Sap Se

Matthias Frank

Rajesh Vellore Arumugam

Stefan Klaus Baur

Hoang-Vu Nguyen

Johannes Hoehne

Anoop Raveendra Katti

Shachar Klaiman

Marius Lehne

Sundeep Gullapudi

Wei Xia

Lennart Van Der Goten

Sebastian Brarda

Methods, systems, and computer-readable storage media for a ML system that reduces a number of target items from consideration as potential matches to a query item using token embeddings and a search tree.

Entity linking and filtering using efficient search tree and machine learning representations

Methods, systems, and computer-readable storage media for receiving a set of inference results generated by a ML model, the inference results including a set of query entities and a set of target entities, each query entity having one or more target entities matched thereto by the ML model, processing the set of inference results to generate a set of matched sub-sets of target entities by executing a search over target entities in the set of target entities based on constraints, for each problem in a set of problems, providing the problem as a tuple including an index value representative of a target entity in the set of target entities and a value associated with the query entity, the value including a constraint relative to the query entity, and executing at least one task in response to one or more matched sub-sets in the set of matched sub-sets.

Efficient search for combinations of matching entities given constraints

Pairwise entity matching systems and methods are disclosed herein. A deep learning model may be used to match entities from separate data tables. Entities may be preprocessed to fuse textual and numeric data early in the neural network architecture. Numeric data may be represented as a vector of a geometrically progressing function. By fusing textual and numeric data, including dates, early in the neural network architecture the neural network may better learn the relationships between the numeric and textual data. Once preprocessed, the paired entities may be scored and matched using a neural network.

Numeric embeddings for entity-matching

Aspects of the present disclosure therefore involve systems and methods for identifying a set of visually similar scenes to a target scene selected or otherwise identified by a match analyst. A scene retrieval platform performs operations for: receiving an input that comprises an identification of a scene; retrieving a set of coordinates based on the scene identified by the input, where the set of coordinates identify positions of the entities depicted within the frames; generating a set of vector values based on the coordinates of the entities depicted within each of the frames; concatenating the set of vector values to generate a concatenated vector value that represents the scene; generating a visual representation of the concatenated vector value; and identifying one or more similar scenes to the scene identified by the input based on the visual representation of the concatenated vector value.

Visually similar scene retrieval using coordinate data

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Heidelberg, Germany

Matthias Frank