Microsoft Technology Licensing, LLC

Juan Pablo Bottaro

Majid Yazdani

Daria Bogdanova

Maria Laura Jedrzejowska

In an embodiment, the disclosed technologies include identifying a content item of a first digital data source as a candidate for linking with a target entity of a second digital data source by matching a candidate entity mentioned in the content item to the target entity in accordance with semantic similarity data computed between the candidate entity and the target entity; inputting at least one feature of the content item and at least one feature of the target entity to a set of digital models that analyze the at least one feature of the content item and the at least one feature of the target entity and determine and output qualitative data; based on the qualitative data, determining link risk data; based on the link risk data and the semantic similarity data, and determining whether to generate a link between the content item and the target entity.

Risk-aware entity linking

In an example, for each of one or more terms in a text document, one or more entities to which the term potentially maps are identified. The text document includes at least one ambiguous term. One or more features are extracted from the text document. An attention model is applied to the text document based on the extracted one or more features, resulting in an attention weight being applied to each of the one or more terms in the text document. The one or more terms are encoded based on the attention weights. Each of one or more ambiguous terms is classified based on the encoded terms, the classification assigning a value to each different entity that each ambiguous term potentially maps to. A minimum entropy loss function is evaluated using the classification, and results are back-propagated to the attention model.

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Juan Pablo Bottaro