable data fusion mechanism based on data provenance which can trace the data sources of fusion results and the evolutionary process. The ...

Consider two data providers, each maintaining records of different feature sets about common entities. They aim to learn a linear model over the whole set of features. This problem of federated learning over vertically partitioned data includes a crucial upstream issue: entity resolution, i.e. finding the correspondence between the rows of the datasets. It is well known that entity resolution, ...

Entity resolution, the problem of identifying the underlying entity of references found in data, has been researched for many decades in many communities. A common theme in this research has been the importance of incorporating relational features into the resolution process. Relational entity resolution is particularly important in knowledge graphs (KGs), which have a regular structure capturi...

Entity resolution has received considerable attention in recent years. Given many references to underlying entities, the goal is to predict which references correspond to the same entity. We show how to extend the Latent Dirichlet Allocation model for this task and propose a probabilistic model for collective entity resolution for relational domains where references are connected to each other....

We present FEVER, a new evaluation platform for entity resolution approaches. The modular structure of the FEVER framework supports the incorporation or reconstruction of many previously proposed approaches for entity resolution. A distinctive feature of FEVER is that it not only evaluates traditional measures such as precision and recall but also the effort for configuring (e.g., parameter tun...

The traditional mention-pair model for coreference resolution cannot capture information beyond mention pairs for both learning and testing. To deal with this problem, we present an expressive entity-mention model that performs coreference resolution at an entity level. The model adopts the Inductive Logic Programming (ILP) algorithm, which provides a relational way to organize different knowle...

An important aspect of maintaining information quality in data repositories is determining which sets of records refer to the same real world entity. This so called entity resolution problem comes up frequently for data cleaning and integration. In many domains, the underlying entities exhibit strong ties between themselves. Friendships in social networks and collaborations between researchers ...

This software demonstration paper presents Identifying Chemical Entities (ICE), a platform composed by algorithms for chemical entity recognition, entity resolution to a reference database, namely ChEBI, and validation using chemical semantic similarity. It aims to provide the users with an improved display of entity recognition results, exposing outliers which are possible recognition errors a...