Entity Resolution (ER) is the process of identifying groups of records that refer to the same real-world entity. Various measures (e.g., pairwise F1, cluster F1) have been used for evaluating ER results. However, ER measures tend to be chosen in an ad-hoc fashion without careful thought as to what defines a good result for the specific application at hand. In this paper, our contributions are t...

Resolving the ambiguity of person, organisation and location names is a challenging problem in the Natural Language Processing (NLP) area. This problem is usually formulated as a clustering problem, in which the target is to group mentions of the same entity into the same cluster. In this paper, we present a different approach based on the Distributional Hypothesis and edit distance, which asso...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

Entity resolution (ER) is a key data integration problem. Despite the efforts in 70+ years in all aspects of ER, there is still a high demand for democratizing ER – humans are heavily involved in labeling data, performing feature engineering, tuning parameters, and defining blocking functions. With the recent advances in deep learning, in particular distributed representation of words (a.k.a. w...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...