International Business Machines Corporation

Kun Qian

Yunyao Li

Lucian Popa

Min Li

Prithviraj Sen

Sairam Gurajada

Nikita Bhutani

Mauricio Antonio Hernandez-Sherrington

Sheshera Mysore

Jungo Kasai

A method, system, and computer program product for learning entity resolution rules for determining whether entities are matching. The method may include receiving historical pairs of entities. The method may also include determining a set of rules for determining whether a pair of entities are matching, where the set of rules comprises a plurality of conditions. The method may also include developing, using a deep neural network, an entity resolution model based on the historical pairs of entities. The method may also include receiving a new pair of entities. The method may also include applying the entity resolution model to the new pair of entities. The method may also include determining whether one or more rules from the set of rules are satisfied for the new pair of entities. The method may also include categorizing the new pair of entities as matching or not matching.

Deep learning of entity resolution rules

Methods, systems, and computer program products for low-resource entity resolution with transfer learning are provided herein. A computer-implemented method includes processing input data via a first entity resolution model, wherein the input data comprise labeled input data and unlabeled input data; identifying one or more portions of the unlabeled input data to be used in training a neural network entity resolution model, wherein said identifying comprises applying one or more active learning algorithms to the first entity resolution model; training, using (i) the one or more portions of the unlabeled input data and (ii) one or more deep learning techniques, the neural network entity resolution model; and performing one or more entity resolution tasks by applying the trained neural network entity resolution model to one or more datasets.

Low-resource entity resolution with transfer learning

A computer-implemented method according to one embodiment includes receiving a question in a natural language format; deriving a plurality of subqueries from the question; determining that a first subquery of the plurality of subqueries cannot be resolved utilizing a curated knowledge base; resolving the first subquery, utilizing unstructured text; and adjusting the curated knowledge base, utilizing the resolved first subquery.

Resolving queries using structured and unstructured data

Methods, systems, and computer program products for learning models for entity resolution using active learning are provided herein. A computer-implemented method includes determining a set of data items related to a task associated with structured knowledge base creation, and outputting the set of data items to a user for labeling. Such a method also includes generating, based on a user-labeled version of the set of data items, a candidate model for executing the task, and one or more generalized versions of the candidate model. Additionally, such a method can also include generating a final model based on one or more iterations of analysis of the candidate model and analysis of the one or more generalized versions of the candidate model, and performing the task by executing the final model on one or more datasets.

Learning models for entity resolution using active learning

Methods, systems, and computer program products for entity structured representation and variant generation are provided herein. A computer-implemented method includes automatically parsing instances of a given entity type into semantic components by implementing a parser based at least in part on (i) the given entity type and (ii) items of information relevant to the given entity type; generating, based at least in part on (i) the semantic components and (ii) information pertaining to one or more valid component-specific variants, one or more variants of the semantic components; creating, based at least in part on the one or more variants of the one or more semantic components, one or more variants of at least one instance of an entity associated with the given entity type; and outputting, to at least one user, the one or more variants of the at least one instance of the entity.

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Kun Qian